emnlp emnlp2010 emnlp2010-89 knowledge-graph by maker-knowledge-mining

89 emnlp-2010-PEM: A Paraphrase Evaluation Metric Exploiting Parallel Texts

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Author: Chang Liu ; Daniel Dahlmeier ; Hwee Tou Ng

Abstract: We present PEM, the first fully automatic metric to evaluate the quality of paraphrases, and consequently, that of paraphrase generation systems. Our metric is based on three criteria: adequacy, fluency, and lexical dissimilarity. The key component in our metric is a robust and shallow semantic similarity measure based on pivot language N-grams that allows us to approximate adequacy independently of lexical similarity. Human evaluation shows that PEM achieves high correlation with human judgments.

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sentIndex sentText sentNum sentScore

1 s g Abstract We present PEM, the first fully automatic metric to evaluate the quality of paraphrases, and consequently, that of paraphrase generation systems. [sent-4, score-0.625]

2 The key component in our metric is a robust and shallow semantic similarity measure based on pivot language N-grams that allows us to approximate adequacy independently of lexical similarity. [sent-6, score-0.524]

3 Human evaluation shows that PEM achieves high correlation with human judgments. [sent-7, score-0.227]

4 1 Introduction In recent years, there has been an increasing interest in the task of paraphrase generation (PG) (Barzilay and Lee, 2003; Pang et al. [sent-8, score-0.507]

5 Considering that paraphrase evaluation is a very subjective task with no rigid definition, we conduct experiments with human judges to show that humans generally have a consistent intuition for good paraphrases, and that the three criteria are good indicators. [sent-28, score-0.723]

6 PEM takes as input the original sentence R and its paraphrase candidate P, and outputs a single numeric score b estimating the quality of P as a paraphrase of R. [sent-30, score-1.121]

7 To this end, we propose bag of pivot language N-grams (BPNG) as a robust, broad-coverage, and knowledge-lean semantic representation for natural language sentences. [sent-34, score-0.203]

8 Most importantly, BPNG does not depend on lexical or syntactic similarity, allowing us to address the conflicting requirements of paraphrase evaluation. [sent-35, score-0.533]

9 tc ho2d0s10 in A Nsastoucira tlio Lnan fogru Cagoem Ppruotcaetisosninagl, L pinag eusis 9t2ic3s–932, source required to evaluate BPNG is a parallel text of the target language and an arbitrary other language, known as the pivot language. [sent-38, score-0.184]

10 We highlight that paraphrase evaluation and paraphrase recognition (Heilman and Smith, 2010; Das and Smith, 2009; Wan et al. [sent-39, score-1.052]

11 A paraphrase recognition system should assign them a very high score, but a paraphrase evaluation system would assign a relatively low one. [sent-43, score-1.052]

12 We define the task of paraphrase evaluation in Section 3 and develop our metric in Section 4. [sent-47, score-0.633]

13 The correlation of PEM with human judgments is studied in Section 6. [sent-49, score-0.256]

14 Many of these metrics have been shown to correlate bet- ter with human judges than BLEU (Chan and Ng, 2008; Liu et al. [sent-58, score-0.175]

15 Interestingly, few MT evaluation metrics exploit parallel texts as a source of information, when statistical MT is centered almost entirely around mining parallel texts. [sent-60, score-0.206]

16 Compared to these MT evaluation metrics, our 924 method focuses on addressing the unique requirement of paraphrase evaluation: that lexical closeness does not necessarily entail goodness, contrary to the basis of MT evaluation. [sent-61, score-0.593]

17 The only prior attempt to devise an automatic evaluation metric for paraphrases that we are aware of is ParaMetric (Callison-Burch et al. [sent-65, score-0.314]

18 , 2008), which compares the collection of paraphrases discovered by automatic paraphrasing algorithms against a manual gold standard collected over the same sen- tences. [sent-66, score-0.238]

19 The recall and precision of several current paraphrase generation systems are evaluated. [sent-67, score-0.507]

20 ParaMetric does not attempt to propose a single metric to correlate well with human judgments. [sent-68, score-0.166]

21 3 Task definition The first step in defining a paraphrase evaluation metric is to define a good paraphrase. [sent-70, score-0.657]

22 To which we add a third, that the paraphrase must be fluent. [sent-73, score-0.507]

23 The first and last point are similar to MT evaluation, where adequacy and fluency have been established as the standard criteria. [sent-74, score-0.417]

24 In paraphrase evaluation, we have one more: lexical dissimilarity. [sent-75, score-0.533]

25 Although lexical dissimilarity is seemingly the easiest to judge automatically among the three, it poses an interesting challenge to automatic evaluation metrics, as overlap with the reference has been the basis of almost all evaluation metrics. [sent-76, score-0.406]

