emnlp emnlp2010 emnlp2010-77 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Georgiana Dinu ; Mirella Lapata
Abstract: The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.
Reference: text
sentIndex sentText sentNum sentScore
1 Abstract The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. [sent-3, score-0.427]
2 Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. [sent-4, score-0.209]
3 In his paper we propose a probabilistic framework for measuring similarity in context. [sent-5, score-0.211]
4 Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. [sent-6, score-0.433]
5 Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models. [sent-7, score-0.23]
6 1 Introduction The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks within natural language processing (NLP). [sent-8, score-0.312]
7 The advantage of taking such a geometric approach is that the similarity of word meanings can be easily quantified by measuring their distance in the vector space, or the cosine of the angle between them. [sent-16, score-0.349]
8 Vector-based models do not explicitly identify the different senses of words and consequently represent their meaning invariably (i. [sent-17, score-0.32]
9 Recent work addresses this issue indirectly with the development of specialized models that represent word meaning in context (Mitchell and Lapata, 2008; Erk and Pad o´, 2008; Thater et al. [sent-28, score-0.231]
10 These methods first extract typical co-occurrence vectors representing a mixture of senses and then use vector operations to either obtain contextualized representations of a target word (Erk and Pad o´, 2008) or a representation for a set of words (Mitchell and Lapata, 2009). [sent-30, score-0.709]
11 In this paper we propose a probabilistic framework for representing word meaning and measuring similarity in context. [sent-31, score-0.325]
12 We model the meaning of isolated words as a probability distribution over a set of latent senses. [sent-32, score-0.253]
13 c od2s01 in0 N Aastsuorcaialt Lioan g foura Cgeom Prpoucteastisoin ga,l p Laignegsui 1s1ti6c2s–1 72, vector construction process, contextualized meaning can be modeled naturally as a change in the original sense distribution. [sent-36, score-0.537]
14 In the remainder of this paper we give a brief overview of related work, emphasizing vector-based approaches that compute word meaning in context (Section 2). [sent-39, score-0.199]
15 They show that models performing point-wise multiplication of component vectors outperform earlier proposals based on vector addition (Landauer and Dumais, 1997; Kintsch, 2001). [sent-46, score-0.194]
16 For example, the meaning of a verb in the presence of its object is modeled as the multiplication of the verb’s vector with the vector capturing the inverse selectional preferences of the object; the latter are computed as the centroid of the verbs that occur with this object. [sent-50, score-0.347]
17 The meaning of a verb boils down to restricting its vector to the features active in the argument noun (i. [sent-53, score-0.187]
18 These cluster vectors can be used to determine the semantic similarity of both isolated words and words in context. [sent-59, score-0.269]
19 The meaning of a word in context is the set of exemplars most similar to it. [sent-63, score-0.199]
20 Unlike Reisinger and Mooney (2010) and Erk and Pad o´ (2010) our model is probabilistic (we represent word meaning as a distribution over a set of latent senses), which makes it easy to integrate and combine with other systems via mixture or product models. [sent-64, score-0.327]
21 3 Meaning Representation in Context In this section we first describe how we represent the meaning of individual words and then move on to discuss our model of inducing meaning representations in context. [sent-68, score-0.322]
22 Observed Representations Most vector space models in the literature perform computations on a co-occurrence matrix where each row represents a target word, each column a document or another neighboring word, and each entry their co-occurrence frequency. [sent-69, score-0.455]
23 Under this representation, the similarity of word meanings can be easily quantified by measuring their distance in the vector space, the cosine of the angle between them, or their scalar product. [sent-71, score-0.468]
24 Throughout this paper we will use the notation ti with i : 1. [sent-75, score-0.197]
25 A cell (i, j) in the matrix represents the frequency of occurrence of target ti with context feature cj over a corpus. [sent-80, score-0.754]
26 Meaning Representation over Latent Senses We further assume that the target words ti i : 1. [sent-81, score-0.284]
27 I found in a corpus share a global set of meanings or senses Z = {zk |k : 1. [sent-84, score-0.228]
28 And therefore the meaning so Zf in =div {idzu|akl target Kwo}. [sent-88, score-0.201]
29 More formally, a target ti is represented by the following vector: v(ti) = (P(z1 |ti) , . [sent-90, score-0.284]
30 , P(zK |ti)) (1) where component P(z1 |ti) is the probability of sense z1 given target wo|rdt ti, component P(z2 |ti) the probability of sense z2 given ti and so on. [sent-93, score-0.446]
31 Analogously, we can represent the meaning of a target word given a context feature as: v(ti, cj) = (P(z1 |ti, cj ) , . [sent-102, score-0.5]
32 , P(zK |ti, cj)) (2) Here, target ti is again represented as a distribution × over senses, but is now modulated by a specific context cj which reflects actual word usage. [sent-105, score-0.634]
33 This distribution is more “focused” compared to (1); the context helps disambiguate the meaning of the target word, and as a result fewer senses will share most of the probability mass. [sent-106, score-0.46]
34 We will therefKore × Ima Jk-ed mthee simplifying assumption twhiallt target words ti and context features cj are conditionally independent given sense zk: P(zk|ti,cj) ≈PPk(Pzk(z|tki|)tPi)(Pcj(|czjk|z)k) (4) Although not true in general, the assumption is relatively weak. [sent-108, score-0.664]
35 A variety of latent variable models can be used to obtain senses z1 . [sent-110, score-0.3]
36 Note that we abuse terminology here, as the senses our models obtain are not lexicographic meaning distinctions. [sent-114, score-0.32]
