acl acl2013 acl2013-299 knowledge-graph by maker-knowledge-mining
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Author: Ryo Nagata ; Edward Whittaker
Abstract: Mother tongue interference is the phenomenon where linguistic systems of a mother tongue are transferred to another language. Although there has been plenty of work on mother tongue interference, very little is known about how strongly it is transferred to another language and about what relation there is across mother tongues. To address these questions, this paper explores and visualizes mother tongue interference preserved in English texts written by Indo-European language speakers. This paper further explores linguistic features that explain why certain relations are preserved in English writing, and which contribute to related tasks such as native language identification.
Reference: text
sentIndex sentText sentNum sentScore
1 Abstract Mother tongue interference is the phenomenon where linguistic systems of a mother tongue are transferred to another language. [sent-3, score-1.232]
2 Although there has been plenty of work on mother tongue interference, very little is known about how strongly it is transferred to another language and about what relation there is across mother tongues. [sent-4, score-1.04]
3 To address these questions, this paper explores and visualizes mother tongue interference preserved in English texts written by Indo-European language speakers. [sent-5, score-0.94]
4 This paper further explores linguistic features that explain why certain relations are preserved in English writing, and which contribute to related tasks such as native language identification. [sent-6, score-0.253]
5 1 Introduction Transfer of linguistic systems of a mother tongue to another language, namely mother tongue interference, is often observable in the writing of nonnative speakers. [sent-7, score-1.295]
6 The reader may be able to determine the mother tongue of the writer of the following sentence from the underlined article error: The alien wouldn ’t use my spaceship but the hers. [sent-8, score-0.71]
7 The answer would probably be French or Spanish; the definite article is allowed to modify possessive pronouns in these languages, and the usage is sometimes negatively transferred to English writing. [sent-9, score-0.229]
8 Researchers such as Swan and Smith (2001), Aarts and Granger (1998), DavidsenNielsen and Harder (2001), and Altenberg and Tapper (1998) work on mother tongue interference to reveal overused/underused words, part of speech (POS), or grammatical items. [sent-10, score-0.824]
9 uk , In contrast, very little is known about how strongly mother tongue interference is transferred to another language and about what relation there is across mother tongues. [sent-13, score-1.242]
10 At one extreme, one could argue that it is so strongly transferred to texts in another language that the linguistic relations between mother tongues are perfectly preserved in the texts. [sent-14, score-0.578]
11 At the other extreme, one can counter it, arguing that other features such as non-nativeness are more influential than mother tongue interference. [sent-15, score-0.622]
12 One possible reason for this is that a large part of the distinctive language systems of a mother tongue may be eliminated when transferred to another language from a speaker’s mother tongue. [sent-16, score-1.008]
13 However, the difference in the richness cannot be transferred into English because English has almost no inflectional case system. [sent-20, score-0.125]
14 Thus, one cannot determine the mother tongue of a given nonnative text from the inflectional case. [sent-21, score-0.712]
15 Besides, Wong and Dras (2009) show that there are no significant differences, between mother tongues, in the misuse of certain syntactic features such as subject-verb agreement that have different tendencies depending on their mother tongues. [sent-23, score-0.6]
16 We hypothesize that: Hypothesis: Mother tongue interference is so strong that the relations in a language family are preserved in texts written in another language. [sent-27, score-0.871]
17 In other words, mother tongue interference is so strong that one can reconstruct a language fam1137 Proce dingsS o f ita h,e B 5u1lgsta Arinan,u Aaulg Musete 4ti-n9g 2 o0f1 t3h. [sent-28, score-0.891]
18 One of the major contributions of this work is to reveal and visualize a language family tree preserved in non-native texts, by examining the hypothesis. [sent-31, score-0.375]
19 This becomes important in native language identification1 which is useful for improving grammatical error correction systems (Chodorow et al. [sent-32, score-0.195]
20 6, this paper reveals several crucial findings that contribute to improving native language identification. [sent-35, score-0.195]
21 In addition, this paper shows that the findings could contribute to reconstruction of language family trees (Enright and Kondrak, 2011; Gray and Atkinson, 2003; Barbanc ¸on et al. [sent-36, score-0.263]
22 2 Approach To examine the hypothesis, we reconstruct a language family tree from English texts written by non-native speakers of English whose mother tongue is one of the Indo-European languages (Beekes, 2011; Ramat and Ramat, 2006). [sent-51, score-1.151]
23 If the reconstructed tree is sufficiently similar to the original Indo-European family tree, it will support the hypothesis. [sent-52, score-0.377]
24 If not, it suggests that some features other than mother tongue interference are more influential. [sent-53, score-0.824]
25 The approach we use for reconstructing a language family tree is to apply agglomerative hierarchical clustering (Han and Kamber, 2006) to English texts written by non-native speakers. [sent-54, score-0.549]
