This paper addresses the task of predicting the correct French translations of third-person subject pronouns in English discourse, a problem that is relevant as a prerequisite for machine translation and that requires anaphora resolution. We present an approach based on neural networks that models anaphoric links as latent variables and show that its performance is competitive with that of a system with separate anaphora resolution while not requiring any coreference-annotated training data. This demonstrates that the information contained in parallel bitexts can successfully be used to acquire knowledge about pronominal anaphora in an unsupervised way. When texts are translated from one language into another, the translation reconstructs the meaning or function of the source text with the means of the target language. Generally, this has the effect that the entities occurring in the translation and their mutual relations will display similar patterns as the entities in the source text. In particular, coreference patterns tend to be very similar in translations of a text, and this fact has been exploited with good results to project coreference annotations from one language into another by using word alignments (Postolache et al., 2006; Rahman and Ng, 2012) . On the other hand, what is true in general need not be true for all types of linguistic elements. For instance, a substantial percentage of the English thirdperson subject pronouns he, she, it and they does not get realised as pronouns in French translations (Hardmeier, 2012) . Moreover, it has been recognised by various authors in the statistical machine translation (SMT) community (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Guillou, 2012 ) that pronoun translation is a difficult problem because, even when a pronoun does get translated as a pronoun, it may require choosing the correct word form based on agreement features that are not easily predictable from the source text. The work presented in this paper investigates the problem of cross-lingual pronoun prediction for English-French. Given an English pronoun and its discourse context as well as a French translation of the same discourse and word alignments between the two languages, we attempt to predict the French word aligned to the English pronoun. As far as we know, this task has not been addressed in the literature before. In our opinion, it is interesting for several reasons. By studying pronoun prediction as a task in its own right, we hope to contribute towards a better understanding of pronoun translation with a longterm view to improving the performance of SMT systems. Moreover, we believe that this task can lead to interesting insights about anaphora resolution in a multi-lingual context. In particular, we show in this paper that the pronoun prediction task makes it possible to model the resolution of pronominal anaphora as a latent variable and opens up a way to solve a task relying on anaphora resolution without using any data annotated for anaphora. This is what we consider the main contribution of our present work. We start by modelling cross-lingual pronoun prediction as an independent machine learning task after doing anaphora resolution in the source language (English) using the BART software (Broscheit et al., 2010) . We show that it is difficult to achieve satisfactory performance with standard maximum-380
REF addressed the related task of cross-lingual pronoun prediction.
Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction
EMNLP
Computer Science