37 Rather, they denote coarsegrained senses or more generally topics attested in the document collections our model is trained on. [sent-115, score-0.261]
38 4 Parametrizations The general framework outlined above can be parametrized with respect to the input co-occurrence matrix and the algorithm employed for inducing the latent structure. [sent-119, score-0.301]
39 Examples include Probabilistic Latent Semantic Analysis (PLSA, Hofmann (2001)), Probabilistic Principal Components Analysis (Tipping and Bishop, 1999), non-negative matrix factorization (NMF, Lee and Seung (2000)), and latent Dirichlet allocation (LDA, Blei et al. [sent-126, score-0.395]
40 Non-negative Matrix Factorization Nonnegative matrix factorization algorithms approx- imate a non-negative input matrix V by two non-negative factors W and H, under a given loss function. [sent-129, score-0.439]
41 W and H are reduced-dimensional matrices and their product can be regarded as a compressed form of the data in V : VI,J ≈ WI,KHK,J (5) where W is a basis vector matrix and H is an encoded matrix of the basis vectors in equation (5). [sent-130, score-0.488]
42 Le WH Pdenote the fac|ttors in a NMF decomposition of an input matrix V and B be a diagonal matrix with Bkk = Pj Hkj. [sent-136, score-0.342]
43 Similarly, given matrix WB, we can define a diagonal matrix A, with Aii = Pk (WB)ik. [sent-138, score-0.342]
44 The probability of a word token w taking on value igiven that topic z = j is parametrized using a matrix β with bij = P(w = i|z = j). [sent-148, score-0.207]
45 We use LDA to induce senses of target words based on context words, and therefore each row ti in the input matrix transforms into a document. [sent-162, score-0.714]
46 The frequency of ti occurring with context feature cj is the number of times word cj is encountered in the “document” associated with ti. [sent-163, score-0.71]
47 gives the sense distributions of each target ti: θik = P(zk |ti) and φ the context-word distribution for each sense zk: φkj = P(cj |zk). [sent-165, score-0.249]
48 We experimented with two types of semantic space based on NMF and LDA and optimized parameters for these models on a word similarity task. [sent-169, score-0.303]
49 The latter involves judging the similarity sim(ti, ti0) = sim(v(ti) , v(ti0)) of words ti and ti0 out of context, where v(ti) and v(ti0) are obtained from the output of NMF or LDA, respectively. [sent-170, score-0.329]
50 The contextualized representations were next evaluated on lexical substitution (McCarthy and Navigli, 2007). [sent-174, score-0.397]
51 The task requires systems to find appropriate substitutes for target words occurring in context. [sent-175, score-0.192]
52 Five human annotators were asked to provide substitutes for these target words. [sent-180, score-0.192]
53 Following Erk and Pad o´ (2008), we pool together the total set of substitutes for each target word. [sent-182, score-0.192]
54 We rank the candidate substitutes based on the similarity of the contextualized target and the out-of-context substitute, sim(v(ti, cj) , v(ti0)), where ti is the target word, cj a context word and ti0 a substitute. [sent-184, score-1.175]
55 isons with the target and its substitute embedded in an identical context (see also Thater et al. [sent-187, score-0.221]
56 Rows in this matrix are target words and columns are their co-occurring neighbors, within a symmetric window of size 5. [sent-192, score-0.258]
57 We used Gibbs sampling on the “document collection” obtained from the input matrix and estimated the sense distributions as described in Section 4. [sent-205, score-0.252]
58 Among the above similarity measures, the scalar product has the most straightforward interpretation as the probability of two targets sharing a common meaning (i. [sent-211, score-0.406]
59 The scalar product assigns 1 to a pair of identical vectors if and only if P(zi) = 1 for some i and P(zj) = 0, ∀j i. [sent-214, score-0.192]
60 Given a set of context words, we contextualize the target using one context word at a time and compute the overall similarity score by multiplying the individual scores. [sent-217, score-0.474]
61 = Baselines Our baseline models for measuring similarity out of context are Latent Semantic Analysis (Landauer and Dumais, 1997) and a simple semantic space without any dimensionality reduction. [sent-218, score-0.439]
62 For LSA, we computed the UΣV SVD decomposition of the original matrix to rank k = 1000. [sent-219, score-0.2]
63 Similarity computations were performed in the lower rank approximation matrix UΣV , as originally proposed in Deerwester et al. [sent-222, score-0.243]
64 Our second baseline, the simple semantic space, was based on the original input matrix on which we applied several weighting schemes such as pointwise mutual information, tf-idf, and line normalization. [sent-226, score-0.28]
65 Again, we measured similarity using cosine, scalar product and inverse JS divergence. [sent-227, score-0.292]
66 Our baselines for contextualized similarity were vector addition and vector multiplication which we performed using the simple semantic space (Mitchell and Lapata, 2008) and dimensionality reduced representations obtained from NMF and LDA. [sent-229, score-0.779]
67 To create a ranking of the candidate substitutes we compose the vector of the target with its context and compare it with each substitute vector. [sent-230, score-0.43]
68 Given a set of context words, we contextualize the target using each context word at a time and multiply the individual scores. [sent-231, score-0.342]
69 Evaluation Method For the word similarity task we used correlation analysis to examine the relationship between the human ratings and their corresponding vector-based similarity values. [sent-232, score-0.264]
70 We report Spearman’s ρ correlations between the similarity values provided by the models and the mean participant similarity ratings in the Finkelstein et al. [sent-233, score-0.296]
71 For the lexical substitution task, we compare the system ranking with the gold standard ranking using Kendall’s τb rank correlation (which is adjusted for tied ranks). [sent-235, score-0.221]
72 For all contextualized models we defined the context of a target word as the words occurring within a symmetric context window of size 5. [sent-236, score-0.528]
73 The simple cooccurrence based vector space (SVS) performed best with tf-idf weighting and the cosine similarity measure. [sent-247, score-0.348]
74 With regard to LSA, we obtained best results with initial line normalization of the matrix, K = 600 dimensions, and the scalar product similarity measure while performing computations in matrix U. [sent-248, score-0.506]
75 NMF yields best performance with K = 1000 dimensions and the scalar product similarity measure. [sent-250, score-0.324]
76 As a comparison, a baseline configuration with tf-idf weighting and the cosine similarity measure yields a correlation of 0. [sent-258, score-0.226]
77 This model returns the same ranking of the substitute candidates for each instance, based solely on their similarity with the target word. [sent-268, score-0.299]
78 We report results with contextualized NMF and LDA as individual models (the best word similarity settings) and as mixtures (as described above). [sent-271, score-0.437]
79 We implemented an additive model with pmi weighting and Lin’s similarity measure which is defined in an additive fashion. [sent-273, score-0.263]
80 The multiplicative model uses tfidf weighting and cosine similarity, which involves multiplication of vector components. [sent-274, score-0.277]
81 Other combinations of weighting schemes and similarity measures delivered significantly lower results. [sent-275, score-0.181]
82 6178 Table 4: Results on lexical substitution for different parts of speech with a simple semantic space model (SVS), two compositional models (Add-SVS, Mult-SVS), and contextualized mixture models with NMF and LDA (ContNMFMIX, Cont-LDAMIX), using Kendall’s τb correlation coefficient. [sent-289, score-0.61]
83 01) outperform the context agnostic simple semantic space (see SVS in Table 3). [sent-291, score-0.194]
84 We also find that the multiplicative model using a simple semantic space (Mult-SVS) is the best performing compositional model, thus corroborating the results of Mitchell and Lapata (2009). [sent-294, score-0.218]
85 This indicates that the better results we obtain are due to the probabilistic formulation of our contextualized model as a whole rather than the use of NMF or LDA. [sent-296, score-0.273]
86 While verbs and nouns seem to be most difficult, we observe higher gains from the use of contextualized models. [sent-306, score-0.239]
87 148)rcmoaurs,dwitcu,rsafWokn,ibgcourpdhes,iotDgcphrkialws,etnbwr,yaindbruvoi,tedoarce,nhvsberh,idpcsgltaery Table 5: Induced senses ofjam and five most likely words given these senses using an LDA model; sense probabili- ties are shown in parentheses. [sent-309, score-0.429]
88 Finally, we also qualitatively examined how the context words influence the sense distributions of target words using examples from the lexical substitution dataset and the output of an individual Cont-LDA model. [sent-312, score-0.385]
89 In the context of client, bug remains ambiguous between the senses SECRET-AGENCY (0. [sent-329, score-0.293]
90 There are also cases where the contextualized distributions are not correct, especially when senses are domain specific. [sent-332, score-0.413]
91 However, the senses that are triggered by this pair all relate to the “service” sense of function. [sent-334, score-0.255]
92 We also see several cases where the target word and one of the context words are assigned senses that are locally correct, but invalid in the larger context. [sent-336, score-0.346]
93 However, the sentential context ascribes a meaning that is neither related to injury nor sports. [sent-341, score-0.25]
94 Key in this framework is the representation of word meaning as a distribution over a set of global senses where contextualized meaning is modeled as a change in this distribution. [sent-351, score-0.671]
95 The approach is conceptually simple, the same vector representation is used for isolated words and words in context without being tied to a specific sense induction method or type of semantic space. [sent-352, score-0.376]
96 We have illustrated two instantiations of this framework using non-negative matrix factorization and latent Dirichlet allocation for inducing the latent structure, and shown experimentally that they outperform previously proposed methods for measuring similarity in context. [sent-353, score-0.666]
97 However, in practice, we used vector representations that do not distinguish the two: target words and contextual features are both words. [sent-359, score-0.22]
98 However, differentiating target from context representations may be beneficial particularly when the similarity computations are embedded within specific tasks such as the acquisition of paraphrases, the recognition of entailment relations, and thesaurus construction. [sent-361, score-0.407]
99 On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing. [sent-385, score-0.456]
100 A structured vector space model for word meaning in context. [sent-389, score-0.236]
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1 0.98148245 52 emnlp-2010-Further Meta-Evaluation of Broad-Coverage Surface Realization
Author: Dominic Espinosa ; Rajakrishnan Rajkumar ; Michael White ; Shoshana Berleant