26 Researchers have already performed related work on reconstructing language family trees. [sent-55, score-0.319]
27 (1992) and Kita (1999) proposed methods for reconstructing language family trees using clustering. [sent-58, score-0.351]
28 Among them, the 1Recently, native language identification has drawn the at- tention of NLP researchers. [sent-59, score-0.195]
29 For instance, a shared task on native language identification took place at an NAACL-HLT 2013 workshop. [sent-60, score-0.195]
30 Then, agglomerative hierarchical clustering is applied to the language models to reconstruct a language family tree. [sent-65, score-0.384]
31 The similarity used for clustering is based on a divergence-like distance between two language models that was originally proposed by Juang and Rabiner (1985). [sent-66, score-0.097]
32 Kita’s work (and other previous work) targets clustering of a variety of languages whereas our work tries to reconstruct a language family tree preserved in non-native English. [sent-70, score-0.534]
33 Obviously, this does not work on our task; belly is belly in English writing whoever writes it. [sent-74, score-0.098]
34 Although spelling is sometimes influenced by mother tongues, it involves a lot more including overuse, underuse, and misuse of lexical, grammatical, and syntactic systems. [sent-78, score-0.3]
35 To solve the problem, this work adopts a wordbased language model in the expectation that word sequences reflect mother tongue interference. [sent-79, score-0.622]
36 It would reflect the topics of given texts rather than mother tongue interference. [sent-81, score-0.68]
37 Let Di be a set of English 1138 texts where idenotes a mother tongue i. [sent-87, score-0.68]
38 EOS Note that the content of the original sentence is far from clear while reflecting mother tongue interference, especially in the hers. [sent-104, score-0.622]
39 The clustering algorithm used is agglomerative hierarchical clustering with the average linkage method. [sent-110, score-0.141]
40 To sum up, the procedure of the language fam- ily tree construction method is as follows: (i) Preprocess each Di; (ii) Build Mi from Di; (iii) Calculate the distances between the language models; (iv) Cluster the language data using the distances; (v) Output the result as a language family tree. [sent-120, score-0.348]
41 2 Vector-based Method We also examine a vector-based method for language family tree reconstruction. [sent-122, score-0.317]
42 5, this method allows us to interpret clustering results more easily than with the language model-based method while both result in similar language family trees. [sent-124, score-0.286]
43 The clustering procedure is the same as for the language model-based method except that Mi is vector-based and that the distance metric is Euclidean. [sent-128, score-0.097]
44 It consists of English essays written by a wide variety of nonnative speakers of English. [sent-132, score-0.201]
45 For reference, we also used native English (British and American university students’ essays in the LOCNESS corpus5) and two sets of Japanese English (ICLE and the NICE corpus (Sugiura et al. [sent-139, score-0.295]
46 Existing POS taggers might not perform well on non-native English texts because they are normally developed to analyze native English texts. [sent-143, score-0.253]
47 Although it did not perform as well as on native texts, it still achieved a fair accuracy. [sent-149, score-0.195]
48 3For example, because of (iii), essays written by native speakers of Swedish in the Finnish subcorpus were excluded from the experiments. [sent-153, score-0.399]
49 5The LOCNESS corpus is a corpus of native English essays made up of British pupils’ essays, British university students’ essays, and American university students’ essays: http s : / /www . [sent-158, score-0.295]
50 Similarly, we implemented the vector-based method with trigrams using the same frequency cutoff (but without smoothing). [sent-167, score-0.092]
51 The tree at the top is the Indo-European family tree drawn based on the figure shown in Crystal (1997). [sent-170, score-0.403]
52 The second and third trees are the cluster trees generated by the language model-based and vector-based methods, respectively. [sent-172, score-0.1]
53 The two languages belong to the North Germanic branch of the Germanic branch and thus are closely related. [sent-179, score-0.099]
54 ference between its cluster tree and the IndoEuropean family tree is that there are some mismatches within the Germanic and Slavic branches. [sent-185, score-0.439]
55 From these results, we can say that mother tongue interference is transferred into the 11 Englishes, strongly enough for reconstructing its language family tree, which we propose calling the interlanguage Indo-European family tree in English. [sent-188, score-1.645]
56 2 shows the experimental results with native and Japanese Englishes. [sent-190, score-0.195]
57 It shows that the same interlanguage Indo-European family tree was reconstructed as before. [sent-191, score-0.444]
58 More interestingly, native English was detached from the interlanguage Indo-European family tree contrary to the expectation that it would be attached to the Germanic branch because English is of course a member of the Germanic branch. [sent-192, score-0.61]
59 Interlagu eIndo-Eurpeanfm12ilytreEN1nag3tilsvheJEanpOgatlnhsie r31efJaEmnpgaislnyehs2 Figure 2: Experimental results with native and Japanese Englishes. [sent-194, score-0.195]
60 otherwise, native English would be included in the German branch. [sent-195, score-0.195]
61 Based on these results, we can further hypothesize as follows: interfamily distance > non-nativeness > intrafamily distance. [sent-199, score-0.14]
