TY - JOUR AU - Chen, Yun AU - Liu, Yang AU - Li, Victor PY - 2018/04/27 Y2 - 2024/03/29 TI - Zero-Resource Neural Machine Translation with Multi-Agent Communication Game JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Main Track: NLP and Machine Learning DO - 10.1609/aaai.v32i1.11976 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11976 SP - AB - <p> <div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>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. </span></p></div></div></div> </p> ER -