Abstract: We present the first evaluation of the utility of automatic evaluation metrics on surface realizations of Penn Treebank data. Using outputs of the OpenCCG and XLE realizers, along with ranked WordNet synonym substitutions, we collected a corpus of generated surface realizations. These outputs were then rated and post-edited by human annotators. We evaluated the realizations using seven automatic metrics, and analyzed correlations obtained between the human judgments and the automatic scores. In contrast to previous NLG meta-evaluations, we find that several of the metrics correlate moderately well with human judgments of both adequacy and fluency, with the TER family performing best overall. We also find that all of the metrics correctly predict more than half of the significant systemlevel differences, though none are correct in all cases. We conclude with a discussion ofthe implications for the utility of such metrics in evaluating generation in the presence of variation. A further result of our research is a corpus of post-edited realizations, which will be made available to the research community. 1 Introduction and Background In building surface-realization systems for natural language generation, there is a need for reliable automated metrics to evaluate the output. Unlike in parsing, where there is usually a single goldstandard parse for a sentence, in surface realization there are usually many grammatically-acceptable ways to express the same concept. This parallels the task of evaluating machine-translation (MT) systems: for a given segment in the source language, 564 there are usually several acceptable translations into the target language. As human evaluation of translation quality is time-consuming and expensive, a number of automated metrics have been developed to evaluate the quality of MT outputs. In this study, we investigate whether the metrics developed for MT evaluation tasks can be used to reliably evaluate the outputs of surface realizers, and which of these metrics are best suited to this task. A number of surface realizers have been developed using the Penn Treebank (PTB), and BLEU scores are often reported in the evaluations of these systems. But how useful is BLEU in this context? The original BLEU study (Papineni et al., 2001) scored MT outputs, which are of generally lower quality than grammar-based surface realizations. Furthermore, even for MT systems, the usefulness of BLEU has been called into question (Callison-Burch et al., 2006). BLEU is designed to work with multiple reference sentences, but in treebank realization, there is only a single reference sentence available for comparison. A few other studies have investigated the use of such metrics in evaluating the output of NLG systems, notably (Reiter and Belz, 2009) and (Stent et al., 2005). The former examined the performance of BLEU and ROUGE with computer-generated weather reports, finding a moderate correlation with human fluency judgments. The latter study applied several MT metrics to paraphrase data from Barzilay and Lee’s corpus-based system (Barzilay and Lee, 2003), and found moderate correlations with human adequacy judgments, but little correlation with fluency judgments. Cahill (2009) examined the performance of six MT metrics (including BLEU) in evaluating the output of a LFG-based surface realizer for ProceMedITin,g Ms oasfs thaceh 2u0se1t0ts C,o UnSfAer,e n9c-e1 on O Ectmobpeir ic 2a0l1 M0.e ?tc ho2d0s10 in A Nsastoucira tlio Lnan fogru Cagoem Ppruotcaetisosninagl, L pinag eusis 5t6ic4s–574, German, also finding only weak correlations with the human judgments. To study the usefulness of evaluation metrics such as BLEU on the output of grammar-based surface realizers used with the PTB, we assembled a corpus of surface realizations from three different realizers operating on Section 00 of the PTB. Two human judges evaluated the adequacy and fluency of each of the realizations with respect to the reference sentence. The realizations were then scored with a number of automated evaluation metrics developed for machine translation. In order to investigate the correlation of targeted metrics with human evaluations, and gather other acceptable realizations for future evaluations, the judges manually repaired each unacceptable realization during the rating task. In contrast to previous NLG meta-evaluations, we found that several of the metrics correlate moderately well with human judgments of both adequacy and fluency, with the TER family performing best. However, when looking at statistically significant system-level differences in human judgments, we found that some of the metrics get some of the rankings correct, but none get them all correct, with different metrics making different ranking errors. This suggests that multiple metrics should be routinely consulted when comparing realizer systems. Overall, our methodology is similar to that of previous MT meta-evaluations, in that we collected human judgments of system outputs, and compared these scores with those assigned by automatic metrics. A recent alternative approach to paraphrase evaluation is ParaMetric (Callison-Burch et al., 2008); however, it requires a corpus of annotated (aligned) paraphrases (which does not yet exist for PTB data), and is arguably focused more on paraphrase analysis than paraphrase generation. The plan of the paper is as follows: Section 2 discusses the preparation of the corpus of surface realizations. Section 3 describes the human evaluation task and the automated metrics applied. Sections 4 and 5 present and discuss the results of these evaluations. We conclude with some general observations about automatic evaluation of surface realizers, and some directions for further research. 565 2 Data Preparation We collected realizations of the sentences in Section 00 of the WSJ corpus from the following three sources: 1. OpenCCG, a CCG-based chart realizer (White, 2006) 2. The XLE Generator, a LFG-based system developed by Xerox PARC (Crouch et al., 2008) 3. WordNet synonym substitutions, to investigate how differences in lexical choice compare to grammar-based variation.1 Although all three systems used Section 00 of the PTB, they were applied with various parameters (e.g., language models, multiple-output versus single-output) and on different input structures. Accordingly, our study does not compare OpenCCG to XLE, or either of these to the WordNet system. 2.1 OpenCCG realizations OpenCCG is an open source parsing/realization library with multimodal extensions to CCG (Baldridge, 2002). The OpenCCG chart realizer takes logical forms as input and produces strings by combining signs for lexical items. Alternative realizations are scored using integrated n-gram and perceptron models. For robustness, fragments are greedily assembled when necessary. Realizations were generated from 1,895 gold standard logical forms, created by constrained parsing of development-section derivations. The following OpenCCG models (which differ essentially in the way the output is ranked) were used: 1. Baseline 1: Output ranked by a trigram word model 2. Baseline 2: Output ranked using three language models (3-gram words 3-gram words with named entity class replacement factored language model of words, POS tags and CCG supertags) + + 1Not strictly surface realizations, since they do not involve an abstract input specification, but for simplicity we refer to them as realizations throughout. 