NP Animacy Identification for Anaphora Resolution

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision

Microparametric variation in the syntax of Spanish and Greek pronominal subjects

The present research aims to investigate the interface phenomenon of third-person subject distribution in two prototypical null subject (NS) languages, Greek and Spanish focusing on Chilean Spanish, in adult monolingual speakers. The data were obtained from oral production of narratives (Study 1) and anaphora resolution (AR) (Study 2). All elicited data were submitted to statistical analyses while the production data were as well qualitatively scrutinised. Greek and Spanish were directly compared in order to discover differences between them, which were expected to emerge in the scope of the overt subject pronoun (OSP). The two languages were largely similar, sharing analogous clause structures and displaying generally similar properties on the distribution of subject forms, i.e. NS, OSP, as well as lexical subjects (LS) in oral production. The findings, confirming the predictions, showed crosslinguistic differences in the scope of OSP in topic shift (TS) between the languages due to deictic distinctions, with Greek OSP carrying deictic properties, which are less pronounced in its Spanish counterpart. This evidences the fact that NS languages may not be identical regarding subject distribution. Another key aspect, which emerged in the oral production data in both Greek and Spanish, was the felicitous use of NS in TS contexts. NS were also found to be flexible or ambiguous in AR in both languages, thereby displaying a more variable distribution than sometimes assumed.</jats:p

A constraint-based approach to noun phrase coreference resolution in German newspaper text

In this paper, we investigate the usefulness of a wide range of features for their usefulness in the resolution of nominal coreference, both as hard constraints (i.e. completely removing elements from the list of possible candidates) as well as soft constraints (where a cumulation of violations of soft constraints will make it less likely that a candidate is chosen as the antecedent). We present a state of the art system based on such constraints and weights estimated with a maximum entropy model, using lexical information to resolve cases of coreferent bridging

Linguistic Structure in Statistical Machine Translation

This thesis investigates the influence of linguistic structure in statistical machine translation. We develop a word reordering model based on syntactic parse trees and address the issues of pronouns and morphological agreement with a source discriminative word lexicon predicting the translation for individual words using structural features. When used in phrase-based machine translation, the models improve the translation for language pairs with different word order and morphological variation

Modeling contextual information in neural machine translation

Machine translation has provided impressive translation quality for many language pairs. The improvements over the past few years are largely due to the introduction of neural networks to the field, resulting in the modern sequence-to-sequence neural machine translation models. NMT is at the core of many largescale industrial tools for automatic translation such as Google Translate, Microsoft Translator, Amazon Translate and many others. Current NMT models work on the sentence-level, meaning they are used to translate individual sentences. However, for most practical use-cases, a user is interested in translating a document. In these cases, an MT tool splits a document into individual sentences and translates them independently. As a result, any dependencies between the sentences are ignored. This is likely to result in an incoherent document translation, mainly because of inconsistent translation of ambiguous source words or wrong translation of anaphoric pronouns. For example, it is undesirable to translate “bank” as a “financial bank” in one sentence and then later as a “river bank”. Furthermore, the translation of, e.g., the English third person pronoun “it” into German depends on the grammatical gender of the English antecedent’s German translation. NMT has shown that it has impressive modeling capabilities, but is nevertheless unable to model discourse-level phenomena as it needs access to contextual information. In this work, we study discourse-level phenomena in context-aware NMT. To facilitate the particular studies of interest, we propose several models capable of incorporating contextual information into standard sentence-level NMT models. We direct our focus on several discourse phenomena, namely, coreference (anaphora) resolution, coherence and cohesion. We discuss these phenomena in terms of how well can they be modeled by context-aware NMT, how can we improve upon current state-of-the-art as well as the optimal granularity at which these phenomena should be modeled. We further investigate domain as a factor in context-aware NMT. Finally, we investigate existing challenge sets for anaphora resolution evaluation and provide a robust alternative. We make the following contributions: i) We study the importance of coreference (anaphora) resolution and coherence for context-aware NMT by making use of oracle information specific to these phenomena. ii) We propose a method for improving performance on anaphora resolution based on curriculum learning which is inspired by the way humans organize learning. iii) We investigate the use of contextual information for better handling of domain information, in particular in the case of modeling multiple domains at once and when applied to zero-resource domains. iv) We present several context-aware models to enable us to examine the specific phenomena of interest we already mentioned. v) We study the optimal way of modeling local and global context and present a model theoretically capable of using very large document context. vi) We study the robustness of challenge sets for evaluation of anaphora resolution in MT by means of adversarial attacks and provide a template test set that robustly evaluates specific steps of an idealized coreference resolution pipeline for MT

