Qing Li


2021

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Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
Yun Ma | Yangbin Chen | Xudong Mao | Qing Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Unsupervised text style transfer aims to alter the underlying style of the text to a desired value while keeping its style-independent semantics, without the support of parallel training corpora. Existing methods struggle to achieve both high style conversion rate and low content loss, exhibiting the over-transfer and under-transfer problems. We attribute these problems to the conflicting driving forces of the style conversion goal and content preservation goal. In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left. In our collaborative learning mechanism, each decoder is regularized by knowledge from its peer which has a different knowledge acquisition process. The difference is guaranteed by their opposite decoding directions and a distinguishability constraint. As a result, mutual knowledge distillation drives both decoders to a better optimum and alleviates the over-transfer and under-transfer problems. Experimental results on two benchmark datasets show that our framework achieves strong empirical results on both style compatibility and content preservation.

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Exploring Non-Autoregressive Text Style Transfer
Yun Ma | Qing Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer. We first propose a base NAR model by directly adapting the common training scheme from its AutoRegressive (AR) counterpart. Despite the faster inference speed over the AR model, this NAR model sacrifices its transfer performance due to the lack of conditional dependence between output tokens. To this end, we investigate three techniques, i.e., knowledge distillation, contrastive learning, and iterative decoding, for performance enhancement. Experimental results on two benchmark datasets suggest that, although the base NAR model is generally inferior to AR decoding, their performance gap can be clearly narrowed when empowering NAR decoding with knowledge distillation, contrastive learning, and iterative decoding.

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Incorporating Global Information in Local Attention for Knowledge Representation Learning
Yu Zhao | Han Zhou | Ruobing Xie | Fuzhen Zhuang | Qing Li | Ji Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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IgSEG: Image-guided Story Ending Generation
Qingbao Huang | Chuan Huang | Linzhang Mo | Jielong Wei | Yi Cai | Ho-fung Leung | Qing Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Neural Mixed Counting Models for Dispersed Topic Discovery
Jiemin Wu | Yanghui Rao | Zusheng Zhang | Haoran Xie | Qing Li | Fu Lee Wang | Ziye Chen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.

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Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Qingbao Huang | Jielong Wei | Yi Cai | Changmeng Zheng | Junying Chen | Ho-fung Leung | Qing Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.

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A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
Xinhong Chen | Qing Li | Jianping Wang
Proceedings of the 28th International Conference on Computational Linguistics

Emotion-cause pair extraction (ECPE) aims at extracting emotions and causes as pairs from documents, where each pair contains an emotion clause and a set of cause clauses. Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up. However, the extracted emotion-cause pairs of different emotion types cannot be distinguished from each other through simple binary classifiers, which limits the applicability of the existing approaches. Moreover, such two-step approaches may suffer from possible cascading errors. In this paper, to address the first problem, we assign emotion type labels to emotion and cause clauses so that emotion-cause pairs of different emotion types can be easily distinguished. As for the second problem, we reformulate the ECPE task as a unified sequence labeling task, which can extract multiple emotion-cause pairs in an end-to-end fashion. We propose an approach composed of a convolution neural network for encoding neighboring information and two Bidirectional Long-Short Term Memory networks for two auxiliary tasks. Experiment results demonstrate the feasibility and effectiveness of our approaches.

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A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Haopeng Ren | Yi Cai | Xiaofeng Chen | Guohua Wang | Qing Li
Proceedings of the 28th International Conference on Computational Linguistics

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.

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Controllable Abstractive Sentence Summarization with Guiding Entities
Changmeng Zheng | Yi Cai | Guanjie Zhang | Qing Li
Proceedings of the 28th International Conference on Computational Linguistics

Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary. Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations. Our model can also generate more novel entities with them incorporated into outputs directly. To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives. We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics.

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Conditional Causal Relationships between Emotions and Causes in Texts
Xinhong Chen | Qing Li | Jianping Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.

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Task-oriented Domain-specific Meta-Embedding for Text Classification
Xin Wu | Yi Cai | Yang Kai | Tao Wang | Qing Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks. However, domain-specific knowledge is still ignored by existing meta-embedding methods, which results in unstable performances across specific domains. Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem. In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings. We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.

2018

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Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Qing Li | Jianlong Fu | Dongfei Yu | Tao Mei | Jiebo Luo
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In Visual Question Answering, most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image. Next, a reasoning module utilizes these explanations in place of the image to infer an answer. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some insights for the predicted answer; (2) these intermediate results can help identify the inabilities of the image understanding or the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and our system achieves comparable performance with the baselines, yet with added benefits of explanability and the inherent ability to further improve with higher quality explanations.

2017

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A Network Framework for Noisy Label Aggregation in Social Media
Xueying Zhan | Yaowei Wang | Yanghui Rao | Haoran Xie | Qing Li | Fu Lee Wang | Tak-Lam Wong
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents. To aggregate noisy labels at a small cost, a network framework is proposed by calculating the matching degree of a document’s topics and the annotators’ meta-data. Unlike using the back-propagation algorithm, a probabilistic inference approach is adopted to estimate network parameters. Finally, a new simulation method is designed for validating the effectiveness of the proposed framework in aggregating noisy labels.

2014

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Exploiting Social Relations and Sentiment for Stock Prediction
Jianfeng Si | Arjun Mukherjee | Bing Liu | Sinno Jialin Pan | Qing Li | Huayi Li
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Exploiting Topic based Twitter Sentiment for Stock Prediction
Jianfeng Si | Arjun Mukherjee | Bing Liu | Qing Li | Huayi Li | Xiaotie Deng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2010

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Recommendation in Internet Forums and Blogs
Jia Wang | Qing Li | Yuanzhu Peter Chen | Zhangxi Lin
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Effective Use of Chinese Structural Auxiliaries for Chinese Parsing
Yun Jin | Qing Li | Yingshun Wu | Young-Gil Kim
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

2006

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Concept Unification of Terms in Different Languages for IR
Qing Li | Sung-Hyon Myaeng | Yun Jin | Bo-yeong Kang
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2004

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Converting Text into Agent Animations: Assigning Gestures to Text
Yukiko I. Nakano | Masashi Okamoto | Daisuke Kawahara | Qing Li | Toyoaki Nishida
Proceedings of HLT-NAACL 2004: Short Papers

2003

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An Approach for Combining Content-based and Collaborative Filters
Qing Li | Byeong Man Kim
Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages

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Extraction of User Preferences from a Few Positive Documents
Byeong Man Kim | Qing Li | Jong Wan Kim
Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages