Hao Li


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Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Lu Xu | Hao Li | Wei Lu | Lidong Bing
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness.


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Learning Explicit and Implicit Structures for Targeted Sentiment Analysis
Hao Li | Wei Lu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model’s effectiveness and robustness.

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Neural Chinese Address Parsing
Hao Li | Wei Lu | Pengjun Xie | Linlin Li
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

This paper introduces a new task – Chinese address parsing – the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.

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Reinforced Dynamic Reasoning for Conversational Question Generation
Boyuan Pan | Hao Li | Ziyu Yao | Deng Cai | Huan Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage into the general encoder-decoder framework. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multi-turn question-answering conversations for passages in SQuAD.


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Learning with Structured Representations for Negation Scope Extraction
Hao Li | Wei Lu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We report an empirical study on the task of negation scope extraction given the negation cue. Our key observation is that certain useful information such as features related to negation cue, long-distance dependencies as well as some latent structural information can be exploited for such a task. We design approaches based on conditional random fields (CRF), semi-Markov CRF, as well as latent-variable CRF models to capture such information. Extensive experiments on several standard datasets demonstrate that our approaches are able to achieve better results than existing approaches reported in the literature.


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Cross-genre Event Extraction with Knowledge Enrichment
Hao Li | Heng Ji
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Two-Stage Hashing for Fast Document Retrieval
Hao Li | Wei Liu | Heng Ji
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media
Weiwei Guo | Hao Li | Heng Ji | Mona Diab
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Combining Social Cognitive Theories with Linguistic Features for Multi-genre Sentiment Analysis
Hao Li | Yu Chen | Heng Ji | Smaranda Muresan | Dequan Zheng
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation


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Cross-lingual Slot Filling from Comparable Corpora
Matthew Snover | Xiang Li | Wen-Pin Lin | Zheng Chen | Suzanne Tamang | Mingmin Ge | Adam Lee | Qi Li | Hao Li | Sam Anzaroot | Heng Ji
Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web


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Domain-Independent Novel Event Discovery and Semi-Automatic Event Annotation
Hao Li | Xiang Li | Heng Ji | Yuval Marton
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation