Zero-pronoun Data Augmentation for J apanese-to- E nglish Translation (2021)

Abstract: For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.

## introduction while neural machine translation (nmt) has demonstrated high performance in single-sentence translation, it is still challenging to handle linguistic phenomena involving discourse contexts. one such issue is the translation of zero pronouns (zp) in japanese-to-english translation. in japanese, subjects and objects are often omitted when the listener can infer them from the context. however, when translating them into english, the omitted words must be explicitly translated in most cases. for example, in the following sentence, the subject omitted in japanese is the first person, and i has to be output in english. the prediction of zps, essentially, requires understanding the topic and old information in the discourse, or referring to the world knowledge. on the other hand, linguistic information within the sentence may provide some clues @xcite . for example, in the sentence above, the auxiliary verb たい (want) suggests that the sentence expresses a subjective statement and thus the missing pronoun is the first person. here we refer to such information as local context. correlations between local context and zps can be learned by the standard single-sentence neural machine translation, but it may not be possible under low-resource conditions. for example, the translation of conversations, which usually contain a large number of zps, is currently one of the underresourced domains. to address this problem, we propose zero pronoun data augmentation to facilitate learning correlations between local context and zps (figure 1 ). we augment the training data by deleting personal pronouns in the source japanese sentence. this creates parallel data that include zps and provides additional training signals to learn to predict zps. our method is simple yet effective: it does not require any modification to the model architecture nor additional computation at inference time, but significantly improves the accuracy of the zp translation. ## is local context useful for predicting zero pronouns? our proposed method is based on the assumption that local context in japanese sentences is useful for predicting zps. we begin by analyzing to what extent zps can be inferred from local context, and what kind of local context is useful. for the analysis, we use the business scene dialogue corpus @xcite , which is a japanese and english parallel corpus in the conversational domain. besides the published data, we also use the in-house version of the corpus, which amounts to a total of 104,961 sentence pairs. ## zp data augmentation in the previous section, we confirmed that local context is useful for predicting zps. in this section, we examine the usefulness of zp data augmentation for machine translation. the method artificially creates training data containing zps by deleting pronouns in the source japanese sentence along with the following particles. the pronouns to be deleted are detected by string matching with manually created lists (appendix a). the augmented data is supposed to provide useful training signals for learning correlations between zps and local context. ## conclusion to address the problem of zero pronoun translation, we proposed zero pronoun data augmentation. through the analysis with the japanese-english conversational parallel corpus, we showed that zero pronouns in japanese sentences can be predicted to some extent from local context within the sentence. in the conversational translation experiment, we compared a translation model trained on the augmented data with the baseline and demonstrate that our method significantly improves the accuracy of zero pronoun translation. nevertheless, zero pronoun data augmentation does not solve the cases where the information necessary for zero pronoun translation exists outside the sentence. also, the analysis suggests that local context is useful for predicting frequent pronouns such as the first and second-person pronouns, but not for the third-person pronouns. an interesting avenue for future work is to explicitly incorporate discourse-level contextual information such as topics or people involved in the conversation into the translation models.