Modelling pronominal anaphora in statistical machine translation

By Christian Hardmeier, Marcello Federico. 2010

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In Natural Language Processing

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].”
Context-Aware Neural Machine Translation Learns Anaphora Resolution (2018, background): “Earlier research on this topic focused on handling speciﬁc phenomena, such as translating pro-nouns (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Hardmeier et al., 2015), discourse connectives (Meyer et al., 2012), verb tense (Gong et al., 2012), increasing lexical consistency (Carpuat,…”
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender (2021, background): “…measuring performance for unambiguous examples, it does not focused on the NMT use case, and its examples require cross-sentential coreferences, which will likely require a more complex linguistic toolbox than our intrasentential case (Szabolcsi, 2003; Hardmeier and Federico, 2010; Reinhart, 2016).”
Pronoun Translation in English-French Machine Translation: An Analysis of Error Types (2018, background): “Gender agreement was long assumed to be the most important problem that needed to be addressed to solve the issue of pronoun translation [1, 30].”
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments (2018, background): “Pronominal coreference relates to the issue of translating anaphoric pronouns and is tackled in several works (Guillou, 2016; Hardmeier and Federico, 2010; Le Nagard and Koehn, 2010) and is the central motivation for the DiscoMT shared task on cross-lingual pronoun prediction (Loáiciga et al.”
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point (2018, methodology): “Attempting to create a similar framework for efﬁcient research, researchers have proposed automatic reference-based evaluation metrics speciﬁcally targeting pro-noun translation (Hardmeier and Federico, 2010; Werlen and Popescu-Belis, 2017).”
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).”
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2021, unknown): “Hardmeier and Federico (2010)”
Neural Machine Translation with Extended Context (2017, background): “Modest improvements could, for example, be shown for the translation of pronouns (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Hardmeier, 2014) and the generation of appropriate discourse connectives (Meyer et al.”
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).”
Improving Pronoun Translation for Statistical Machine Translation (2012, methodology): “This would not be an issue for single-step translation systems such as that used by Hardmeier & Federico (2010).”
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).”