3. Baseline 3: Perceptron with syntax features and the three LMs mentioned above 4. Perceptron full-model: n-best realizations ranked using perceptron with syntax features and the three n-gram models, as well as discriminative n-grams The perceptron model was trained on sections 0221 of the CCGbank, while a grammar extracted from section 00-21 was used for realization. In addition, oracle supertags were inserted into the chart during realization. The purpose of such a non-blind testing strategy was to evaluate the quality of the output produced by the statistical ranking models in isolation, rather than focusing on grammar coverage, and avoid the problems associated with lexical smoothing, i.e. lexical categories in the development section not being present in the training section. To enrich the variation in the generated realizations, dative-alternation was enforced during realization by ensuring alternate lexical categories of the verb in question, as in the following example: (1) the executives gave [the chefs] [a standing ovation] (2) the executives gave [a standing ovation] [to the chefs] 2.2 XLE realizations The corpus of realizations generated by the XLE system contained 42,527 surface realizations of approximately 1,421 section 00 sentences (an average of 30 per sentence), initially unranked. The LFG f-structures used as input to the XLE generator were derived from automatic parses, as described in (Riezler et al., 2002). The realizations were first tokenized using Penn Treebank conventions, then ranked using perplexities calculated from the same trigram word model used with OpenCCG. For each sentence, the top 4 realizations were selected. The XLE generator provides an interesting point of comparison to OpenCCG as it uses a manuallydeveloped grammar with inputs that are less abstract but potentially noisier, as they are derived from automatic parses rather than gold-standard ones. 566 2.3 WordNet synonymizer To produce an additional source of variation, the nouns and verbs of the sentences in section 00 of the PTB were replaced with all of their WordNet synonyms. Verb forms were generated using verb stems, part-of-speech tags, and the morphg tool.2 These substituted outputs were then filtered using the n-gram data which Google Inc. has made available.3 Those without any 5-gram matches centered on the substituted word (or 3-gram matches, in the case of short sentences) were eliminated. 3 Evaluation From the data sources described in the previous sec- tion, a corpus of realizations to be evaluated by the human judges was constructed by randomly choosing 305 sentences from section 00, then selecting surface realizations of these sentences using the following algorithm: 1. Add OpenCCG’s best-scored realization. 2. Add other OpenCCG realizations until all four models are represented, to a maximum of 4. 3. Add up to 4 realizations from either the XLE system or the WordNet pool, chosen randomly. The intent was to give reasonable coverage of all realizer systems discussed in Section 2 without overloading the human judges. “System” here means any instantiation that emits surface realizations, including various configurations of OpenCCG (using different language models or ranking systems), and these can be multiple-output, such as an n-best list, or single-output (best-only, worst-only, etc.). Accordingly, more realizations were selected from the OpenCCG realizer because 5 different systems were being represented. Realizations were chosen randomly, rather than according to sentence types or other criteria, in order to produce a representative sample of the corpus. In total, 2,114 realizations were selected for evaluation. 2http : //www. informatics . sussex. ac .uk/ re search/ groups / nlp / carro l /morph .html l 3http : //www . ldc . upenn .edu/Catalog/docs/ LDC2 0 0 6T 13 / readme .txt 3.1 Human judgments Two human judges evaluated each surface realization on two criteria: adequacy, which represents the extent to which the output conveys all and only the meaning of the reference sentence; and fluency, the extent to which it is grammatically acceptable. The realizations were presented to the judges in sets containing a reference sentence and the 1-8 outputs selected for that sentence. To aid in the evaluation of adequacy, one sentence each of leading and trailing context were displayed. Judges used the guidelines given in Figure 1, based on the scales developed by the NIST Machine Translation Evaluation Workshop. In addition to rating each realization on the two five-point scales, each judge also repaired each output which he or she did not judge to be fully adequate and fluent. An example is shown in Figure 2. These repairs resulted in new reference sentences for a substantial number of sentences. These repaired realizations were later used to calculate targeted versions of the evaluation metrics, i.e., using the repaired sentence as the reference sentence. Although targeted metrics are not fully automatic, they are of interest because they allow the evaluation algorithm to focus on what is actually wrong with the input, rather than all textual differences. Notably, targeted TER (HTER) has been shown to be more consistent with human judgments than human annotators are with one another (Snover et al., 2006). 