Liang Yao


2020

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Domain Transfer based Data Augmentation for Neural Query Translation
Liang Yao | Baosong Yang | Haibo Zhang | Boxing Chen | Weihua Luo
Proceedings of the 28th International Conference on Computational Linguistics

Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries. Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model. In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples. Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model. In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking. Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy.

2018

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Alibaba’s Neural Machine Translation Systems for WMT18
Yongchao Deng | Shanbo Cheng | Jun Lu | Kai Song | Jingang Wang | Shenglan Wu | Liang Yao | Guchun Zhang | Haibo Zhang | Pei Zhang | Changfeng Zhu | Boxing Chen
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submission systems of Alibaba for WMT18 shared news translation task. We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese. Our systems are based on Google’s Transformer model architecture, into which we integrated the most recent features from the academic research. We also employed most techniques that have been proven effective during the past WMT years, such as BPE, back translation, data selection, model ensembling and reranking, at industrial scale. For some morphologically-rich languages, we also incorporated linguistic knowledge into our neural network. For the translation tasks in which we have participated, our resulting systems achieved the best case sensitive BLEU score in all 5 directions. Notably, our English → Russian system outperformed the second reranked system by 5 BLEU score.

2016

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Image-Image Search for Comparable Corpora Construction
Yu Hong | Liang Yao | Mengyi Liu | Tongtao Zhang | Wenxuan Zhou | Jianmin Yao | Heng Ji
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We present a novel method of comparable corpora construction. Unlike the traditional methods which heavily rely on linguistic features, our method only takes image similarity into consid-eration. We use an image-image search engine to obtain similar images, together with the cap-tions in source language and target language. On the basis, we utilize captions of similar imag-es to construct sentence-level bilingual corpora. Experiments on 10,371 target captions show that our method achieves a precision of 0.85 in the top search results.

2015

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Short Text Understanding by Leveraging Knowledge into Topic Model
Shansong Yang | Weiming Lu | Dezhi Yang | Liang Yao | Baogang Wei
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies