Huanbo Luan


2020

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THUMT: An Open-Source Toolkit for Neural Machine Translation
Zhixing Tan | Jiacheng Zhang | Xuancheng Huang | Gang Chen | Shuo Wang | Maosong Sun | Huanbo Luan | Yang Liu
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2019

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Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang | Yang Liu | Chao Wang | Huanbo Luan | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation.

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Learning to Copy for Automatic Post-Editing
Xuancheng Huang | Yang Liu | Huanbo Luan | Jingfang Xu | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied. Finally, CopyNet (Gu et.al., 2016) can be combined with our method to place the copied words in correct positions in post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.

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.

2017

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Visualizing and Understanding Neural Machine Translation
Yanzhuo Ding | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.

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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Jiacheng Zhang | Yang Liu | Huanbo Luan | Jingfang Xu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model. Experiments on Chinese-English dataset show that our approach leads to significant improvements.

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Adversarial Training for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.

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Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover’s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover’s distance shows potential as a measure of language difference.

2016

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Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
Meng Zhang | Yang Liu | Huanbo Luan | Yiqun Liu | Maosong Sun
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover’s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon.

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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)

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Neural Relation Extraction with Selective Attention over Instances
Yankai Lin | Shiqi Shen | Zhiyuan Liu | Huanbo Luan | Maosong Sun
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin | Zhiyuan Liu | Huanbo Luan | Maosong Sun | Siwei Rao | Song Liu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Consistency-Aware Search for Word Alignment
Shiqi Shen | Yang Liu | Maosong Sun | Huanbo Luan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Online Learning of Interpretable Word Embeddings
Hongyin Luo | Zhiyuan Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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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