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Conference Paper: Zero-resource neural machine translation with multi-agent communication game
Title | Zero-resource neural machine translation with multi-agent communication game |
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Authors | |
Keywords | NMT Zero-resource Multimodal |
Issue Date | 2018 |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) Press. |
Citation | Proceedings of the Thirty-Second Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana, USA, 2-7 February 2018, p. 5086-5093 How to Cite? |
Abstract | While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods. |
Description | Session: AAAI18 - NLP and Machine Learning |
Persistent Identifier | http://hdl.handle.net/10722/262423 |
DC Field | Value | Language |
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dc.contributor.author | Chen, Y | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Li, VOK | - |
dc.date.accessioned | 2018-09-28T04:59:06Z | - |
dc.date.available | 2018-09-28T04:59:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the Thirty-Second Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana, USA, 2-7 February 2018, p. 5086-5093 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262423 | - |
dc.description | Session: AAAI18 - NLP and Machine Learning | - |
dc.description.abstract | While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods. | - |
dc.language | eng | - |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) Press. | - |
dc.relation.ispartof | AAAI Conference on Artificial Intelligence, AAAI-18 | - |
dc.subject | NMT | - |
dc.subject | Zero-resource | - |
dc.subject | Multimodal | - |
dc.title | Zero-resource neural machine translation with multi-agent communication game | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.identifier.hkuros | 292178 | - |
dc.identifier.spage | 5086 | - |
dc.identifier.epage | 5093 | - |
dc.publisher.place | United States | - |