26 That is, while MT evaluation and paraphrase evaluation are conceptually closely related, the latter actually highlights the deficiencies of the former, namely that in most automatic evaluations, semantic equivalence is underrepresented and substituted by lexical and syntactic equivalence. [sent-77, score-0.675]

27 The task of paraphrase evaluation is then defined as follows: Given an original sentence R and a paraphrase candidate P, output a numeric score b estimating the quality of P as a paraphrase of R by considering adequacy, fluency, and lexical dissimilarity. [sent-78, score-1.692]

28 In particular, a high dissimilarity score is meaningless by itself. [sent-81, score-0.244]

29 It could simply be that the paraphrase is unrelated to the source sentence, or is incoherent. [sent-82, score-0.507]

30 However, when accompanied by high adequacy and fluency scores, it differentiates the mediocre para- phrases from the good ones. [sent-83, score-0.485]

31 To measure adequacy without relying on lexical similarity, we make the key observation that the aligned French texts can act as a proxy of the semantics to a fragment of an English text. [sent-97, score-0.293]

32 1 Figure 1: A confusion network in the pivot language Bonjour , monsieur . [sent-132, score-0.331]

33 0 Figure 2: A degenerated confusion network in the pivot language 4. [sent-134, score-0.216]

34 A naive comparison of the bags of French phrases aligned to Morning , sir . [sent-145, score-0.168]

35 The confusion network is formed by first joining the different French translations of every English phrase in parallel, and then joining these segments in series. [sent-149, score-0.202]

36 We call this representation of an English sentence a bag of pivot language N-grams (BPNG), where French is the pivot language in our illustrating ex- ample. [sent-172, score-0.33]

37 The F1 agreement is defined as F1=2 ×Pr ePcreisciiosino +n × Re Rceacllal The precision and the recall for an original sentence R and a paraphrase P is defined as follows. [sent-191, score-0.56]

38 We call the resulting metric the pivot language F1. [sent-199, score-0.222]

39 Our semantic representation successfully recognizes that put forth and put forward are paraphrases of each other, based on their similar Chinese translation statistics (ti2 chu1 in Chinese). [sent-206, score-0.297]

40 4 Fluency and dissimilarity We measure the fluency of a paraphrase by a normalized language model score Pn, defined by Pn=lloegngPthr( SS)) where Pr(S) is the sentence probability predicted by a standard 4-gram language model. [sent-208, score-0.998]

41 We measure dissimilarity between two English sentences using the target language F1, where we collect the bag of all N-grams up to 4-grams from each English (referred to as the target language) sentence. [sent-209, score-0.268]

42 The target language F1 is then defined as the F1 agreement of the two bags of N-grams, analogous to the definition of the pivot language F1. [sent-210, score-0.199]

43 The target language F1 correlates positively with the similar- ity of the two sentences, or equivalently, negatively with the dissimilarity of the two sentences. [sent-211, score-0.21]

44 5 The metric To produce the final PEM metric, we combine the three component automatic metrics, pivot language F1, normalized language model, and target language F1, which measure adequacy, fluency, and dissimilarity respectively. [sent-213, score-0.482]

45 The SVM is to be trained on a set of human-judged paraphrase pairs, where the three component automatic metrics are fit to the human overall assessment. [sent-218, score-0.658]

46 After training, the model can then be used to evaluate new paraphrase pairs in a fully automatic fashion. [sent-219, score-0.537]

47 5 Human evaluation To validate our definition of paraphrase evaluation and the PEM method, we conduct an experiment to evaluate paraphrase qualities manually, which allows us to judge whether paraphrase evaluation according to our definition is an inherently coherent and well-defined problem. [sent-220, score-1.747]

48 The evaluation also allows us to establish an upper bound for the paraphrase evaluation task, and to validate the contribution of the three proposed criteria to the overall paraphrase score. [sent-221, score-1.165]

49 Two other human translations and two automatic machine translations serve as paraphrases of the original sentences. [sent-226, score-0.356]

50 We refer to the two human translations and the two MT systems as paraphrase systems human1, human2, machine1, and machine2. [sent-227, score-0.606]

51 Therefore, each judge assesses 1,200 paraphrase pairs in total. [sent-229, score-0.552]

52 : LDC2002T01 judgment for each paraphrase pair consists of four scores, each given on a five-point scale: • Adequacy (Is the meaning preserved adequately? [sent-231, score-0.584]

53 ) • Lexical Dissimilarity (How much has the paraphrase changed ltahreit original sentence? [sent-233, score-0.531]

54 A good paraphrase should convey the same meaning as the original sentence, while being as different as possible on the surface form and being fluent and gram- matical English. [sent-235, score-0.531]

55 The paraphrases are presented to the judges in a random order and without any information as to which paraphrase system produced the paraphrase. [sent-237, score-0.722]