62 5 Discussion To get a better understanding of the interlanguage Indo-European family tree, we further explore lin- guistic features that explain well the above phenomena. [sent-200, score-0.298]
63 It is almost impossible to find someone who has a good knowledge of the 11 languages and their mother language interference in English writing. [sent-202, score-0.539]
64 Also, because we had access to a native speaker of Russian who had a good knowledge of English, we included Russian-English in our focus. [sent-209, score-0.195]
65 Second, we used a method for extracting interesting trigrams from the corpus data. [sent-211, score-0.092]
66 If we remove instances of a trigram from each set, the clustering tree involving 1141 the three may change. [sent-213, score-0.185]
67 For example, the removal of but the hers may result in a cluster tree merging French- and Russian-Englishes before Frenchand Spanish-Englishes. [sent-214, score-0.122]
68 We analyzed what trigrams had contributed to the clustering results with this approach. [sent-216, score-0.147]
69 Thus, the greater s is, the higher the chance that the cluster tree changes. [sent-221, score-0.122]
70 Therefore, we can obtain a list of interesting trigrams by sorting them according to s. [sent-222, score-0.092]
71 Table 2 shows the top 15 interesting trigrams where Di, Dj, and Dk are French-, Spanish-, and Russian-Englishes, respectively. [sent-226, score-0.092]
72 The list reveals that many of the trigrams contain the article a or the. [sent-228, score-0.18]
73 An exception is that oftrigrams containing the definite article in Bulgarian-English; it tends to be higher in Bulgarian-English than in the other Slavic Englishes. [sent-236, score-0.143]
74 Surprisingly and interestingly, however, it reflects the fact that Bulgarian does have the definite article but not the indefinite article (e. [sent-237, score-0.266]
75 Table 3 shows that the differences in article use exist even between the Italic and Germanic branches despite the fact that both have the indefinite and definite articles. [sent-246, score-0.226]
76 The definite article is used more frequently in the Italic-Englishes than in the Germanic Englishes (except for Dutch-English). [sent-252, score-0.143]
77 We speculate that this is perhaps because the Italic languages have a wider usage of the definite article such as its modification of possessive pronouns and proper nouns. [sent-253, score-0.18]
78 This corresponds to the fact that noun-noun compounds are less common in the Italic languages than in English and that instead, the of-phrase (NN of NN) is preferred (Swan and Smith, 2001). [sent-324, score-0.116]
79 instance, orange juice is expressed as juice of orange in the Italic languages (e. [sent-392, score-0.223]
80 In other words, the length tends to be shorter than in the others where we define the length as the number of consecutive repetitions of common nouns (for example, the length of orange juice is one because a noun is consecutively repeated once). [sent-401, score-0.126]
81 This tendency in the length of nounnoun compounds provides us with a crucial insight for native language identification, which we will Relative frequency of indefinite article (%) Figure 3: Distribution of articles. [sent-406, score-0.397]
82 (2005) work on native language identification and show that machine learning-based methods are effective. [sent-427, score-0.195]
83 The experimental results show that n-grams containing articles are predictive for identifying native languages. [sent-430, score-0.228]
84 This indicates that they should be used in the native language identification task. [sent-431, score-0.195]
85 Importantly, all n-grams containing articles should be used in the classifier unlike the previous methods that are based only on ngrams containing article errors. [sent-432, score-0.121]
86 ” In addition, the length of noun-noun compounds and the position of adverbs should also be considered in native language identification. [sent-435, score-0.306]
87 7 shows that the observed values in the French- English data very closely fit the theoretical proba11For comparison, we conducted a pilot study where we reconstructed a language family tree from English texts in European Parliament Proceedings Parallel Corpus (Europarl) (Koehn, 2011). [sent-442, score-0.435]
88 It turned out that the reconstructed tree was different from the canonical tree (available at http : / /web . [sent-443, score-0.232]
89 Consequently, Equation (5) should be useful in native language identi- fication. [sent-453, score-0.195]
90 In the domain of historical linguistics, researchers have used computational and corpusbased methods for reconstructing language family trees. [sent-455, score-0.319]
91 , 2005) apply clustering techniques to the task of language family tree reconstruction. [sent-459, score-0.372]
92 These methods reconstruct language family trees based on linguistic features that exist within words including lexical, phonological, and morphological features. [sent-461, score-0.33]
93 The experimental results in this paper suggest the possibility of the use of non-native texts for reconstructing language family trees. [sent-462, score-0.377]
94 7 Conclusions In this paper, we have shown that mother tongue interference is so strong that the relations between members of the Indo-European language family are preserved in English texts written by Indo-European language speakers. [sent-470, score-1.171]
95 To show this, we have used clustering to reconstruct a language family tree from 11 sets of non-native English texts. [sent-471, score-0.439]
96 It turned out that the reconstructed tree correctly groups them into the Italic, Germanic, and Slavic branches of the IndoEuropean family tree. [sent-472, score-0.425]
