Feifei Zhai


2019

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A Compact and Language-Sensitive Multilingual Translation Method
Yining Wang | Long Zhou | Jiajun Zhang | Feifei Zhai | Jingfang Xu | Chengqing Zong
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multilingual neural machine translation (Multi-NMT) with one encoder-decoder model has made remarkable progress due to its simple deployment. However, this multilingual translation paradigm does not make full use of language commonality and parameter sharing between encoder and decoder. Furthermore, this kind of paradigm cannot outperform the individual models trained on bilingual corpus in most cases. In this paper, we propose a compact and language-sensitive method for multilingual translation. To maximize parameter sharing, we first present a universal representor to replace both encoder and decoder models. To make the representor sensitive for specific languages, we further introduce language-sensitive embedding, attention, and discriminator with the ability to enhance model performance. We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot. Extensive experiments demonstrate that our proposed methods remarkably outperform strong standard multilingual translation systems on WMT and IWSLT datasets. Moreover, we find that our model is especially helpful in low-resource and zero-shot translation scenarios.

2018

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Improving the Transformer Translation Model with Document-Level Context
Jiacheng Zhang | Huanbo Luan | Maosong Sun | Feifei Zhai | Jingfang Xu | Min Zhang | Yang Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST Chinese-English datasets and the IWSLT French-English datasets show that our approach improves over Transformer significantly.

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Three Strategies to Improve One-to-Many Multilingual Translation
Yining Wang | Jiajun Zhang | Feifei Zhai | Jingfang Xu | Chengqing Zong
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Due to the benefits of model compactness, multilingual translation (including many-to-one, many-to-many and one-to-many) based on a universal encoder-decoder architecture attracts more and more attention. However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models. In this work, we introduce three strategies to improve one-to-many multilingual translation by balancing the shared and unique features. Within the architecture of one decoder for all target languages, we first exploit the use of unique initial states for different target languages. Then, we employ language-dependent positional embeddings. Finally and especially, we propose to divide the hidden cells of the decoder into shared and language-dependent ones. The extensive experiments demonstrate that our proposed methods can obtain remarkable improvements over the strong baselines. Moreover, our strategies can achieve comparable or even better performance than the individually trained translation models.

2015

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A pilot study towards end-to-end MT training
Feifei Zhai | Liang Huang
Proceedings of Machine Translation Summit XV: Papers

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Search-Aware Tuning for Hierarchical Phrase-based Decoding
Feifei Zhai | Liang Huang | Kai Zhao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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RNN-based Derivation Structure Prediction for SMT
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Handling Ambiguities of Bilingual Predicate-Argument Structures for Statistical Machine Translation
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Unsupervised Tree Induction for Tree-based Translation
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Transactions of the Association for Computational Linguistics, Volume 1

In current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture the bilingual mapping between language pairs. To train the model efficiently, we develop a Gibbs sampler with three novel Gibbs operators. The sampler is capable of exploring the infinite space of tree structures by performing local changes on the tree nodes. Experimental results show that the string-to-tree translation system using our Bayesian tree structures significantly outperforms the strong baseline string-to-tree system using parse trees.

2012

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Machine Translation by Modeling Predicate-Argument Structure Transformation
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of COLING 2012

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Tree-based Translation without using Parse Trees
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of COLING 2012

2011

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Augmenting String-to-Tree Translation Models with Fuzzy Use of Source-side Syntax
Jiajun Zhang | Feifei Zhai | Chengqing Zong
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Simple but Effective Approaches to Improving Tree-to-tree Model
Feifei Zhai | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of Machine Translation Summit XIII: Papers