Juan Liu


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A tunable language model for statistical machine translation
Junfei Guo | Juan Liu | Qi Han | Andreas Maletti
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

A novel variation of modified KNESER-NEY model using monomial discounting is presented and integrated into the MOSES statistical machine translation toolkit. The language model is trained on a large training set as usual, but its new discount parameters are tuned to the small development set. An in-domain and cross-domain evaluation of the language model is performed based on perplexity, in which sizable improvements are obtained. Additionally, the performance of the language model is also evaluated in several major machine translation tasks including Chinese-to-English. In those tests, the test data is from a (slightly) different domain than the training data. The experimental results indicate that the new model significantly outperforms a baseline model using SRILM in those domain adaptation scenarios. The new language model is thus ideally suited for domain adaptation without sacrificing performance on in-domain experiments.


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Deploying MT into a Localisation Workflow: Pains and Gains
Yanli Sun | Juan Liu | Yi Li
Proceedings of Machine Translation Summit XIII: Papers

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Combining ConceptNet and WordNet for Word Sense Disambiguation
Junpeng Chen | Juan Liu
Proceedings of 5th International Joint Conference on Natural Language Processing

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Question classification based on an extended class sequential rule model
Zijing Hui | Juan Liu | Lumei Ouyang
Proceedings of 5th International Joint Conference on Natural Language Processing


Mining Chinese-English Parallel Corpora from the Web
Bo Li | Juan Liu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II


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Mining Parallel Text from the Web based on Sentence Alignment
Bo Li | Juan Liu | Huili Zhu
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation