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Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations

arXiv 1809.00344 ↗First posted 2 Sep 2018cs.CLcited by 15

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Abstract (arXiv)

Recent works in neural machine translation have begun to explore document translation. However, translating online multi-speaker conversations is still an open problem. In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task. To initiate an evaluation for this task, we introduce datasets extracted from Europarl v7 and OpenSubtitles2016. Our experiments on four language-pairs confirm the significance of leveraging conversation history, both in terms of BLEU and manual evaluation.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Sep 2018.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

15 papers.

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