Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns

Rutherford, Attapol T.; Xue, Nianwen

doi:10.3115/v1/e14-1068

Cited by 98 publications

(107 citation statements)

References 19 publications

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“…Discourse parsing is a semantic task, at least when it comes to predict a rhetorical relation between two spans of text, and information from words have proven to be crucial (Rutherford and Xue, 2014;Braud and Denis, 2015). We thus include word features using bilingual dictionaries -i.e.…”

Section: Cross-lingual Discourse Parsingmentioning

confidence: 99%

Cross-lingual RST Discourse Parsing

Braud¹,

Coavoux²,

Søgaard³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on crosslingual discourse parsing.

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Section: Cross-lingual Discourse Parsingmentioning

confidence: 99%

Cross-lingual RST Discourse Parsing

Braud¹,

Coavoux²,

Søgaard³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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“…Our approach is most similar to the one by Rutherford and Xue (2014), who successfully integrate distributional representations to substitute word-pair features.…”

Section: Implicit Relation Classificationmentioning

confidence: 96%

A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition

Chiarcos

Schenk

2015

Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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We describe a minimalist approach to shallow discourse parsing in the context of the CoNLL 2015 Shared Task. 1 Our parser integrates a rule-based component for argument identification and datadriven models for the classification of explicit and implicit relations. We place special emphasis on the evaluation of implicit sense labeling, we present different feature sets and show that (i) word embeddings are competitive with traditional word-level features, and (ii) that they can be used to considerably reduce the total number of features. Despite its simplicity, our parser is competitive with other systems in terms of sense recognition and thus provides a solid ground for further refinement.

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“…Some research (Li et al 2014, Rutherford andXue, 2014) has done on monolingual annotation and analysis of Chinese DCs. Li et al (2014a) and Yung et al (2015aYung et al ( , 2015b) also present some cross-lingual discourse relation analysis.…”

Section: Related Workmentioning

confidence: 99%

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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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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Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns

Cited by 98 publications

References 19 publications

Cross-lingual RST Discourse Parsing

Cross-lingual RST Discourse Parsing

A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition

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

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