97 Based on the resulting trees, we have then hypothesized that the following relation holds in mother tongue interference: interfamily distance > non-nativeness > intrafamily distance. [sent-473, score-0.762]
98 Determining an author’s native language by mining a text for errors. [sent-562, score-0.195]
99 From bag of languages to family trees from noisy corpus. [sent-582, score-0.3]
100 A discriminant analysis of non-native speakers and native speakers of English. [sent-595, score-0.295]
wordName wordTfidf (topN-words)
[('englishes', 0.349), ('tongue', 0.322), ('engilsh', 0.32), ('mother', 0.3), ('family', 0.231), ('interference', 0.202), ('native', 0.195), ('nn', 0.191), ('italic', 0.177), ('kita', 0.132), ('germanic', 0.126), ('slavic', 0.114), ('di', 0.108), ('essays', 0.1), ('trigrams', 0.092), ('reconstructing', 0.088), ('article', 0.088), ('tree', 0.086), ('transferred', 0.086), ('mi', 0.084), ('batagelj', 0.082), ('jj', 0.081), ('compounds', 0.079), ('eos', 0.076), ('mj', 0.073), ('interlanguage', 0.067), ('dras', 0.067), ('reconstruct', 0.067), ('reconstructed', 0.06), ('texts', 0.058), ('preserved', 0.058), ('ramat', 0.058), ('bos', 0.056), ('wong', 0.056), ('clustering', 0.055), ('nns', 0.055), ('definite', 0.055), ('rti', 0.054), ('subcorpus', 0.054), ('rb', 0.054), ('nonnative', 0.051), ('granger', 0.051), ('koppel', 0.051), ('speakers', 0.05), ('barbanc', 0.049), ('belly', 0.049), ('enright', 0.049), ('interfamily', 0.049), ('intrafamily', 0.049), ('nakhleh', 0.049), ('swan', 0.049), ('dj', 0.049), ('orange', 0.049), ('branches', 0.048), ('pr', 0.047), ('dk', 0.045), ('juice', 0.044), ('tongues', 0.044), ('trigram', 0.044), ('english', 0.043), ('distance', 0.042), ('japanese', 0.041), ('crftagger', 0.04), ('icle', 0.04), ('inflectional', 0.039), ('french', 0.039), ('phylogenetic', 0.038), ('languages', 0.037), ('cluster', 0.036), ('indoeuropean', 0.036), ('swedish', 0.035), ('europarl', 0.035), ('indefinite', 0.035), ('articles', 0.033), ('russian', 0.033), ('repetitions', 0.033), ('aarts', 0.033), ('altenberg', 0.033), ('elleg', 0.033), ('ivbp', 0.033), ('kroeber', 0.033), ('locness', 0.033), ('luay', 0.033), ('overuse', 0.033), ('ringe', 0.033), ('rtj', 0.033), ('rtk', 0.033), ('ryo', 0.033), ('strigram', 0.033), ('sugiura', 0.033), ('tandy', 0.033), ('tapper', 0.033), ('trtirtjrtk', 0.033), ('warnow', 0.033), ('adverbs', 0.032), ('strongly', 0.032), ('trees', 0.032), ('distances', 0.031), ('branch', 0.031), ('agglomerative', 0.031)]
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(201 1), we assume that a part-of-speech tagger is available for the target language. 2.2 SRL in the Low-resource Setting Several approaches have been proposed to obtain an SRL model for a new language with little or no manual annotation. Unsupervised SRL models (Lang and Lapata, 2010) cluster the arguments of predicates in a given corpus according to their semantic roles. The performance of such models can be impressive, especially for those languages where semantic roles correlate strongly with syntactic relation of the argument to its predicate. However, assigning meaningful role labels to the resulting clusters requires additional effort and the model’s parameters generally need some adjustment for every language. If the necessary resources are already available for a closely related language, they can be utilized to facilitate the construction of a model for the target language. This can be achieved either by means of cross-lingual annotation projection (Yarowsky et al., 2001) or by cross-lingual model transfer (Zeman and Resnik, 2008). This last approach is the one we are considering in this work, and the other two options are treated as baselines. The unsupervised model will be further referred to as UNSUP and the projection baseline as PROJ. 2.3 Evaluation Measures We use the F1 measure as a metric for the argument identification stage and accuracy as an aggregate measure of argument classification performance. When comparing to the unsupervised SRL system the clustering evaluation measures are used instead. These are purity and collocation 1191 N1Ximajx|Gj∩ Ci| CO =N1Xjmiax|Gj∩ Ci|, PU = where Ci is the set of arguments in the i-th induced cluster, Gj is the set of arguments in the jth gold cluster and N is the total number of arguments. We report the harmonic mean ofthe two (Lang and Lapata, 2011) and denote it F1c to avoid confusing it with the supervised metric. 3 Model Transfer The idea of this work is to abstract the model away from the particular source language and apply it to a new one. This setup requires that we use the same feature representation for both languages, for example part-of-speech tags and dependency relation labels should be from the same inventory. Some features are not applicable to certain lan- guages because the corresponding phenomena are absent in them. For example, consider a strongly inflected language and an analytic one. While the latter can usually convey the information encoded in the word form in the former one (number, gender, etc.), finding a shared feature representation for such information is non-trivial. In this study we will confine ourselves to those features that are applicable to all languages in question, namely: part-of-speech tags, syntactic dependency structures and representations of the word’s identity. 