Xiaojie Wang


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Learn to Adapt for Generalized Zero-Shot Text Classification
Yiwen Zhang | Caixia Yuan | Xiaojie Wang | Ziwei Bai | Yongbin Liu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationary in predicting procedures. To address these challenges, we propose a novel Learn to Adapt (LTA) network using a variant meta-learning framework. Specifically, LTA trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning (GZSL) scenario in accordance with the test time, and simultaneously learns to calibrate the class prototypes and sample representations to make the learned parameters adaptive to incoming unseen classes. We claim that the proposed model is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space. Extensive experiments on five text classification datasets show that our model outperforms several competitive previous approaches by large margins. The code and the whole datasets are available at https://github.com/Quareia/LTA.

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Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction
Hao Chen | Zepeng Zhai | Fangxiang Feng | Ruifan Li | Xiaojie Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words. Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence. After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively. Thus, relation-aware node representations can be learnt. Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model. Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly.

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A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
Sai Zhang | Yuwei Hu | Yuchuan Wu | Jiaman Wu | Yongbin Li | Jian Sun | Caixia Yuan | Xiaojie Wang
Findings of the Association for Computational Linguistics: ACL 2022

A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a common phenomenon in daily life, but little attention has been paid to it in previous work. To fill the gap, this paper defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD) and builds a Chinese dialog dataset SSD for boosting research on SSTOD. The dataset includes a total of 40K dialogs and 500K utterances from four different domains: Chinese names, phone numbers, ID numbers and license plate numbers. The data is well annotated with sub-slot values, slot values, dialog states and actions. We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task. We test three state-of-the-art dialog models on SSTOD and find they cannot handle the task well on any of the four domains. We also investigate an improved model by involving slot knowledge in a plug-in manner. More work should be done to meet the new challenges raised from SSTOD which widely exists in real-life applications. The dataset and code are publicly available via https://github.com/shunjiu/SSTOD.

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Co-VQA : Answering by Interactive Sub Question Sequence
Ruonan Wang | Yuxi Qian | Fangxiang Feng | Xiaojie Wang | Huixing Jiang
Findings of the Association for Computational Linguistics: ACL 2022

Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into a sequence of simple sub questions and finally obtain the answer to the original question after answering the sub question sequence(SQS). By simulating the process, this paper proposes a conversation-based VQA (Co-VQA) framework, which consists of three components: Questioner, Oracle, and Answerer. Questioner raises the sub questions using an extending HRED model, and Oracle answers them one-by-one. An Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed, where the question-answer pair is used to update the visual representation sequentially. To perform supervised learning for each model, we introduce a well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2 datasets. Experimental results show that our method achieves state-of-the-art on VQA-CP v2. Further analyses show that SQSs help build direct semantic connections between questions and images, provide question-adaptive variable-length reasoning chains, and with explicit interpretability as well as error traceability.


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Slot Transferability for Cross-domain Slot Filling
Hengtong Lu | Zhuoxin Han | Caixia Yuan | Xiaojie Wang | Shuyu Lei | Huixing Jiang | Wei Wu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
Junpeng Liu | Yanyan Zou | Hainan Zhang | Hongshen Chen | Zhuoye Ding | Caixia Yuan | Xiaojie Wang
Findings of the Association for Computational Linguistics: EMNLP 2021

Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progression and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via .

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Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
Duo Zheng | Zipeng Xu | Fandong Meng | Xiaojie Wang | Jiaan Wang | Jie Zhou
Findings of the Association for Computational Linguistics: EMNLP 2021

Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner needs to raise a series of questions to collect information of an undisclosed image. Despite progress has been made in Supervised Learning (SL) and Reinforcement Learning (RL), issues still exist. Firstly, previous methods do not provide explicit and effective guidance for Questioner to generate visually related and informative questions. Secondly, the effect of RL is hampered by an incompetent component, i.e., the Guesser, who makes image predictions based on the generated dialogs and assigns rewards accordingly. To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser that is strong and is optimized for VD especially. Experimental results on the VisDial v1.0 dataset show that our approach achieves state-of-the-art performance on both image-guessing task and question diversity. Human study further verifies that our model generates more visually related, informative and coherent questions.

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Grouped-Attention for Content-Selection and Content-Plan Generation
Bayu Distiawan Trisedya | Xiaojie Wang | Jianzhong Qi | Rui Zhang | Qingjun Cui
Findings of the Association for Computational Linguistics: EMNLP 2021

Content-planning is an essential part of data-to-text generation to determine the order of data mentioned in generated texts. Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input. They use LSTM to encode the input, which assumes a sequential relationship in the input. This may be sub-optimal to encode a set of attributes, where the attributes have a composite structure: the attributes are disordered while each attribute value is an ordered list of tokens. We handle this problem by proposing a neural content-planner that can capture both local and global contexts of such a structure. Specifically, we propose a novel attention mechanism called GSC-attention. A key component of the GSC-attention is grouped-attention, which is token-level attention constrained within each input attribute that enables our proposed model captures both local and global context. Moreover, our content-planner explicitly learns content-selection, which is integrated into the content-planner to select the most important data to be included in the generated text via an attention masking procedure. Experimental results show that our model outperforms the competitors by 4.92%, 4.70%, and 16.56% in terms of Damerau-Levenshtein Distance scores on three real-world datasets.

