The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Syntax augmented machine translation via chart parsing
Open accessAbstract
We present translation results on the shared task "Exploiting Parallel Texts for Statistical Machine Translation" generated by a chart parsing decoder operating on phrase tables augmented and generalized with target language syntactic categories. We use a target language parser to generate parse trees for each sentence on the target side of the bilingual training corpus, matching them with phrase table lattices built for the corresponding source sentence. Considering phrases that correspond to syntactic categories in the parse trees we develop techniques to augment (declare a syntactically motivated category for a phrase pair) and generalize (form mixed terminal and nonterminal phrases) the phrase table into a synchronous bilingual grammar. We present results on the French-to-English task for this workshop, representing significant improvements over the workshop's baseline system. Our translation system is available open-source under the GNU General Public License.
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A decoder for syntax-based statistical MT
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Cited by
Optimizing Statistical Machine Translation for Text Simplification
Xu, W., Napoles, C., Pavlick, E., Chen, Q., & Callison-Burch, C. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00107 · Cited by 521
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., & Simaan, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1209 · Cited by 97
Metadata
- Authors
- Zollmann, A., & Venugopal, A.
- Venue
- Proceedings of the Workshop on Statistical Machine Translation - StatMT '06
- Published
- 2006
- Pages
- 138
- OpenAlex
- W2168966090
- Cited by
- 305
- References
- 10
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- bronze
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