| W15-5003 | translation errors were fixed by neural | MT reranking | . Based on this analysis , we |
| N04-1023 | function in our experiments on | MT reranking | . 5 Experiments and Analysis |
| N04-1023 | algorithm specially designed for | MT reranking | , which has similarities to a |
| W15-5003 | described results applying neural | MT reranking | to a baseline syntax-based machine |
| W15-5003 | terminology . In fact , neural | MT reranking | had an overall negative effect |
| W15-5003 | systems with and without the neural | MT reranking | model . As automatic evaluation |
| N04-1023 | ordinal regression algorithms to | MT reranking | . In the previous works on ranking |
| N04-1023 | perceptron-like algorithms for | MT reranking | . The first one is a splitting |
| N04-1023 | ranks on the entire data . But in | MT reranking | , ranks are defined over every |
| W10-1757 | agnostic to the application . In | MT reranking | , the goal is to translate a |
| W15-5003 | degradations afforded by neural | MT reranking | is shown in Table 2 . From this |
| N04-1023 | paper , we apply this algorithm to | MT reranking | , and we also introduce a new |
| N13-1002 | Papineni et al. , 2002 ) . 6.3 | MT reranking | experiments We first report detailed |
| W15-5003 | found that , within the neural | MT reranking | framework , accuracy gains scaled |
| N04-1023 | algorithm that we will use for | MT reranking | is the - insensitive ordinal |
| W15-5003 | improved or degraded due to neural | MT reranking | , and identify major areas in |
| W15-5003 | neural component ) . While neural | MT reranking | has been noted to improve traditional |
| K15-1031 | as the depth increases .5 3.3 | MT Reranking | with NLMs Our MT models are built |
| W15-5003 | it is safe to say that neural | MT reranking | can be expected to have a large |
| W15-5003 | previous work stating that neural | MT reranking | provides a large gain in objective |