3.2 Automatic evaluation The realizations were also evaluated using seven automatic metrics: • IBM’s BLEU, which scores a hypothesis by counting n-gram matches with the reference sentence (Papineni et al., 2001), with smoothing as described in (Lin and Och, 2004) • • • • • • The NIST n-gram evaluation metric, similar to BLEU, but rewarding rarer n-gram matches, and using a different length penalty METEOR, which measures the harmonic mean of unigram precision and recall, with a higher weight for recall (Banerjee and Lavie, 2005) 567 TER (Translation Edit Rate), a measure of the number of edits required to transform a hypothesis sentence into the reference sentence (Snover et al., 2006) TERP, an augmented version of TER which performs phrasal substitutions, stemming, and checks for synonyms, among other improvements (Snover et al., 2009) TERPA, an instantiation of TERP with edit weights optimized for correlation with adequacy in MT evaluations GTM (General Text Matcher), a generaliza- tion of the F-measure that rewards contiguous matching spans (Turian et al., 2003) Additionally, targeted versions of BLEU, METEOR, TER, and GTM were computed by using the human-repaired outputs as the reference set. The human repair was different from the reference sentence in 193 cases (about 9% of the total), and we expected this to result in better scores and correlations with the human judgments overall. 4 Results 4.1 Human judgments Table 1 summarizes the dataset, as well as the mean adequacy and fluency scores garnered from the human evaluation. Overall adequacy and fluency judgments were high (4.16, 3.63) for the realizer systems on average, and the best-rated realizer systems achieved mean fluency scores above 4. 4.2 Inter-annotator agreement Inter-annotator agreement was measured using the κ-coefficient, which is commonly used to measure the extent to which annotators agree in category P(1A−)P−(PE()E), judgment tasks. κ is defined as where P(A) is the observed agreement 1 b−etPw(eEe)n annotators and P(E) is the probability of agreement due to chance (Carletta, 1996). Chance agreement for this data is calculated by the method discussed in Carletta’s squib. However, in previous work in MT meta-evaluation, Callison-Burch et al. (2007), assume the less strict criterion of uniform chance agreement, i.e. for a five-point scale. They also 51 Score Adequacy Fluency 5All the meaning of the referencePerfectly grammatical 4 Most of the meaning Awkward or non-native; punctuation errors 3 Much of the meaning Agreement errors or minor syntactic problems 2 Meaning substantially different Major syntactic problems, such as missing words 1 Meaning completely different Completely ungrammatical Figure Ref. Realiz. Repair 1: Rating scale and guidelines It wasn’t clear how NL and Mr. Simmons would respond if Georgia Gulf spurns them again It weren’t clear how NL and Mr. Simmons would respond if Georgia Gulf again spurns them It wasn’t clear how NL and Mr. Simmons would respond if Georgia Gulf again spurns them Figure 2: Example of repair introduce the notion of “relative” κ, which measures how often two or more judges agreed that A > B, A = B, or A < B for two outputs A and B, irrespective of the specific values given on the five-point scale; here, uniform chance agreement is taken to be We report both absolute and relative κ in Table 2, using actual chance agreement rather than uniform chance agreement. 31. The κ scores of0.60 for adequacy and 0.63 for fluency across the entire dataset represent “substantial” agreement, according to the guidelines discussed in (Landis and Koch, 1977), better than is typically reported for machine translation evaluation tasks; for example, Callison-Burch et al. (2007) reported “fair” agreement, with κ = 0.281 for fluency and κ = 0.307 for adequacy (relative). Assuming the uniform chance agreement that the previously cited work adopts, our inter-annotator agreements (both absolute and relative) are still higher. This is likely due to the generally high quality of the realizations evaluated, leading to easier judgments. 4.3 Correlation with automatic evaluation To determine how well the automatic evaluation methods described in Section 3 correlate with the human judgments, we averaged the human judgments for adequacy and fluency, respectively, for each of the rated realizations, and then computed both Pearson’s correlation coefficient and Spearman’s rank correlation coefficient between these scores and each of the metrics. Spearman’s correlation makes fewer assumptions about the distribu- tion of the data, but may not reflect a linear rela568 tionship that is actually present. Both are frequently reported in the literature. Due to space constraints, we show only Spearman’s correlation, although the TER family scored slightly better on Pearson’s coefficient, relatively. The results for Spearman’s correlation are given in Table 3. Additionally, the average scores for adequacy and fluency were themselves averaged into a single score, following (Snover et al., 2009), and the Spearman’s correlation of each of the automatic metrics with these scores are given in Table 4. All reported correlations are significant at p < 0.001. 4.4 Bootstrap sampling of correlations For each of the sub-corpora shown in Table 1, we computed confidence intervals for the correlations between adequacy and fluency human scores with selected automatic metrics (BLEU, HBLEU, TER, TERP, and HTER) as described in (Koenh, 2004). We sampled each sub-corpus 1000 times with replace- ment, and calculated correlations between the rankings induced by the human scores and those induced by the metrics for each reference sentence. We then used these coefficients to estimate the confidence interval, after excluding the top 25 and bottom 25 coefficients, following (Lin and Och, 2004). The results of this for the BLEU metric are shown in Table 5. We determined which correlations lay within the 95% confidence interval of the best performing metric in each row of Table Table 3; these figures are italicized. 5 Discussion 5.1 Human judgments of systems The results for the four OpenCCG perceptron models mostly confirm those reported in (White and Rajkumar, 2009), with one exception: the B-3 model was below B-2, though the P-B (perceptron-best) model still scored highest. This may have been due to differences in the testing scenario. None of the differences in adequacy scores among the individual systems are significant, with the exception of the WordNet system. In this case, the lack of wordsense disambiguation for the substituted words results in a poor overall adequacy score (e.g., wage floor → wage story). Conversely, it scores highest ffoloro fluency, as substituting a noun or tve srcbo rwesith h a synonym does not usually introduce ungrammaticality. 