Towards Making the Most of Dialogue Characteristics for Neural Chat Translation (2021)

Abstract: Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English<->German and English<->Chinese) verify the effectiveness and superiority of the proposed approach.

## introduction a cross-lingual conversation involves participants that speak in different languages (e.g., one speaking in english and another in chinese as shown in fig. 1 ), where a chat translator can be applied to help participants communicate in their individual native languages. the chat translator converts the language of bilingual conversational text in both directions, e.g. from english to chinese and vice versa @xcite . with more international communication worldwide, the chat translation task becomes more important and has a wider range of applications. in recent years, although sentence-level neural machine translation (nmt) models @xcite @xcite @xcite have achieved remarkable progress and can be directly used as the chat translator, they often lead to incoherent and speakerirrelevant translations @xcite @xcite due to ignoring the chat history that contains useful contextual information. to exploit chat history, context-aware nmt models @xcite @xcite @xcite @xcite @xcite @xcite can also be directly adapted to chat translation. however, their performances are usually limited because of lacking the modeling of the inherent dialogue characteristics (e.g., the dialogue coherence and speaker personality), which matter for chat translation task as pointed out by @xcite . in this paper, we propose a coherence-speaker-aware nct (csa-nct) training framework to improve the nct model by making use of dialogue characteristics in conversations. concretely, from the perspectives of dialogue coherence and speaker personality, we design four auxiliary tasks along with the main chat translation task. for dialogue coherence, there are three tasks (two generation tasks and one discrimination task), namely monolingual response generation, cross-lingual response generation, and next utterance discrimination. specifically, as shown in fig. 1 , (1) the monolingual response generation task aims to generate the coherent corresponding utterance in target language given the dialogue history context of the same language. similarly, (2) the cross-lingual response generation task is to leverage the dialogue history context in source language to generate the coherent corresponding utterance in target language. besides the above two generation tasks, (3) the next utterance discrimination task focuses on distinguishing whether the translated text is coherent to be the next utterance of the given dialogue history context. moreover, for speaker personality, (4) we design the speaker identification task that judges whether the translated text is consistent with the personality of its original speaker. together with the main chat translation task, the nct model is optimized through the joint objectives of all these auxiliary tasks. in this way, the model is enhanced to capture dialogue coherence and speaker personality in conversation, which thus can generate more coherent and speaker-relevant translations. we validate our csa-nct framework on the datasets of different language pairs: bcon-trast (farajian et al., 2020) (en⇔de foot_0 ) and bmeld @xcite (en⇔zh foot_1 ). the experimental results show that our model achieves consistent improvements on four translation tasks in terms of both bleu @xcite and ter @xcite , demonstrating its effectiveness and generalizability. human evaluation further suggests that our model can generate more coherent and speaker-relevant translations compared to the existing related methods. our contributions are summarized as follows: the model is optimized through the following objective: context-aware nmt. as in @xcite , given a paragraph of input sentences d @xmath0, the training objective of a context-aware nmt model can be formalized as ## csa-nct training framework in this section, we introduce the proposed csa-nct training framework, which aims to improve the nct model with four elaborately designed auxiliary tasks. in the following subsections, we first describe the problem formalization ( § 3.1) and the nct model ( § 3.2). then, we introduce each auxiliary task in detail ( § 3.3). finally, we elaborate the process of training and inference ( § 3.4). ## conclusion in this paper, we propose to enhance the nct model by introducing the modeling of the inherent dialogue characteristics, i.e., dialogue coherence and speaker personality. we train the nct model with the four well-designed auxiliary tasks, i.e., mrg, crg, nud and si. experiments on en⇔de and en⇔zh show that our model notably improves translation quality on both bleu and ter metrics, showing its superiority and generalizability. human evaluation further verifies that our model yields more coherent and speaker-relevant translations.