Junhui Li

Also published as: JunHui Li


2021

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融合零指代识别的篇章级机器翻译(Context-aware Machine Translation Integrating Zero Pronoun Recognition)
Hao Wang (汪浩) | Junhui Li (李军辉) | Zhengxian Gong (贡正仙)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

在汉语等其他有省略代词习惯的语言中,通常会删掉可从上下文信息推断出的代词。尽管以Transformer为代表的的神经机器翻译模型取得了巨大的成功,但这种省略现象依旧对神经机器翻译模型造成了很大的挑战。本文在Transformer基础上提出了一个融合零指代识别的翻译模型,并引入篇章上下文来丰富指代信息。具体地,该模型采用联合学习的框架,在翻译模型基础上,联合了一个分类任务,即判别句子中省略代词在句子所表示的成分,使得模型能够融合零指代信息辅助翻译。通过在中英对话数据集上的实验,验证了本文提出方法的有效性,与基准模型相比,翻译性能提升了1.48个BLEU值。

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基于序列到序列的中文AMR解析(Chinese AMR Parsing based on Sequence-to-Sequence Modeling)
Ziyi Huang (黄子怡) | Junhui Li (李军辉) | Zhengxian Gong (贡正仙)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

抽象语义表示(Abstract Meaning Representation,简称AMR)是将给定的文本的语义特征抽象成一个单根的有向无环图。AMR语义解析则是根据输入的文本获取对应的AMR图。相比于英文AMR,中文AMR的研究起步较晚,造成针对中文的AMR语义解析相关研究较少。本文针对公开的中文AMR语料库CAMR1.0,采用序列到序列的方法进行中文AMR语义解析的相关研究。具体地,首先基于Transformer模型实现一个适用于中文的序列到序列AMR语义解析系统;然后,探索并比较了不同预训练模型在中文AMR语义解析中的应用。基于该语料,本文中文AMR语义解析方法最优性能达到了70.29的Smatch F1值。本文是第一次在该数据集上报告实验结果。

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基于字词粒度噪声数据增强的中文语法纠错(Chinese Grammatical Error Correction enhanced by Data Augmentation from Word and Character Levels)
Zecheng Tang (汤泽成) | Yixin Ji (纪一心) | Yibo Zhao (赵怡博) | Junhui Li (李军辉)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

语法纠错是自然语言处理领域的热门任务之一,其目的是将错误的句子修改为正确的句子。为了缓解中文训练语料不足的问题,本文从数据增强的角度出发,提出一种新颖的扩充和增强数据的方法。具体地,为了使模型能更好地获取不同类型和不同粒度的错误,本文首先对语法纠错中出现的错误进行了字和词粒度的分类,在此基础上提出了融合字词粒度噪声的数据增强方法,以此获得大规模且质量较高的错误数据集。基于NLPCC2018共享任务的实验结果表明,本文提出的融合字词粒度加噪方法能够显著提升模型的性能,在该数据集上达到了最优的性能。最后,本文分析了错误类型和数据规模对中文语法纠错模型性能的影响。

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Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation
Xinglin Lyu | Junhui Li | Zhengxian Gong | Min Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply “one translation per discourse” in NMT, and aim to encourage lexical translation consistency for document-level NMT. This is done by first obtaining a word link for each source word in a document, which tells the positions where the source word appears. Then we encourage the translation of those words within a link to be consistent in two ways. On the one hand, when encoding sentences within a document we properly share context information of those words. On the other hand, we propose an auxiliary loss function to better constrain that their translation should be consistent. Experimental results on Chinese↔English and English→French translation tasks show that our approach not only achieves state-of-the-art performance in BLEU scores, but also greatly improves lexical consistency in translation.

