It-disambiguation and source-aware language models for
            cross-lingual pronoun prediction

Loáiciga, Sharid; Guillou, Liane; Hardmeier, Christian

doi:10.18653/v1/w16-2351

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…In the context of machine translation, work by Le Nagard and Koehn (2010); Novák et al (2013); Guillou (2015) and Loáiciga et al (2016) have also considered disambiguating the function of the pronoun 'it' in the interest of improving pronoun translation into different languages.…”

Section: Related Workmentioning

confidence: 99%

What is it? Disambiguating the different readings of the pronoun ‘it’

Loáiciga¹,

Guillou²,

Hardmeier³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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In this paper, we address the problem of predicting one of three functions for the English pronoun 'it': anaphoric, event reference or pleonastic. This disambiguation is valuable in the context of machine translation and coreference resolution. We present experiments using a MAXENT classifier trained on gold-standard data and self-training experiments of an RNN trained on silver-standard data, annotated using the MAXENT classifier. Lastly, we report on an analysis of the strengths of these two models.

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Section: Related Workmentioning

confidence: 99%

What is it? Disambiguating the different readings of the pronoun ‘it’

Loáiciga¹,

Guillou²,

Hardmeier³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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“…The main contribution of the UUPPSALA-PRIMARY system (Loáiciga et al, 2016) for English-French is a Maximum Entropy classifier used to determine whether an instance of the English pronoun "it" functions as an anaphoric, pleonastic, or event reference pronoun. The classifier is trained on a combination of semantic, based on lexical resources such as VerbNet (Schuler, 2005) and WordNet (Miller, 1995), and frequencies computed over the annotated Gigaword corpus (Napoles et al, 2012), syntactic, from the dependency parser in the Mate tools (Bohnet et al, 2013), and contextual features.…”

Section: Uuppsalamentioning

confidence: 99%

Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction

Guillou¹,

Hardmeier²,

Nakov³

et al. 2016

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemmatised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the EnglishFrench subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-modelbased baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.

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“…The neural network models evaluate the context in the current and in the preceding sentence of the prediction placeholder (in the target language) and the aligned pronoun (in the source language) with a convolutional layer, followed by max-pooling and a softmax output layer. The ngram language model is identical to the sourceaware n-gram model of Hardmeier (2016) and Loáiciga et al (2016). It makes its prediction using Viterbi decoding over a standard n-gram model.…”

Section: Uu-hardmeiermentioning

confidence: 99%

Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction

Loáiciga¹,

Stymne²,

Nakov³

et al. 2017

Proceedings of the Third Workshop on Discourse in Machine Translation

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We 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.

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It-disambiguation and source-aware language models for cross-lingual pronoun prediction

Cited by 5 publications

References 13 publications

What is it? Disambiguating the different readings of the pronoun ‘it’

What is it? Disambiguating the different readings of the pronoun ‘it’

Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction

Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction

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