3.1 Lexical Information We train a model on one language and apply it to a different one. In order for this to work, the words of the two languages have to be mapped into a common feature space. It is also desirable that closely related words from both languages have similar representations in this space. Word mapping. The first option is simply to use the source language words as the shared representation. Here every source language word would have itself as its representation and every target word would map into a source word that corresponds to it. In other words, we supply the model with a gloss of the target sentence. The mapping (bilingual dictionary) we use is derived from a word-aligned parallel corpus, by identifying, for each word in the target language, the word in the source language it is most often aligned to. Cross-lingual clusters. There is no guarantee that each of the words in the evaluation data is present in our dictionary, nor that the corresponding source-language word is present in the training data, so the model would benefit from the ability to generalize over closely related words. This can, for example, be achieved by using cross-lingual word clusters induced in T ¨ackstr o¨m et al. (2012). We incorporate these clusters as features into our model. 3.2 Syntactic Information Part-of-speech Tags. We map part-of-speech tags into the universal tagset following Petrov et al. (2012). This may have a negative effect on the performance of a monolingual model, since most part-of-speech tagsets are more fine-grained than the universal POS tags considered here. For example Penn Treebank inventory contains 36 tags and the universal POS tagset only 12. Since the finergrained POS tags often reflect more languagespecific phenomena, however, they would only be useful for very closely related languages in the cross-lingual setting. The universal part-of-speech tags used in evaluation are derived from gold-standard annotation for all languages except French, where predicted ones had to be used instead. Dependency Structure. Another important aspect of syntactic information is the dependency structure. Most dependency relation inventories are language-specific, and finding a shared representation for them is a challenging problem. One could map dependency relations into a simplified form that would be shared between languages, as it is done for part-of-speech tags in Petrov et al. (2012). The extent to which this would be useful, however, depends on the similarity of syntactic-semantic in– terfaces of the languages in question. In this work we discard the dependency relation labels where the inventories do not match and only consider the unlabeled syntactic dependency graph. Some discrepancies, such as variations in attachment order, may be present even there, but this does not appear to be the case with the datasets we use for evaluation. If a target language is poor in resources, one can obtain a dependency parser for the target language by means of cross-lingual model transfer (Zeman and Resnik, 2008). We 1192 take this into account and evaluate both using the original dependency structures and the ones obtained by means of cross-lingual model transfer. 3.3 The Model The model we use is based on that of Bj ¨orkelund et al. (2009). It is comprised of a set of linear classifiers trained using Liblinear (Fan et al., 2008). The feature model was modified to accommodate the cross-lingual cluster features and the reranker component was not used. We do not model the interaction between different argument roles in the same predicate. While this has been found useful, in the cross-lingual setup one has to be careful with the assumptions made. For example, modeling the sequence of roles using a Markov chain (Thompson et al., 2003) may not work well in the present setting, especially between distant languages, as the order or arguments is not necessarily preserved. Most constraints that prove useful for SRL (Chang et al., 2007) also require customization when applied to a new language, and some rely on languagespecific resources, such as a valency lexicon. Taking into account the interaction between different arguments of a predicate is likely to improve the performance of the transferred model, but this is outside the scope of this work. 3.4 Feature Selection Compatibility of feature representations is necessary but not sufficient for successful model transfer. We have to make sure that the features we use are predictive of similar outcomes in the two languages as well. Depending on the pair of languages in question, different aspects of the feature representation will retain or lose their predictive power. We can be reasonably certain that the identity of an argument word is predictive of its semantic role in any language, but it might or might not be true of, for example, the word directly preceding the argument word. It is therefore important to pre- SCPDGylOespoSntreslTabunc1lra:obsFel-daitnguplrdoaeusntpagd-elronwfu-dcsopeyrnsd c.eylafguhtorsia mepgnrhs vent the model from capturing overly specific aspects of the source language, which we do by confining the model to first-order features. We also avoid feature selection, which, performed on the source language, is unlikely to help the model to better generalize to the target one. The experiments confirm that feature selection and the use of second-order features degrade the performance of the transferred model. 