56 Our final data set therefore consists of 2,100 paraphrase pairs with judgments on 4 different criteria. [sent-240, score-0.574]

57 2 Inter-judge correlation The first step in our evaluation is to investigate the correlation between the human judges. [sent-242, score-0.362]

58 We investigate inter-judge correlation at the sentence and at the system level. [sent-244, score-0.164]

59 At the sentence level, we construct three vectors, each containing the 1,200 sentence level judgments from one judge 928 TaJbuldeg 1:CBInteJ0ur-. [sent-245, score-0.191]

60 Note that we exclude the three artificial control paraphrase systems from consideration, as that would inflate the correlation. [sent-253, score-0.507]

61 At the system level, we construct three vectors each of size four, containing the average scores given by one judge to each of the four paraphrase systems human1, human2, machine1, and machine2. [sent-254, score-0.577]

62 The high correlation confirms that our evaluation task is well defined. [sent-265, score-0.173]

63 Having confirmed that human judgments correlate strongly, we combine the scores of the three judges by taking their arithmetic mean. [sent-266, score-0.249]

64 Together with the three artificial control paraphrase systems, they form the human reference evaluation which we use for the remainder of the experiments. [sent-267, score-0.618]

65 3 Adequacy, fluency, and dissimilarity In this section, we empirically validate the importance of our three proposed criteria: adequacy, fluency, and lexical dissimilarity. [sent-269, score-0.236]

66 We can see a positive correlation of adequacy and Figure 3: Scatter plot of dissimilarity vs. [sent-272, score-0.592]

67 overall score for paraphrases with high adequacy and fluency. [sent-273, score-0.438]

68 fluency with the overall score, and the correlation with adequacy is particularly strong. [sent-274, score-0.579]

69 Thus, higher adequacy and to a lesser degree higher fluency indicate higher paraphrase quality to the human judges. [sent-275, score-0.978]

70 On the other hand, dissimilarity is found to have a negative correlation with the overall score. [sent-276, score-0.372]

71 This effect dominates a simple linear fitting of the paraphrase score vs. [sent-278, score-0.541]

72 We note that a high dissimilarity alone tells us little about the quality of the paraphrase. [sent-280, score-0.21]

73 Rather, we expect dissimilarity to be a differentiator between the mediocre and good paraphrases. [sent-281, score-0.232]

74 To test this hypothesis, we select the subset of paraphrase pairs that receive adequacy and fluency scores of at least four and again measure the correlation of the dissimilarity and the overall score. [sent-282, score-1.321]

75 The empirical results presented so far confirm that paraphrase evaluation is a well-defined task permitting consistent subjective judgments, and that ade- quacy, fluency, and dissimilarity are suitable criteria for paraphrase quality. [sent-285, score-1.31]

76 human evaluation In the last section, we have shown that the three proposed criteria are good indicators of paraphrase quality. [sent-288, score-0.647]

77 In this section, we investigate how well PEM can predict the overall paraphrase quality from the three automatic metrics (pivot language F1, normalized language model, and target language F1), designed to match the three evaluation criteria. [sent-289, score-0.662]

78 The paraphrases are taken from the MTC corpus in the same way as the human experiment described in Section 5. [sent-295, score-0.212]

79 Bags of Chinese pivot language N-grams are extracted for all paraphrase pairs as described in Section 4. [sent-307, score-0.641]

80 The language model used to judge fluency is trained on the English side of the FBIS parallel text. [sent-313, score-0.293]

81 The PEM SVM regression is trained on the paraphrase pairs for the first 200 original English sentences and tested on the paraphrase pairs of the remaining 100 original English sentences. [sent-315, score-1.082]

82 human judgment (overall score) at the sentence level Figure 5: Scatter plot of PEM vs. [sent-318, score-0.168]

83 human judgment (overall score) at the system level of pivot language F1, normalized language model score, and target language F1. [sent-319, score-0.265]

84 2 Results As in the human evaluation, we investigate the correlation of the PEM scores with the human judgments at the sentence and at the system level. [sent-322, score-0.364]

85 Fig- ure 4 shows the sentence level PEM scores plotted against the human overall scores, where each human overall score is the arithmetic mean of the scores given by the three judges. [sent-323, score-0.318]

86 The Pearson correlation between the automatic PEM scores and the human judgments is 0. [sent-324, score-0.311]

87 This is substantially higher than the sentence level correlation of MT metrics 930 TPa bE lMe 3:v sC . [sent-326, score-0.225]

88 For example, the highest sentence level Pearson correlation by any metric in the MetricsMATR 2008 competition (Przybocki et al. [sent-331, score-0.273]

89 The Pearson correlation between PEM scores and the human scores at the system level is 0. [sent-337, score-0.26]

90 We also calculate the Pearson correlation between PEM and each individual human judge. [sent-339, score-0.189]

91 Here, we exclude the three artificial control paraphrase systems from the data, to make the results comparable to the inter-judge correlation presented in Section 5. [sent-340, score-0.642]

92 As we would expect, the correlation between PEM and a human judge is not as high as the correlation between two human judges, but PEM still shows a strong and consistent correlation with all three judges. [sent-347, score-0.558]

93 Instead, the English paraphrases are multiple translations of the same Chinese source sentence. [sent-350, score-0.203]

94 The reason for using multi- ple translations is that we could not find any PG system that can paraphrase a whole input sentence and is publicly available. [sent-352, score-0.581]

95 Therefore, we can easily formulate a paraphrase generator by directly optimizing the PEM metric, although solving it is not trivial: paraphrase(R) = argmPaxPEM(P,R) where R is the original sentence and P is the paraphrase. [sent-364, score-0.56]

96 Finally, the PEM metric, in particular the semantic representation BPNG, can be useful in many other contexts, such as MT evaluation, summary evaluation, and paraphrase recognition. [sent-365, score-0.543]

97 8 Conclusion We proposed PEM, a novel automatic metric for paraphrase evaluation based on adequacy, fluency, and lexical dissimilarity. [sent-371, score-0.689]

98 The key component in our metric is a novel technique to measure the semantic similarity of two sentences through their N-gram overlap in an aligned foreign language text. [sent-372, score-0.165]

99 We conducted an extensive human evaluation of paraphrase quality which shows that our proposed metric achieves high correlation with human judgments. [sent-373, score-0.876]

100 To the best of our knowledge, PEM is the first auto- matic metric for paraphrase evaluation. [sent-374, score-0.595]

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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. 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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. Lee. 2003. Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. In proceedings of HLT-NAACL, volume 2003, pages 16–23. Aoife Cahill. 2009. Correlating human and automatic evaluation of a german surface realiser. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 97–100, Suntec, Singapore, August. Association for Computational Linguistics. C. Callison-Burch, M. Osborne, and P. Koehn. 2006. Reevaluating the role of BLEU in machine translation research. In Proceedings of EACL, volume 2006, pages 249–256. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz, and Josh Schroeder. 2007. (meta-) evaluation ofmachine translation. In StatMT ’07: Proceedings of the Second Workshop on Statistical Machine Translation, pages 136–158, Morristown, NJ, USA. Association for Computational Linguistics. C. Callison-Burch, T. Cohn, and M. Lapata. 2008. Parametric: An automatic evaluation metric for paraphrasing. In Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pages 97–104. Association for Computational Linguistics. J. Carletta. 1996. Assessing agreement on classification tasks: the kappa statistic. Computational linguistics, 22(2):249–254. Dick Crouch, Mary Dalrymple, Ron Kaplan, Tracy King, John Maxwell, and Paula Newman. 2008. Xle documentation. Technical report, Palo Alto Research Center. Philip Koenh. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. J.R. Landis and G.G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics, 33(1): 159–174. Lin and Franz Josef Och. 2004. Orange: a method for evaluating automatic evaluation metrics for machine translation. In COLING ’04: Proceedings Chin-Yew of the 20th international conference on Computational 501, Morristown, NJ, USA. Associfor Computational Linguistics. Papineni, S. Roukos, T. Ward, and W. Zhu. 2001. Linguistics, page ation K. Bleu: a method for automatic evaluation of machine translation. E. Technical report, IBM Research. Reiter and A. Belz. 2009. An investigation into the validity of some metrics for automatically evaluating natural language generation systems. Computational Linguistics, 35(4):529–558. Stefan Riezler, Tracy H. King, Ronald M. Kaplan, Richard Crouch, John T. III Maxwell, and Mark Johnson. 2002. Parsing the wall street journal using a lexical-functional grammar and discriminative estimation techniques. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, pages 271–278, Philadelphia, Pennsylvania, USA, July. Association for Computational Linguistics. Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In In Proceedings of Association for Machine Translation in the Americas, pages 223–23 1. M. Snover, N. Madnani, B.J. Dorr, and R. Schwartz. 2009. Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric. In Proceedings of the Fourth Workshop on Statistical Machine Translation, pages 259–268. Association for Computational Linguistics. Amanda Stent, Matthew Marge, and Mohit Singhai. 2005. Evaluating evaluation methods for generation in the presence of variation. In Proceedings of CICLing. J.P. Turian, L. Shen, and I.D. Melamed. 2003. Evaluation of machine translation and its evaluation. recall (C— R), 100:2. Michael White and Rajakrishnan Rajkumar. 2009. Perceptron reranking for CCG realization. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 410–419, Singapore, August. Association for Computational Linguistics. Michael White. 2006. 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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