# Modeling Bilingual Conversational Characteristics for Neural Chat Translation

arXiv: https://arxiv.org/abs/2107.11164 · HTML: https://mlsanity.com/paper/2107.11164/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 75 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [Bridging the Gap between Training and Inference for Neural Machine Translation](https://mlsanity.com/paper/1906.02448/) (2019)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Document-Level Neural Machine Translation with Hierarchical Attention Networks](https://mlsanity.com/paper/1809.01576/) (2018)
- [Improving the Transformer Translation Model with Document-Level Context](https://mlsanity.com/paper/1810.03581/) (2018)
- [Selective Attention for Context-aware Neural Machine Translation](https://mlsanity.com/paper/1903.08788/) (2019)
- [Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation](https://mlsanity.com/paper/1808.10432/) (2018)
- [Exploiting Cross-Sentence Context for Neural Machine Translation](https://mlsanity.com/paper/1704.04347/) (2017)
- [Document Context Neural Machine Translation with Memory Networks](https://mlsanity.com/paper/1711.03688/) (2017)
- [Context-Aware Monolingual Repair for Neural Machine Translation](https://mlsanity.com/paper/1909.01383/) (2019)
- [Learning to Remember Translation History with a Continuous Cache](https://mlsanity.com/paper/1711.09367/) (2017)
- [Does Neural Machine Translation Benefit from Larger Context?](https://mlsanity.com/paper/1704.05135/) (2017)
- [Variational Neural Machine Translation](https://mlsanity.com/paper/1605.07869/) (2016)
- [Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2005.03393/) (2020)
- [Guiding Variational Response Generator to Exploit Persona](https://mlsanity.com/paper/1911.02390/) (2019)
- [Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning](https://mlsanity.com/paper/2010.04314/) (2020)
- [Variational Recurrent Neural Machine Translation](https://mlsanity.com/paper/1801.05119/) (2018)

## Built on by (most cited first, 18 of 18)

- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [Towards Making the Most of Dialogue Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2109.00668/) (2021)
- [Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation](https://mlsanity.com/paper/2305.08096/) (2023)
- [Conversational Speech Recognition By Learning Conversation-level Characteristics](https://mlsanity.com/paper/2202.07855/) (2022)
- [Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation](https://mlsanity.com/paper/2503.06594/) (2025)
- [Scheduled Multi-task Learning for Neural Chat Translation](https://mlsanity.com/paper/2205.03766/) (2022)
- [P-Transformer: Towards Better Document-to-Document Neural Machine Translation](https://mlsanity.com/paper/2212.05830/) (2022)
- [Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation](https://mlsanity.com/paper/2310.14278/) (2023)
- [A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation](https://mlsanity.com/paper/2301.11749/) (2023)
- [Chat Translation Error Detection for Assisting Cross-lingual Communications](https://mlsanity.com/paper/2308.01044/) (2023)
- [MQM-Chat: Multidimensional Quality Metrics for Chat Translation](https://mlsanity.com/paper/2408.16390/) (2024)
- [A Context-aware Framework for Translation-mediated Conversations](https://mlsanity.com/paper/2412.04205/) (2024)
- [BJTU-WeChat's Systems for the WMT22 Chat Translation Task](https://mlsanity.com/paper/2211.15009/) (2022)
- [Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues](https://mlsanity.com/paper/2311.11976/) (2023)
- [An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication](https://mlsanity.com/paper/2408.15543/) (2024)
- [Unified Model Learning for Various Neural Machine Translation](https://mlsanity.com/paper/2305.02777/) (2023)

## Abstract (arXiv)

Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT (English-German) and a self-collected bilingual dialogue corpus, named BMELD (English-Chinese). Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER. Additionally, we make the BMELD dataset publicly available for the research community.