3.5 Feature Groups For each word, we use its part-of-speech tag, cross-lingual cluster id, word identity (glossed, when evaluating on the target language) and its dependency relation to its parent. Features associated with an argument word include the attributes of the predicate word, the argument word, its parent, siblings and children, and the words directly preceding and following it. Also included are the sequences of part-of-speech tags and dependency relations on the path between the predicate and the argument. Since we are also interested in the impact of different aspects of the feature representation, we divide the features into groups as summarized in table 1 and evaluate their respective contributions to the performance of the model. If a feature group is enabled the model has access to the corre– sponding source of information. For example, if only POS group is enabled, the model relies on the part-of-speech tags of the argument, the predicate and the words to the right and left of the argument word. If Synt is enabled too, it also uses the POS tags of the argument’s parent, children and siblings. Word order information constitutes an implicit group that is always available. It includes the Pos it ion feature, which indicates whether the argument is located to the left or to the right of the predicate, and allows the model to look up the attributes of the words directly preceding and following the argument word. The model we compare against the baselines uses all applicable feature groups (Deprel is only used in EN-CZ and CZ-EN experiments with original syntax). 4 Evaluation 4.1 Datasets and Preprocessing Evaluation of the cross-lingual model transfer requires a rather specific kind of dataset. Namely, the data in both languages has to be annotated 1193 with the same set of semantic roles following the same (or compatible) guidelines, which is seldom the case. We have identified three language pairs for which such resources are available: EnglishChinese, English-Czech and English-French. The evaluation datasets for English and Chinese are those from the CoNLL Shared Task 2009 (Haji ˇc et al., 2009) (henceforth CoNLL-ST). Their annotation in the CoNLL-ST is not identical, but the guidelines for “core” semantic roles are similar (Kingsbury et al., 2004), so we evaluate only on core roles here. The data for the second language pair is drawn from the Prague Czech-English Dependency Treebank 2.0 (Haji ˇc et al., 2012), which we converted to a format similar to that of CoNLL-ST1 . The original annotation uses the tectogrammatical representation (Haji ˇc, 2002) and an inventory of semantic roles (or functors), most of which are interpretable across various predicates. Also note that the syntactic anno- tation of English and Czech in PCEDT 2.0 is quite similar (to the extent permitted by the difference in the structure of the two languages) and we can use the dependency relations in our experiments. For English-French, the English CoNLL-ST dataset was used as a source and the model was evaluated on the manually annotated dataset from van der Plas et al. (201 1). The latter contains one thousand sentences from the French part ofthe Europarl (Koehn, 2005) corpus, annotated with semantic roles following an adapted version of PropBank (Palmer et al., 2005) guidelines. The authors perform annotation projection from English to French, using a joint model of syntax and semantics and employing heuristics for filtering. We use a model trained on the output of this projection system as one of the baselines. The evaluation dataset is relatively small in this case, so we perform the transfer only one-way, from English to French. The part-of-speech tags in all datasets were replaced with the universal POS tags of Petrov et al. (2012). For Czech, we have augmented the map- pings to account for the tags that were not present in the datasets from which the original mappings were derived. Namely, tag “t” is mapped to “VERB” and “Y” to “PRON”. We use parallel data to construct a bilingual dictionary used in word mapping, as well as in the projection baseline. For English-Czech – 1see http://www.ml4nlp.de/code-and-data/treex2conll and English-French, the data is drawn from Europarl (Koehn, 2005), for English-Chinese from MultiUN (Eisele and Chen, 2010). The word alignments were obtained using GIZA++ (Och and Ney, 2003) and the intersection heuristic. – 4.2 Syntactic Transfer In the low-resource setting, we cannot always rely on the availability of an accurate dependency parser for the target language. If one is not available, the natural solution would be to use crosslingual model transfer to obtain it. Unfortunately, the models presented in the previous work, such as Zeman and Resnik (2008), McDonald et al. (201 1) and T ¨ackstr o¨m et al. (2012), were not made available, so we reproduced the direct transfer algorithm of McDonald et al. (201 1), using Malt parser (Nivre, 2008) and the same set of features. We did not reimplement the projected transfer algorithm, however, and used the default training procedure instead of perceptron-based learning. The dependency structure thus obtained is, of course, only a rough approximation even a much more sophisticated algorithm may not perform well when transferring syntax between such languages as Czech and English, given the inherent difference in their structure. The scores are shown in table 2. We will henceforth refer to the syntactic annotations that were provided with the datasets as original, as opposed to the annotations obtained by means of syntactic transfer. – 4.3 Baselines Unsupervised Baseline: We are using a version of the unsupervised semantic role induction system of Titov and Klementiev (2012a) adapted to SetupUAS, % Table2:SyntaciE C ZcN HNt- rE ZaCFnN HZRsfer34 692567acuracy,unlabe dat- tachment score (percent). Note that in case of French we evaluate against the output of a supervised system, since manual annotation is not available for this dataset. This score does not reflect the true performance of syntactic transfer. 