Liqiang Nie


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MMCoQA: Conversational Question Answering over Text, Tables, and Images
Yongqi Li | Wenjie Li | Liqiang Nie
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The rapid development of conversational assistants accelerates the study on conversational question answering (QA). However, the existing conversational QA systems usually answer users’ questions with a single knowledge source, e.g., paragraphs or a knowledge graph, but overlook the important visual cues, let alone multiple knowledge sources of different modalities. In this paper, we hence define a novel research task, i.e., multimodal conversational question answering (MMCoQA), aiming to answer users’ questions with multimodal knowledge sources via multi-turn conversations. This new task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge. To facilitate the data-driven approaches in this area, we construct the first multimodal conversational QA dataset, named MMConvQA. Questions are fully annotated with not only natural language answers but also the corresponding evidence and valuable decontextualized self-contained questions. Meanwhile, we introduce an end-to-end baseline model, which divides this complex research task into question understanding, multi-modal evidence retrieval, and answer extraction. Moreover, we report a set of benchmarking results, and the results indicate that there is ample room for improvement.

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MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning
Fangkai Jiao | Yangyang Guo | Xuemeng Song | Liqiang Nie
Findings of the Association for Computational Linguistics: ACL 2022

Logical reasoning is of vital importance to natural language understanding. Previous studies either employ graph-based models to incorporate prior knowledge about logical relations, or introduce symbolic logic into neural models through data augmentation. These methods, however, heavily depend on annotated training data, and thus suffer from over-fitting and poor generalization problems due to the dataset sparsity. To address these two problems, in this paper, we propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text, to perform self-supervised pre-training on abundant unlabeled text data. Two novel strategies serve as indispensable components of our method. In particular, a strategy based on meta-path is devised to discover the logical structure in natural texts, followed by a counterfactual data augmentation strategy to eliminate the information shortcut induced by pre-training. The experimental results on two challenging logical reasoning benchmarks, i.e., ReClor and LogiQA, demonstrate that our method outperforms the SOTA baselines with significant improvements.


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REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Fangkai Jiao | Yangyang Guo | Yilin Niu | Feng Ji | Feng-Lin Li | Liqiang Nie
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Improving Distantly-Supervised Relation Extraction with Joint Label Embedding
Linmei Hu | Luhao Zhang | Chuan Shi | Liqiang Nie | Weili Guan | Cheng Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Distantly-supervised relation extraction has proven to be effective to find relational facts from texts. However, the existing approaches treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. In this paper, we propose a novel multi-layer attention-based model to improve relation extraction with joint label embedding. The model makes full use of both structural information from Knowledge Graphs and textual information from entity descriptions to learn label embeddings through gating integration while avoiding the imposed noise with an attention mechanism. Then the learned label embeddings are used as another atten- tion over the instances (whose embeddings are also enhanced with the entity descriptions) for improving relation extraction. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods.


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Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
Yansen Wang | Chenyi Liu | Minlie Huang | Liqiang Nie
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.


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A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
Yan Chen | Zhoujun Li | Liqiang Nie | Xia Hu | Xiangyu Wang | Tat-Seng Chua | Xiaoming Zhang
Proceedings of COLING 2012

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The Use of Dependency Relation Graph to Enhance the Term Weighting in Question Retrieval
Weinan Zhang | Zhaoyan Ming | Yu Zhang | Liqiang Nie | Ting Liu | Tat-Seng Chua
Proceedings of COLING 2012