5.2 Correlations of human judgments with MT metrics Of the non-human-targeted metrics evaluated, BLEU and TER/TERP demonstrate the highest correlations with the human judgments of fluency (r = 0.62, 0.64). The TER family of evaluation metrics have been observed to perform very well in MTevaluation tasks, and although the data evaluated here differs from typical MT data in some important ways, the correlation of TERP with the human judgments is substantial. In contrast with previous MT evaluations where TERP performs considerably better than TER, these scored close to equal on our data, possibly because TERP’s stem, synonym, and paraphrase matching are less useful when most of the variation is syntactic. The correlations with BLEU and METEOR are lower than those reported in (Callison-Burch et al., 2007); in that study, BLEU achieved adequacy and fluency correlations of 0.690 and 0.722, respectively, and METEOR achieved 0.701 and 0.719. The correlations for these metrics might be expected to be lower for our data, since overall quality is higher, making the metrics’ task more difficult as the outputs involve subtler differences between acceptable and unacceptable variation. The human-targeted metrics (represented by the prefixed H in the data tables) correlated even more strongly with the human judgments, compared to the non-targeted versions. HTER demonstrated the best 569 correlation with realizer fluency (r = 0.75). For several kinds of acceptable variation involving the rearrangement of constituents (such as dative shift), TERP gives a more reasonable score than BLEU, due to its ability to directly evaluate phrasal shifts. The following realization was rated 4.5 for fluency, and was more correctly ranked by TERP than BLEU: (3) Ref: The deal also gave Mitsui access to a high-tech medical product. (4) Realiz.: The deal also gave access to a high-tech medical product to Mitsui. For each reference sentence, we compared the ranking of its realizations induced from the human scores to the ranking induced from the TERP score, and counted the rank errors by the latter, informally categorizing them by error type (see Table 7). In the 50 sentences with the highest numbers of rank errors, 17 were affected by punctuation differences, typically involving variation in comma placement. Human fluency judgments of outputs with only punctuation problems were generally high, and many realizations with commas inserted or removed were rated fully fluent by the annotators. However, TERP penalizes such insertions or deletions. Agreement errors are another frequent source of ranking errors for TERP. The human judges tended to harshly penalize sentences with number-agreement or tense errors, whereas TERP applies only a single substitution penalty for each such error. We expect that with suitable optimization of edit weights to avoid over-penalizing punctuation shifts and underpenalizing agreement errors, TERP would exhibit an even stronger correlation with human fluency judgments. None of the evaluation metrics can distinguish an acceptable movement of a word or constituent from an unacceptable movement, with only one reference sentence. A substantial source of error for both TERP and BLEU is variation in adverbial placement, as shown in (7). Similar errors are seen with prepositional phrases and some commonly-occurring temporal adverbs, which typically admit a number of variations in placement. Another important example of acceptable variation which these metrics do not generally rank correctly is dative alternation: Ref. We need to clarify what exactly is wrong with it. Realiz. Flu. TERP BLEU We need to clarify exactly what is wrong with it.50.10.5555 We need to clarify exactly what ’s wrong with it. 5 0.2 0.4046 (7) We need to clarify what , exactly , is wrong with it. 5 0.2 0.5452 We need to clarify what is wrong with it exactly. 4.5 0.1 0.6756 We need to clarify what exactly , is wrong with it. 4 0.1 0.7017 We need to clarify what , exactly is wrong with it. 4 0.1 0.7017 We needs to clarify exactly what is wrong with it. (5) Ref. When test booklets were passed out 48 hours ahead of time, she says she copied questions in the social studies section and gave the answers to students. (6) Realiz. When test booklets were passed out 48 hours ahead of time , she says she copied questions in the social studies section and gave students the answers. The correlations of each of the metrics with the human judgments of fluency for the realizer systems indicate at least a moderate relationship, in contrast with the results reported in (Stent et al., 2005) for paraphrase data, which found an inverse correlation for fluency, and (Cahill, 2009) for the output ofa surface realizer for German, which found only a weak correlation. However, the former study employed a corpus-based paraphrase generation system rather than grammar-driven surface realizers, and the resulting paraphrases exhibited much broader variation. In Cahill’s study, the outputs of the realizer were almost always grammatically correct, and the automated evaluation metrics were ranking markedness instead of grammatical acceptability. 5.3 System-level comparisons In order to investigate the efficacy of the metrics in ranking different realizer systems, or competing realizations from the same system generated using different ranking models, we considered seven different “systems” from the whole dataset of realizations. These consisted of five OpenCCG-based realizations (the best realization from three baseline models, and the best and the worst realization from the full perceptron model), and two XLE-based sys- tems (the best and the worst realization, after ranking the outputs of the XLE realizer with an n-gram model). The mean of the combined adequacy and 570 3 0.103 0.346 fluency scores of each of these seven systems was compared with that of every other system, resulting in 21 pairwise comparisons. Then Tukey’s HSD test was performed to determine the systems which differed significantly in terms of the average adequacy and fluency rating they received.4 The test revealed five pairwise comparisons where the scores were significantly different. Subsequently, for each of these systems, an overall