Improving Pronoun Translation for Statistical Machine Translation

By Liane Guillou. 2012

75 citations recorded by the source corpus.

Subfields: Natural Language Processing, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

In Machine Translation

Citations in the corpus

Toward Gender-Inclusive Coreference Resolution (2019, background): “In machine translation, producing correct gendered forms in gender languages often requires coreference to have been solved [Guillou 2012; Hardmeier and Federico 2010; Hardmeier and Guillou 2018; Mitkov 1999].”
On Integrating Discourse in Machine Translation (2017, background): “, 2014), pronoun prediction (Guillou, 2012; Hardmeier et al., 2013b; Guillou, 2016) and negation (Fancellu and Webber, 2014;”
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (2019, background): “This task is of crucial importance in various other NLP tasks, such as information extraction (Nakayama, 2019) and machine translation (Guillou, 2012).”
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction (2017, background): “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; Guillou, 2012; Hardmeier, 2014).”
Using Coreference Links to Improve Spanish-to-English Machine Translation (2017, background): “Despite the numerous coreference and anaphora resolution systems designed in the past decades (Mitkov, 2002; Ng, 2010), the interest in using them to improve pronoun translation has only recently emerged (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Guillou, 2012).”
Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction (2019, background): “Pronoun translation poses a problem for current state-of-the-art Statistical Machine Translation (SMT) systems (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Nov´ak, 2011; Guillou, 2012; Hardmeier, 2014).”
Gender Bias in Machine Translation (2021, background): “In MT, we record prior studies concerned with pronoun translation and coreference resolution across typologically different languages accounting for both animate and inanimate referents (Hardmeier and Federico, 2010; Le Nagard and Koehn, 2010; Guillou, 2012).”
Neural Machine Translation with Extended Context (2017, background): “…document-wide translation consistency, the proper handling of referential elements such as pronominal anaphora and other discourse-level phenomena (Guillou, 2012; Russo et al., 2012; Voigt and Jurafsky, 2012; Xiong et al., 2013a; Ben et al., 2013; Xiong and Zhang, 2013; Xiong et al., 2013b;…”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “In machine translation, producing correct gendered forms in gender languages often requires coreference to have been solved (Mitkov 1999; Hardmeier and Federico 2010; Guillou 2012; Hardmeier and Guillou 2018).”