Heng Yu


2022

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Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei | Heng Yu | Yue Hu | Rongxiang Weng | Weihua Luo | Rong Jin
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, and thus produce a model capable of generalizing to unseen instances. However, it is commonly observed that the generalization performance of the model is highly influenced by the amount of parallel data used in training. Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples. In this paper, we present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CsaNMT), which augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning. We conduct extensive experiments on both rich-resource and low-resource settings involving various language pairs, including WMT14 English{German,French}, NIST ChineseEnglish and multiple low-resource IWSLT translation tasks. The provided empirical evidences show that CsaNMT sets a new level of performance among existing augmentation techniques, improving on the state-of-the-art by a large margin. The core codes are contained in Appendix E.

2020

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Towards Enhancing Faithfulness for Neural Machine Translation
Rongxiang Weng | Heng Yu | Xiangpeng Wei | Weihua Luo
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.

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Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei | Heng Yu | Yue Hu | Rongxiang Weng | Luxi Xing | Weihua Luo
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other. However, the dominant methods for NMT only observe one of them from the parallel corpora for the model training but have to deal with adequate variations under the same meaning at inference. This leads to a discrepancy of the data distribution between the training and the inference phases. To address this problem, we propose uncertainty-aware semantic augmentation, which explicitly captures the universal semantic information among multiple semantically-equivalent source sentences and enhances the hidden representations with this information for better translations. Extensive experiments on various translation tasks reveal that our approach significantly outperforms the strong baselines and the existing methods.

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Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei | Heng Yu | Yue Hu | Yue Zhang | Rongxiang Weng | Weihua Luo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a MultiScale Collaborative (MSC) framework to ease the training of NMT models that are substantially deeper than those used previously. We explicitly boost the gradient back-propagation from top to bottom levels by introducing a block-scale collaboration mechanism into deep NMT models. Then, instead of forcing the whole encoder stack directly learns a desired representation, we let each encoder block learns a fine-grained representation and enhance it by encoding spatial dependencies using a context-scale collaboration. We provide empirical evidence showing that the MSC nets are easy to optimize and can obtain improvements of translation quality from considerably increased depth. On IWSLT translation tasks with three translation directions, our extremely deep models (with 72-layer encoders) surpass strong baselines by +2.2~+3.1 BLEU points. In addition, our deep MSC achieves a BLEU score of 30.56 on WMT14 English-to-German task that significantly outperforms state-of-the-art deep NMT models. We have included the source code in supplementary materials.

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Language-aware Interlingua for Multilingual Neural Machine Translation
Changfeng Zhu | Heng Yu | Shanbo Cheng | Weihua Luo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multilingual neural machine translation (NMT) has led to impressive accuracy improvements in low-resource scenarios by sharing common linguistic information across languages. However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained. In this paper, we incorporate a language-aware interlingua into the Encoder-Decoder architecture. The interlingual network enables the model to learn a language-independent representation from the semantic spaces of different languages, while still allowing for language-specific specialization of a particular language-pair. Experiments show that our proposed method achieves remarkable improvements over state-of-the-art multilingual NMT baselines and produces comparable performance with strong individual models.

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Factorized Transformer for Multi-Domain Neural Machine Translation
Yongchao Deng | Hongfei Yu | Heng Yu | Xiangyu Duan | Weihua Luo
Findings of the Association for Computational Linguistics: EMNLP 2020

Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging. To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain. We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset. Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and open-domain applications.

2019

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Code-Switching for Enhancing NMT with Pre-Specified Translation
Kai Song | Yue Zhang | Heng Yu | Weihua Luo | Kun Wang | Min Zhang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.

2016

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Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Generalized Agreement for Bidirectional Word Alignment
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
Heng Yu | Xuan Zhu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Review and analysis of China workshop on machine translation 2013 evaluation
Sitong Yang | Heng Yu | Hongmei Zhao | Qun Liu | Yajuan Lü
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

This paper gives a general review and detailed analysis of China Workshop on Machine Translation (CWMT) Evaluation. Compared with the past CWMT evaluation campaigns, CWMT2013 evaluation is characterized as follows: first, adopting gray-box evaluation which makes the results more replicable and controllable; second, adding one rule-based system as a counterpart; third, carrying out manual evaluations on some specific tasks to give a more comprehensive analysis of the translation errors. Boosted by those new features, our analysis and case study on the evaluation results shows the pros and cons of both rule-based and statistical systems, and reveals some interesting correlations bewteen automatic and manual evaluation metrics on different translation systems.

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A Structured Language Model for Incremental Tree-to-String Translation
Heng Yu | Haitao Mi | Liang Huang | Qun Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Max-Violation Perceptron and Forced Decoding for Scalable MT Training
Heng Yu | Liang Huang | Haitao Mi | Kai Zhao
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Topic-Triggered Language Model for Statistical Machine Translation
Heng Yu | Jinsong Su | Yajuan Lv | Qun Liu
Proceedings of the Sixth International Joint Conference on Natural Language Processing