1194 the shared feature representation considered in order to make the scores comparable with those of the transfer model and, more importantly, to enable evaluation on transferred syntax. Note that the original system, tailored to a more expressive language-specific syntactic representation and equipped with heuristics to identify active/passive voice and other phenomena, achieves higher scores than those we report here. Projection Baseline: The projection baseline we use for English-Czech and English-Chinese is a straightforward one: we label the source side of a parallel corpus using the source-language model, then identify those verbs on the target side that are aligned to a predicate, mark them as predicates and propagate the argument roles in the same fashion. A model is then trained on the resulting training data and applied to the test set. For English-French we instead use the output of a fully featured projection model of van der Plas et al. (201 1), published in the CLASSiC project. 5 Results In order to ensure that the results are consistent, the test sets, except for the French one, were partitioned into five equal parts (of 5 to 10 thousand sentences each, depending on the dataset) and the evaluation performed separately on each one. All evaluation figures for English, Czech or Chinese below are the average values over the five subsets. In case of French, the evaluation dataset is too small to split it further, so instead we ran the evaluation five times on a randomly selected 80% sample of the evaluation data and averaged over those. In both cases the results are consistent over the subsets, the standard deviation does not exceed 0.5% for the transfer system and projection baseline and 1% for the unsupervised system. 5.1 Argument Identification We summarize the results in table 3. Argument identification is known to rely heavily on syntactic information, so it is unsurprising that it proves inaccurate when transferred syntax is used. Our simple projection baseline suffers from the same problem. Even with original syntactic information available, the performance of argument identification is moderate. Note that the model of (van der Plas et al., 2011), though relying on more expressive syntax, only outperforms the transferred system by 3% (F1) on this task. SetupSyntaxTRANSPROJ ZEC NH Z- EFCZNRHt r a n s 3462 1. 536 142 35. 4269 Table3EZ C:N H- CFEZANHZRrgumeon rt ig identf56 7ic13 a. t27903ion,21569t10ra. 3976nsferd model vs. projection baseline, F1. Most unsupervised SRL approaches assume that the argument identification is performed by some external means, for example heuristically (Lang and Lapata, 2011). Such heuristics or unsupervised approaches to argument identification (Abend et al., 2009) can also be used in the present setup. 5.2 Argument Classification In the following tables, TRANS column contains the results for the transferred system, UNSUP for the unsupervised baseline and PROJ for projection baseline. We highlight in bold the higher score where the difference exceeds twice the maximum of the standard deviation estimates of the two results. Table 4 presents the unsupervised evaluation results. Note that the unsupervised model performs as well as the transferred one or better where the – – SetupSyntaxTRANSUNSUP ZEC NH Z- EFCZNRHt r a n s 768 93648. 34627 6 5873. 1769 TableEZ C4NHZ:- FCEZANHZRrgumoe nr itg clasi78 fi94 3c. a25136tion,8 7 r9a4263n. 07 sferd model vs. unsupervised baseline in terms of the clustering metric F1c (see section 2.3). 1195 SetupSyntaxTRANSPROJ ZEC NH Z- EFCZNRHt r a n s 657 053. 1 36456419. 372 Table5EZ C:N H- CFEZANHZRrgumeon rt ig clasif657ic1936a. t170 ion,65 9t3804ra. 20847nsferd model vs. projection baseline, accuracy. original syntactic dependencies are available. In the more realistic scenario with transferred syn- tax, however, the transferred model proves more accurate. In table 5 we compare the transferred system with the projection baseline. It is easy to see that the scores vary strongly depending on the language pair, due to both the difference in the annotation scheme used and the degree of relatedness between the languages. The drop in performance when transferring the model to another language is large in every case, though, see table 6. SetupTargetSource Table6:MoCEZdHeNZ l- FECaZNRcH urac67 y53169o. 017nthes87 o25670u. r1245ceandtrge language using original syntax. The source language scores for English vary between language pairs because of the difference in syntactic annotation and role subset used. We also include the individual F1 scores for the top-10 most frequent labels for EN-CZ transfer with original syntax in table 7. The model provides meaningful predictions here, despite low overall accuracy. Most of the labels2 are self-explanatory: Patient (PAT), Actor (ACT), Time (TWHEN), Effect (EFF), Location (LOC), Manner (MANN), Addressee (ADDR), Extent (EXT). CPHR marks the 2http://ufal.mff.cuni.cz/∼toman/pcedt/en/functors.html LabelFreq.F1Re.Pr. recall and precision for the top-10 most frequent roles. nominal part of a complex predicate, as in “to have [a plan]CPHR”, and DIR3 indicates destination. 5.3 Additional Experiments We now evaluate the contribution of different aspects of the feature representation to the performance of the model. Table 8 contains the results for English-French. FeaturesOrigTrans ferent feature subsets, using original and transferred syntactic information. The fact that the model performs slightly better with transferred syntax may be explained by two factors. Firstly, as we already mentioned, the original syntactic annotation is also produced automatically. Secondly, in the model transfer setup it is more important how closely the syntacticsemantic interface on the target side resembles that on the source side than how well it matches the “true” structure of the target language, and in this respect a transferred dependency parser may have an advantage over one trained on target-language data. The high impact of the Glos s features here 1196 may be partly attributed to the fact that the mapping is derived from the same corpus as the