Incorporating pronoun function into statistical machine translation

Pronouns are used frequently in language, and perform a range of functions. Some pronouns are used to express coreference, and others are not. Languages and genres differ in how and when they use pronouns and this poses a problem for Statistical Machine Translation (SMT) systems (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Novák, 2011; Guillou, 2012; Weiner, 2014; Hardmeier, 2014). Attention to date has focussed on coreferential (anaphoric) pronouns with NP antecedents, which when translated from English into a language with grammatical gender, must agree with the translation of the head of the antecedent. Despite growing attention to this problem, little progress has been made, and little attention has been given to other pronouns. The central claim of this thesis is that pronouns performing different functions in text should be handled differently by SMT systems and when evaluating pronoun translation. This motivates the introduction of a new framework to categorise pronouns according to their function: Anaphoric/cataphoric reference, event reference, extra-textual reference, pleonastic, addressee reference, speaker reference, generic reference, or other function. Labelling pronouns according to their function also helps to resolve instances of functional ambiguity arising from the same pronoun in the source language having multiple functions, each with different translation requirements in the target language. The categorisation framework is used in corpus annotation, corpus analysis, SMT system development and evaluation. I have directed the annotation and conducted analyses of a parallel corpus of English-German texts called ParCor (Guillou et al., 2014), in which pronouns are manually annotated according to their function. This provides a first step toward understanding the problems that SMT systems face when translating pronouns. In the thesis, I show how analysis of manual translation can prove useful in identifying and understanding systematic differences in pronoun use between two languages and can help inform the design of SMT systems. In particular, the analysis revealed that the German translations in ParCor contain more anaphoric and pleonastic pronouns than their English originals, reflecting differences in pronoun use. This raises a particular problem for the evaluation of pronoun translation. Automatic evaluation methods that rely on reference translations to assess pronoun translation, will not be able to provide an adequate evaluation when the reference translation departs from the original source-language text. I also show how analysis of the output of state-of-the-art SMT systems can reveal how well current systems perform in translating different types of pronouns and indicate where future efforts would be best directed. The analysis revealed that biases in the training data, for example arising from the use of “it” and “es” as both anaphoric and pleonastic pronouns in both English and German, is a problem that SMT systems must overcome. SMT systems also need to disambiguate the function of those pronouns with ambiguous surface forms so that each pronoun may be translated in an appropriate way. To demonstrate the value of this work, I have developed an automated post-editing system in which automated tools are used to construct ParCor-style annotations over the source-language pronouns. The annotations are then used to resolve functional ambiguity for the pronoun “it” with separate rules applied to the output of a baseline SMT system for anaphoric vs. non-anaphoric instances. The system was submitted to the DiscoMT 2015 shared task on pronoun translation for English-French. As with all other participating systems, the automatic post-editing system failed to beat a simple phrase-based baseline. A detailed analysis, including an oracle experiment in which manual annotation replaces the automated tools, was conducted to discover the causes of poor system performance. The analysis revealed that the design of the rules and their strict application to the SMT output are the biggest factors in the failure of the system. The lack of automatic evaluation metrics for pronoun translation is a limiting factor in SMT system development. To alleviate this problem, Christian Hardmeier and I have developed a testing regimen called PROTEST comprising (1) a hand-selected set of pronoun tokens categorised according to the different problems that SMT systems face and (2) an automated evaluation script. Pronoun translations can then be automatically compared against a reference translation, with mismatches referred for manual evaluation. The automatic evaluation was applied to the output of systems submitted to the DiscoMT 2015 shared task on pronoun translation. This again highlighted the weakness of the post-editing system, which performs poorly due to its focus on producing gendered pronoun translations, and its inability to distinguish between pleonastic and event reference pronouns

On L1 Attrition and Prosody in Pronominal Anaphora Resolution

This thesis is a collection of four studies on pronominal anaphora resolution with a focus on first language (L1) attrition and prosody. In Study I, we explored the temporariness of attrition effects on anaphora resolution in L1 Italian speakers who moved to Sweden after puberty (i.e., late bilinguals). An experimental group of 20 late Italian-Swedish bilinguals and a control group of 21 Italian monolinguals completed a self-paced interpretation task twice, and we measured response preferences and response times. In Study II, we investigated how L1 Italian and L1 Swedish speakers use pause features and prominence cues to resolve globally ambiguous anaphora sentences, and whether their patterns in the use of prosody mirror the divergent coreference patterns in the two languages. 28 L1 Italian speakers and 28 L1 Swedish speakers completed a speech production task, in which we analyzed the inter-clausal pause length and the pronoun’s degree of prosodic prominence, and a control interpretation task, in which we considered response preferences. Study III represents a continuation of Study II, since we examined a group of 18 late Italian-Swedish bilinguals, who completed the same experimental tasks of Study II. Study IV is a theoretical investigation, in which we discussed previous inconsistent findings on anaphora resolution in light of the interplay between hierarchical structure and linear order of a sentence. The results of the four studies suggest, first, that anaphora resolution may also affect null pronouns, and that task-learning effects should be taken into account for further research on L1 re-immersion. Second, they suggest that inter-clausal pause and prosodic prominence of pronouns are likely to break the canonical coreference pattern, both in a null subject language and in a non-null subject language. Third, the findings also reveal that L1 attrition affects prominence patterns and pause features in pronoun resolution. In particular, the longer the residence in the foreign language (FL) environment, the higher the probability that late bilinguals adapt to the FL patterns when they use prosody to resolve anaphora sentences. Fourth, both monolinguals and bilinguals are sensitive to the interplay between hierarchical structure and linear order of anaphora. However, they employ different strategies to interpret an anaphora sentence, in which hierarchical structure and linear order favor different antecedents. The implications of the findings are discussed in light of the role of processing and cross-linguistic influence (CLI) in L1 attrition, as well as in light of the use of prosodic cues to resolve an anaphoric reference, both in relation to the Null Subject Parameter and in relation to L1 attrition