2018
EMNLP
EMNLP 2018
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
Abstract
AbstractRecent 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.
🌱
Topic Pioneer
— Machine Translation
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
📈
Trend Setter
— Machine Translation
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Keyword Pioneer
— document translation
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Hot Topic Early Bird
— context modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
Authors
Topics
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Deep Learning > Learning Types > Representation Learning
Machine Learning > Learning Types > Machine Translation