Name
Title | ||
|---|---|---|
Hybrid Neural Network Alignment And Lexicon Model In Direct Hmm For Statistical Machine Translation |
Abstract | ||
|---|---|---|
Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct hidden Markov model (HMM) with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0% BLEU scores on two different translation tasks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-2020 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2 |
Field | DocType | Volume |
Computer science,Machine translation,Speech recognition,Hybrid neural network,Lexicon,Natural language processing,Artificial intelligence,Hidden Markov model | Conference | P17-2 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiyue Wang | 1 | 8 | 5.19 |
| Tamer Alkhouli | 2 | 75 | 7.56 |
| Derui Zhu | 3 | 0 | 0.34 |
| Hermann Ney | 4 | 14178 | 1506.93 |