TY - JOUR AU - Xia, Mengzhou AU - Huang, Guoping AU - Liu, Lemao AU - Shi, Shuming PY - 2019/07/17 Y2 - 2024/03/29 TI - Graph Based Translation Memory for Neural Machine Translation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v33i01.33017297 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4716 SP - 7297-7304 AB - <p>A translation memory (TM) is proved to be helpful to improve neural machine translation (NMT). Existing approaches either pursue the decoding efficiency by merely accessing local information in a TM or encode the global information in a TM yet sacrificing efficiency due to redundancy. We propose an efficient approach to making use of the global information in a TM. The key idea is to pack a redundant TM into a compact graph and perform additional attention mechanisms over the packed graph for integrating the TM representation into the decoding network. We implement the model by extending the state-of-the-art NMT, Transformer. Extensive experiments on three language pairs show that the proposed approach is efficient in terms of running time and space occupation, and particularly it outperforms multiple strong baselines in terms of BLEU scores.</p> ER -