Boli Wang


2018

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XMU Neural Machine Translation Systems for WAT2018 Myanmar-English Translation Task
Boli Wang | Jinming Hu | Yidong Chen | Xiaodong Shi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

2017

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Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
Changxing Wu | Xiaodong Shi | Yidong Chen | Jinsong Su | Boli Wang
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over baselines.

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XMU Neural Machine Translation Online Service
Boli Wang | Zhixing Tan | Jinming Hu | Yidong Chen | Xiaodong Shi
Proceedings of the IJCNLP 2017, System Demonstrations

We demonstrate a neural machine translation web service. Our NMT service provides web-based translation interfaces for a variety of language pairs. We describe the architecture of NMT runtime pipeline and the training details of NMT models. We also show several applications of our online translation interfaces.

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XMU Neural Machine Translation Systems for WMT 17
Zhixing Tan | Boli Wang | Jinming Hu | Yidong Chen | Xiaodong Shi
Proceedings of the Second Conference on Machine Translation

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XMU Neural Machine Translation Systems for WAT 2017
Boli Wang | Zhixing Tan | Jinming Hu | Yidong Chen | Xiaodong Shi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

This paper describes the Neural Machine Translation systems of Xiamen University for the shared translation tasks of WAT 2017. Our systems are based on the Encoder-Decoder framework with attention. We participated in three subtasks. We experimented subword segmentation, synthetic training data and model ensembling. Experiments show that all these methods can give substantial improvements.