evaluation data Europarl (Koehn, 2005) and partly by the similarity between English and French in terms of word order, usage of articles and prepositions. The moderate contribution of the crosslingual cluster features are likely due to the insufficient granularity of the clustering for this task. For more distant language pairs, the contributions of individual feature groups are less interpretable, so we only highlight a few observations. First of all, both EN-CZ and CZ-EN benefit noticeably from the use of the original syntactic annotation, including dependency relations, but not from the transferred syntax, most likely due to the low syntactic transfer performance. Both perform better when lexical information is available, although – – the improvement is not as significant as in the case of French only up to 5%. The situation with Chinese is somewhat complicated in that adding lexical information here fails to yield an improvement in terms of the metric considered. This is likely due to the fact that we consider only the core roles, which can usually be predicted with high accuracy based on syntactic information alone. – 6 Related Work Development of robust statistical models for core NLP tasks is a challenging problem, and adaptation of existing models to new languages presents a viable alternative to exhaustive annotation for each language. Although the models thus obtained are generally imperfect, they can be further refined for a particular language and domain using techniques such as active learning (Settles, 2010; Chen et al., 2011). Cross-lingual annotation projection (Yarowsky et al., 2001) approaches have been applied ex- tensively to a variety of tasks, including POS tagging (Xi and Hwa, 2005; Das and Petrov, 2011), morphology segmentation (Snyder and Barzilay, 2008), verb classification (Merlo et al., 2002), mention detection (Zitouni and Florian, 2008), LFG parsing (Wr o´blewska and Frank, 2009), information extraction (Kim et al., 2010), SRL (Pad o´ and Lapata, 2009; van der Plas et al., 2011; Annesi and Basili, 2010; Tonelli and Pianta, 2008), dependency parsing (Naseem et al., 2012; Ganchev et al., 2009; Smith and Eisner, 2009; Hwa et al., 2005) or temporal relation prediction (Spreyer and Frank, 2008). Interestingly, it has also been used to propagate morphosyntactic information between old and modern versions of the same language (Meyer, 2011). Cross-lingual model transfer methods (McDonald et al., 2011; Zeman and Resnik, 2008; Durrett et al., 2012; Søgaard, 2011; Lopez et al., 2008) have also been receiving much attention recently. The basic idea behind model transfer is similar to that of cross-lingual annotation projection, as we can see from the way parallel data is used in, for example, McDonald et al. (201 1). A crucial component of direct transfer approaches is the unified feature representation. There are at least two such representations of lexical information (Klementiev et al., 2012; T ¨ackstr o¨m et al., 2012), but both work on word level. This makes it hard to account for phenomena that are expressed differently in the languages considered, for example the syntactic function of a certain word may be indicated by a preposition, inflection or word order, depending on the language. Accurate representation of such information would require an extra level of abstraction (Haji ˇc, 2002). A side-effect ofusing adaptation methods is that we are forced to use the same annotation scheme for the task in question (SRL, in our case), which in turn simplifies the development of cross-lingual tools for downstream tasks. Such representations are also likely to be useful in machine translation. Unsupervised semantic role labeling methods (Lang and Lapata, 2010; Lang and Lapata, 2011; Titov and Klementiev, 2012a; Lorenzo and Cerisara, 2012) also constitute an alternative to cross-lingual model transfer. For an overview of of semi-supervised approaches we refer the reader to Titov and Klementiev (2012b). 7 Conclusion We have considered the cross-lingual model transfer approach as applied to the task of semantic role labeling and observed that for closely related languages it performs comparably to annotation projection approaches. It allows one to quickly construct an SRL model for a new language without manual annotation or language-specific heuristics, provided an accurate model is available for one of the related languages along with a certain amount of parallel data for the two languages. While an1197 notation projection approaches require sentenceand word-aligned parallel data and crucially depend on the accuracy of the syntactic parsing and SRL on the source side of the parallel corpus, cross-lingual model transfer can be performed using only a bilingual dictionary. Unsupervised SRL approaches have their advantages, in particular when no annotated data is available for any of the related languages and there is a syntactic parser available for the target one, but the annotation they produce is not always sufficient. In applications such as Information Retrieval it is preferable to have precise labels, rather than just clusters of arguments, for example. Also note that when applying cross-lingual model transfer in practice, one can improve upon the performance of the simplistic model we use for evaluation, for example by picking the features manually, taking into account the properties of the target language. Domain adaptation techniques can also be employed to adjust the model to the target language. Acknowledgments The authors would like to thank Alexandre Klementiev and Ryan McDonald for useful suggestions and T ¨ackstr o¨m et al. (2012) for sharing the cross-lingual word representations. 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