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Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection
Xincheng Ju | Dong Zhang | Rong Xiao | Junhui Li | Shoushan Li | Min Zhang | Guodong Zhou
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA). In the textual analysis, joint extracting both aspect terms and sentiment polarities has been drawn much attention due to the better applications than individual sub-task. However, in the multi-modal scenario, the existing studies are limited to handle each sub-task independently, which fails to model the innate connection between the above two objectives and ignores the better applications. Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA). Specifically, we first build an auxiliary text-image relation detection module to control the proper exploitation of visual information. Second, we adopt the hierarchical framework to bridge the multi-modal connection between MATE and MASC, as well as separately visual guiding for each sub module. Finally, we can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. Extensive experiments show the effectiveness of our approach against the joint textual approaches, pipeline and collapsed multi-modal approaches.

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XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
Dongqin Xu | Junhui Li | Muhua Zhu | Min Zhang | Guodong Zhou
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to- X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation. Specifically, we consider three types of relevant tasks, including AMR parsing, AMR-to-text generation, and machine translation. We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages. With properly pretrained models, we explore four different finetuning methods, i.e., vanilla fine-tuning with a single task, one-for-all MTL fine-tuning, targeted MTL fine-tuning, and teacher-studentbased MTL fine-tuning. Experimental results on AMR parsing and text generation of multiple non-English languages demonstrate that our approach significantly outperforms a strong baseline of pre-training approach, and greatly advances the state of the art. In detail, on LDC2020T07 we have achieved 70.45%, 71.76%, and 70.80% in Smatch F1 for AMR parsing of German, Spanish, and Italian, respectively, while for AMR-to-text generation of the languages, we have obtained 25.69, 31.36, and 28.42 in BLEU respectively. We make our code available on github https://github.com/xdqkid/XLPT-AMR.

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Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training
Linqing Chen | Junhui Li | Zhengxian Gong | Boxing Chen | Weihua Luo | Min Zhang | Guodong Zhou
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. To break the corpus bottleneck, in this paper we aim to improve context-aware NMT by taking the advantage of the availability of both large-scale sentence-level parallel dataset and source-side monolingual documents. To this end, we propose two pre-training tasks. One learns to translate a sentence from source language to target language on the sentence-level parallel dataset while the other learns to translate a document from deliberately noised to original on the monolingual documents. Importantly, the two pre-training tasks are jointly and simultaneously learned via the same model, thereafter fine-tuned on scale-limited parallel documents from both sentence-level and document-level perspectives. Experimental results on four translation tasks show that our approach significantly improves translation performance. One nice property of our approach is that the fine-tuned model can be used to translate both sentences and documents.

2020

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Improving AMR Parsing with Sequence-to-Sequence Pre-training
Dongqin Xu | Junhui Li | Muhua Zhu | Min Zhang | Guodong Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at https:// github.com/xdqkid/S2S-AMR-Parser.

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Multi-modal Multi-label Emotion Detection with Modality and Label Dependence
Dong Zhang | Xincheng Ju | Junhui Li | Shoushan Li | Qiaoming Zhu | Guodong Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.

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融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax)
Jie Zhu (朱杰) | Junhui Li (李军辉)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

抽象语义表示到文本(AMR-to-Text)生成的任务是给定AMR图,生成相同语义表示的文本。可以把此任务当作一个从源端AMR图到目标端句子的机器翻译任务。目前存在的一些方法都在探索如何更好的对图结构进行建模。然而,它们都存在一个未限定的问题,因为在生成阶段许多句法的决策并不受语义图的约束,从而忽略了句子内部潜藏的句法信息。为了明确考虑这一不足,该文提出一种直接而有效的方法,显示的在AMR-to-Text生成的任务中融入句法信息,并在Transformer和目前该任务最优性能的模型上进行了实验。实验结果表明,在现存的两份标准英文数据集LDC2018E86和LDC2017T10上,都取得了显著的提升,达到了新的最高性能。

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层次化结构全局上下文增强的篇章级神经机器翻译(Hierarchical Global Context Augmented Document-level Neural Machine Translation)
Linqing Chen (陈林卿) | Junhui Li (李军辉) | Zhengxian Gong (贡正仙)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