system-level score for each of the MT metrics was calculated. For the five pairwise comparisons where the adequacy-fluency group means differed significantly, we checked whether the metric ranked the systems correctly. Table 8 shows the results of a pairwise comparison between the ranking induced by each evaluation metric, and the ranking induced by the human judgments. Five of the seven non- targeted metrics correctly rank more than half of the systems. NIST, METEOR, and GTM get the most comparisons right, but neither NIST nor GTM correctly rank the OpenCCG-baseline model 1 with respect to the XLE-best model. TER and TERP get two of the five comparisons correct, and they incorrectly rank two of the five OpenCCG model comparisons, as well as the comparison between the XLE-worst and OpenCCG-best systems. For the targeted metrics, HNIST is correct for all five comparisons, while neither HBLEU nor HMETEOR correctly rank all the OpenCCG models. On the other hand, HTER and HGTM incorrectly rank the XLE-best system versus OpenCCG-based models. In summary, some of the metrics get some of the rankings correct, but none of the non-targeted metrics get all of them correct. Moreover, different metrics make different ranking errors. This argues for 4This particular test was chosen since it corrects for multiple post-hoc analyses conducted on the same data-set. the use of multiple metrics in comparing realizer systems. 6 Conclusion Our study suggests that although the task of evaluating the output from realizer systems differs from the task of evaluating machine translations, the automatic metrics used to evaluate MT outputs deliver moderate correlations with combined human fluency and adequacy scores when used on surface realizations. We also found that the MT-evaluation metrics are useful in evaluating different versions of the same realizer system (e.g., the various OpenCCG realization ranking models), and finding cases where a system is performing poorly. As in MT-evaluation tasks, human-targeted metrics have the highest correlations with human judgments overall. These results suggest that the MT-evaluation metrics are useful for developing surface realizers. However, the correlations are lower than those reported for MT data, suggesting that they should be used with caution, especially for cross-system evaluation, where consulting multiple metrics may yield more reliable comparisons. In our study, the targeted version of TERP correlated most strongly with human judgments of fluency. In future work, the performance of the TER family of metrics on this data might be improved by opti- mizing the edit weights used in computing its scores, so as to avoid over-penalizing punctuation movements or under-penalizing agreement errors, both of which were significant sources of ranking errors. Multiple reference sentences may also help mitigate these problems, and the corpus of human-repaired realizations that has resulted from our study is a step in this direction, as it provides multiple references for some cases. We expect the corpus to also prove useful for feature engineering and error analysis in developing better realization models.5 Acknowledgements We thank Aoife Cahill and Tracy King for providing us with the output of the XLE generator. We also thank Chris Callison-Burch and the anonymous reviewers for their helpful comments and suggestions. 5The corpus can be downloaded from http : / /www . l ing .ohio-st ate . edu / ˜mwhite / dat a / emnlp 10 / . 571 This material is based upon work supported by the National Science Foundation under Grant No. 0812297. References Jason Baldridge. 2002. Lexically Specified Derivational Control in Combinatory Categorial Grammar. Ph.D. thesis, University of Edinburgh. S. Banerjee and A. Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pages 65–72. R. Barzilay and L. 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Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar. Research on Language and Computation, 4(1):39–75. 572 Table 1: Descriptive statistics Table 2: Corpora-wise inter-annotator agreement (absolute and relative κ values shown) SXAROWlpeyLos-aErFndAliCzueqrtd-GAFluq0 N.354217690 B.356219470M .35287410G .35241780 TP.465329170T.A34521670T.465230 H.54T76321H0 .543N89270H.653B7491280H.563M41270H.5643G218 Table 3: Spearman’s correlations among NIST (N), BLEU (B), METEOR (M), GTM (G), TERp (TP), TERpa (TA), TER (T), human variants (HN, HB, HM, HT, HG) and human judgments (-Adq: adequacy and -Flu: Fluency); Scores which fall within the 95 %CI of the best are italicized. SROXAWlLeypoasErldniCze rtG0 N.35246 190 B.5618740 M.542719G0 .5341890T .P632180T.A54268 0T .629310 H.7T6 3985H0 .546N180 H.765B8730H.673M5190 H.56G 4318 Table 4: Spearman’s correlations among NIST (N), BLEU (B), METEOR (M), GTM (G), TERp (TP), TERpa (TA), TER (T), human variants (HN, HB, HM, HT, HG) and human judgments (combined adequacy and fluency scores) 573 SRAXOWylLpeosatrEldniezCm rtG0S A.p61d35q94107 .5304%65874L09.5462%136U0SF .lp256u 1209 .51 6%9213L0 .562%91845U Table 5: Spearman’s correlation analysis (bootstrap sampling) of the BLEU scores of various systems with human adequacy and fluency scores SRXOAWylLpeosarEndiCztGH J -12 0 N.6543210 B.6512830 M.4532 960 G.13457960T.P56374210T.A45268730T.562738140 H.7T6854910H.56N482390H.675B1398240H.567M3 240H.56G41290H.8J71562- Table 6: Spearman’s correlations of NIST (N), BLEU (B), METEOR (M), GTM (G), TERp (TP), TERpa (TA), human variants (HT, HN, HB, HM, HG), and individual human judgments (combined adq. and flu. scores) Factor Count Punctuation17 Adverbial shift Agreement Other shifts Conjunct rearrangement Complementizer ins/del PP shift 16 14 8 8 5 4 Table 7: Factors influencing TERP ranking errors for 50 worst-ranked realization groups Table 8: Metric-wise ranking performance in terms of agreement with a ranking induced by combined adequacy and fluency scores; each metric gets a score out of 5 (i.e. number of system-level comparisons that emerged significant as per the Tukey’s HSD test) Legend: Perceptron Best (PB); Perceptron Worst (PW); XLE Best (XB); XLE Worst (XW); OpenCCG baseline models 1 to 3 (C1 ... C3) 574
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