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DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation
Yuzhao Mao | Guang Liu | Xiaojie Wang | Weiguo Gao | Xuan Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4%-16% for most of the metrics on three benchmark datasets.

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Task-Oriented Clustering for Dialogues
Chenxu Lv | Hengtong Lu | Shuyu Lei | Huixing Jiang | Wei Wu | Caixia Yuan | Xiaojie Wang
Findings of the Association for Computational Linguistics: EMNLP 2021

A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.

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Multi-stage Pre-training over Simplified Multimodal Pre-training Models
Tongtong Liu | Fangxiang Feng | Xiaojie Wang
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)

Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them impossible to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT-S) which is with 45.9% parameters of the original LXMERT model and only 11.44% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.

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Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis
Ruifan Li | Hao Chen | Fangxiang Feng | Zhanyu Ma | Xiaojie Wang | Eduard Hovy
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)

Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the informal expressions and complexity of online reviews. To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously. Particularly, to alleviate dependency parsing errors, we design a SynGCN module with rich syntactic knowledge. To capture semantic correlations, we design a SemGCN module with self-attention mechanism. Furthermore, we propose orthogonal and differential regularizers to capture semantic correlations between words precisely by constraining attention scores in the SemGCN module. The orthogonal regularizer encourages the SemGCN to learn semantically correlated words with less overlap for each word. The differential regularizer encourages the SemGCN to learn semantic features that the SynGCN fails to capture. Experimental results on three public datasets show that our DualGCN model outperforms state-of-the-art methods and verify the effectiveness of our model.


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Connecting Embeddings for Knowledge Graph Entity Typing
Yu Zhao | Anxiang Zhang | Ruobing Xie | Kang Liu | Xiaojie Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/Adam1679/ConnectE .


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MrMep: Joint Extraction of Multiple Relations and Multiple Entity Pairs Based on Triplet Attention
Jiayu Chen | Caixia Yuan | Xiaojie Wang | Ziwei Bai
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

This paper focuses on how to extract multiple relational facts from unstructured text. Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs. However, these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs. In this work, we propose a novel architecture that augments the encoder and decoder in two elegant ways. First, we apply a binary CNN classifier for each relation, which identifies all possible relations maintained in the text, while retaining the target relation representation to aid entity pair recognition. Second, we perform a multi-head attention over the text and a triplet attention with the target relation interacting with every token of the text to precisely produce all possible entity pairs in a sequential manner. Experiments on three benchmark datasets show that our proposed method successfully addresses the multiple relations and multiple entity pairs even with complex overlapping and significantly outperforms the state-of-the-art methods.


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Cross-lingual Pseudo Relevance Feedback Based on Weak Relevant Topic Alignment
Xuwen Wang | Qiang Zhang | Xiaojie Wang | Junlian Li
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Response Generation in Dialogue Using a Tailored PCFG Parser
Caixia Yuan | Xiaojie Wang | Qianhui He
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)


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Normalized Accessor Variety Combined with Conditional Random Fields in Chinese Word Segmentation
Saike He | Taozheng Zhang | Xue Bai | Xiaojie Wang | Yuan Dong
Proceedings of the Student Research Workshop

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Accurate Learning for Chinese Function Tags from Minimal Features
Caixia Yuan | Fuji Ren | Xiaojie Wang
Proceedings of the ACL-IJCNLP 2009 Student Research Workshop

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Multi-Task Learning in Conditional Random Fields for Chunking in Shallow Semantic Parsing
Saike He | Xiaojie Wang | Yuan Dong | Taozheng Zhang | Xue Bai
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1


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BUPT Systems in the SIGHAN Bakeoff 2007
Ying Qin | Caixia Yuan | Jiashen Sun | Xiaojie Wang
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing


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Word Segmentation and Named Entity Recognition for SIGHAN Bakeoff3
Suxiang Zhang | Ying Qin | Juan Wen | Xiaojie Wang
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing


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Trajectory Based Word Sense Disambiguation
Xiaojie Wang | Yuji Matsumoto
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics


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Combining Segmenter and Chunker for Chinese Word Segmentation
Masayuki Asahara | Chooi Ling Goh | Xiaojie Wang | Yuji Matsumoto
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing


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A new way to conceptual meaning representation
Xiaojie Wang | Yixin Zhong
Proceedings of Machine Translation Summit VII