如何有效利用篇章上下文信息一直是篇章级神经机器翻译研究领域的一大挑战。本文提出利用来源于整个篇章的层次化全局上下文提高篇章级神经机器翻译性能。为了实现该目标,本文模型分别获取当前句内单词与篇章内所有句子及单词之间的依赖关系,结合不同层次的依赖关系以获取含有层次化篇章信息的全局上下文。最终源语言当前句子中的每个单词都能获取其独有的综合词和句级别依赖关系的上下文。为了充分利用平行句对语料在训练中的优势本文使用两步训练法,在句子级语料训练模型的基础上使用含有篇章信息的语料进行二次训练以获得捕获全局上下文的能力。在若干基准语料数据集上的实验表明本文提出的模型与若干强基准模型相比取得了有意义的翻译质量提升。实验进一步表明,结合层次化篇章信息的上下文比仅使用词级别上下文更具优势。除此之外,本文尝试通过不同方式将全局上下文与翻译模型结合并观察其对模型性能的影响,并初步探究篇章翻译中全局上下文在篇章中的分布情况。

2019

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A Discrete CVAE for Response Generation on Short-Text Conversation
Jun Gao | Wei Bi | Xiaojiang Liu | Junhui Li | Guodong Zhou | Shuming Shi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled latent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses.

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Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu | Junhui Li | Muhua Zhu | Longhua Qian | Min Zhang | Guodong Zhou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.

2018

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Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
Shaohui Kuang | Junhui Li | António Branco | Weihua Luo | Deyi Xiong
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language. In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others. Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.

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Adaptive Weighting for Neural Machine Translation
Yachao Li | Junhui Li | Min Zhang
Proceedings of the 27th International Conference on Computational Linguistics

In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output. However, the weights in a WSM are independent of each other and fixed for all inputs, suggesting that by ignoring different needs of inputs, the WSM lacks effective control on the influence of each input. In this paper, we propose adaptive weighting for WSMs to control the contribution of each input. Specifically, we apply adaptive weighting for both GRU and the output state in NMT. Experimentation on Chinese-to-English translation and English-to-German translation demonstrates that the proposed adaptive weighting is able to much improve translation accuracy by achieving significant improvement of 1.49 and 0.92 BLEU points for the two translation tasks. Moreover, we discuss in-depth on what type of information is encoded in the encoder and how information influences the generation of target words in the decoder.

2017

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Modeling Source Syntax for Neural Machine Translation
Junhui Li | Deyi Xiong | Zhaopeng Tu | Muhua Zhu | Min Zhang | Guodong Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.

2016

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SoNLP-DP System for ConLL-2016 English Shallow Discourse Parsing
Fang Kong | Sheng Li | Junhui Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

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SoNLP-DP System for ConLL-2016 Chinese Shallow Discourse Parsing
Junhui Li | Fang Kong | Sheng Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

2015

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Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar
Junhui Li | Muhua Zhu | Wei Lu | Guodong Zhou
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation
Junhui Li | Yuval Marton | Philip Resnik | Hal Daumé III
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Modeling Syntactic and Semantic Structures in Hierarchical Phrase-based Translation
Junhui Li | Philip Resnik | Hal Daumé III
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Using Syntactic Head Information in Hierarchical Phrase-Based Translation
Junhui Li | Zhaopeng Tu | Guodong Zhou | Josef van Genabith
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Head-Driven Hierarchical Phrase-based Translation
Junhui Li | Zhaopeng Tu | Guodong Zhou | Josef van Genabith
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2010

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Learning the Scope of Negation via Shallow Semantic Parsing
Junhui Li | Guodong Zhou | Hongling Wang | Qiaoming Zhu
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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A Unified Framework for Scope Learning via Simplified Shallow Semantic Parsing
Qiaoming Zhu | Junhui Li | Hongling Wang | Guodong Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Joint Syntactic and Semantic Parsing of Chinese
Junhui Li | Guodong Zhou | Hwee Tou Ng
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Improving Nominal SRL in Chinese Language with Verbal SRL Information and Automatic Predicate Recognition
Junhui Li | Guodong Zhou | Hai Zhao | Qiaoming Zhu | Peide Qian
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Semi-Supervised Learning for Relation Extraction
GuoDong Zhou | JunHui Li | LongHua Qian | QiaoMing Zhu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I