Disambiguating Discourse Connectives for Statistical Machine Translation

Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards ChineseEnglish MT, in this paper we describe cross-lingual annotation and alignment of discourse connectives in a parallel corpus, describing related surveys and findings. We then conduct some evaluation experiments to testify the translation of implicit connectives and whether representing implicit connectives explicitly in source language can improve the final translation performance significantly. Preliminary results show it has little improvement by just inserting explicit connectives for implicit relations.

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“…As (Meyer et al, 2012) also argues that translation of DCs can actually be improved while BLEU scores remain similar.…”

Section: Bleu Meteormentioning

confidence: 93%

Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment

Langlais

Jin

2017

Proceedings of the Third Workshop on Discourse in Machine Translation

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“…In terms of discourse phenomena, this would mean that a source text does not contain linguistic markers that trigger some discourse relations, whereas its translation does, as was analysed by Meyer & Webber (2013) or by Becher (2011b), including also the opposite process of implicitation.…”

Section: Related Workmentioning

confidence: 99%

“…In the field of MT, some previous works have been mainly focus on DCs in European language pairs (Becher, 2011;Zufferey and Cartoni, 2014) such as English, French and German, including but not limited to disambiguating DCs in translation Meyer and PopescuBelis, 2012), labeled and implicit DCs translation (Meyer and Webber, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…and Yung et al (2015aYung et al ( , 2015b) also present some cross-lingual discourse relation analysis. But they just analyze within sentences instead of crosssentences.In the field of MT, some previous works have been mainly focus on DCs in European language pairs (Becher, 2011; Zufferey and Cartoni, 2014) such as English, French and German, including but not limited to disambiguating DCs in translation Meyer and PopescuBelis, 2012), labeled and implicit DCs translation (Meyer and Webber, 2013).As for Chinese discourse relations and translation, Tu et al (2013) employ a RST-based discourse parsing approaches in SMT, in their following work (Tu et al 2014), they also present a tree-string model on Chinese complex sentences, integrating discourse relations into MT, gaining some improvement on translation performance. argues the influence of discourse factors in translation.…”

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confidence: 99%

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Proceedings of the Third Workshop on Discourse in Machine Translation

2017

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We hope that workshops such as this one will continue to stimulate work on Discourse and Machine Translation, in a wide range of discourse phenomena and MT architectures.We would like to thank all the authors who submitted papers to the workshop, as well as all the members of the Program Committee who reviewed the submissions and delivered thoughtful, informative reviews. AbstractWe describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the targetlanguage lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document.We offered four subtasks, each for a different language pair and translation direction: English-to-French, Englishto-German, German-to-English, and Spanish-to-English.Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that all participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin. IntroductionPronoun translation poses a problem for machine translation (MT) as pronoun systems do not map well across languages, e.g., due to differences in gender, number, case, formality, or humanness, as well as because of language-specific restrictions about where pronouns may be used. For example, when translating the English it into French an MT system needs to choose between il, elle, and cela, while translating the same pronoun into German would require a choice between er, sie, and es. This is hard as selecting the correct pronoun may need discourse analysis as well as linguistic and world knowledge. Null subjects in pro-drop languages pose additional challenges as they express person and number within the verb's morphology, rendering a subject pronoun or noun phrase redundant. Thus, translating from such languages requires generating a pronoun in the target language for which there is no pronoun in the source.Pronoun translation is known to be challenging not only for MT in general, but also for Statistical Machine Translation (SMT) in particular (Le Nagard and Koehn, 2010;Hardmeier and Federico, 2010; Novák, 2011;. Phrase-based SMT (Koehn et al., 2013) was state of the art until recently, but it is gradually being replaced by Neural Machine Translation, or NMT, (Cho et al., 2014; Sutskever et al., 2014; Bahdanau et al., 2015;Luong et al., 2015). 1 NMT yields generally higher-quality translation, but is harder to analyze, and thus little is known about how well it handles pronoun translation. Yet, it is clear that it has access to larger context compa...

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“…e.g. Meyer et al, 2011, Stede, 2014or Lin et al, 2014 and may help in machine translation, information extraction, text generation and other areas.…”

Section: Introductionmentioning

confidence: 99%

CzeDLex – A Lexicon of Czech Discourse Connectives

Mírovský¹,

Synková²,

Rysová³

et al. 2017

The Prague Bulletin of Mathematical Linguistics

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CzeDLex is a new electronic lexicon of Czech discourse connectives, planned for publication by the end of this year. Its data format and structure are based on a study of similar existing resources, and adjusted to comply with the Czech syntactic tradition and specifics and with the Prague approach to the annotation of semantic discourse relations in text.In the article, we first put the lexicon in context of related resources and discuss theoretical aspects of building the lexicon -we present arguments for our choice of the data structure and for selecting features of the lexicon entries, while special attention is paid to a consistent and (as far as possible) uniform encoding of both primary (such as in English because, therefore) and secondary connectives (e.g. for this reason, this is the reason why). The main principle adopted for nesting entries in the lexicon is -apart from the lexical form of the connective -a discoursesemantic type (sense) expressed by the given connective, which enables us to deal with a broad formal variability of connectives and is convenient for interlinking CzeDLex with lexicons in other languages.Second, we introduce the chosen technical solution based on the Prague Markup Language, which allows for an efficient incorporation of the lexicon into the family of Prague treebanksit can be directly opened and edited in the tree editor TrEd, processed from the command line in btred, interlinked with its source corpus and queried in the PML Tree Query engine.Third, we describe the process of getting data for the lexicon by exploiting a large corpus manually annotated with discourse relations -the Prague Discourse Treebank 2.0: we elaborate on the automatic extraction part, post-extraction checks and manual addition of supplementary linguistic information.

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Disambiguating Discourse Connectives for Statistical Machine Translation

Cited by 70 publications

References 40 publications

Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment

Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment

Proceedings of the Third Workshop on Discourse in Machine Translation

CzeDLex – A Lexicon of Czech Discourse Connectives

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