Multimodal machine translation and textual chat translation have received
considerable attention in recent years. Although the conversation in its
natural form is usually multimodal, there still lacks work on multimodal
machine translation in conversations. In this work, we introduce a new task
named Multimodal Chat Translation (MCT), aiming to generate more accurate
translations with the help of the associated dialogue history and visual
context. To this end, we firstly construct a Multimodal Sentiment Chat
Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs
in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in
3,079 bilingual dialogues. Each utterance pair, corresponding to the visual
context that reflects the current conversational scene, is annotated with a
sentiment label. Then, we benchmark the task by establishing multiple baseline
systems that incorporate multimodal and sentiment features for MCT. Preliminary
experiments on four language directions (English-Chinese and English-German)
verify the potential of contextual and multimodal information fusion and the
positive impact of sentiment on the MCT task. Additionally, as a by-product of
the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment
analysis. Our work can facilitate research on both multimodal chat translation
and multimodal dialogue sentiment analysis.

本研究介绍了一项名为 Multimodal Chat Translation (MCT) 的新任务，旨在通过与对话历史和视觉上下文相关联的信息来生成更准确的翻译。为此，构建了一个 Multimodal Sentiment Chat Translation Dataset (MSCTD)，并建立了多个基线系统，实现了基于情感的各种特征多模式融合。初步实验证实了人工智能上下文多模式融合和情感因素对 MCT 任务的积极影响，为多模式聊天翻译和多模式对话情感分析的研究提供了可能性。