Name
Abstract | ||
|---|---|---|
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if the sentence is ambiguous. When translating a text, humans often create an initial understanding of the source sentence and then incrementally refine it along the translation on the target side. Starting from this intuition, we propose a novel encoder-refiner-decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a strategy, leveraging the power of reinforcement learning models, to decide when to refine at specific decoding steps. Experimental results on both Chinese-English and English-German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder-decoder framework. Furthermore, when refining strategy is applied, results still show reasonable improvement over the baseline without much decrease in decoding speed. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1812.10230 | 0 | 0.34 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| xinwei geng | 1 | 0 | 1.01 |
| Longyue Wang | 2 | 72 | 18.24 |
| Xing Wang | 3 | 58 | 10.07 |
| Bing Qin | 4 | 1076 | 72.82 |
| Ting Liu | 5 | 2735 | 232.31 |
| Zhaopeng Tu | 6 | 518 | 39.95 |