Meng Liao


2022

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ECO v1: Towards Event-Centric Opinion Mining
Ruoxi Xu | Hongyu Lin | Meng Liao | Xianpei Han | Jin Xu | Wei Tan | Yingfei Sun | Le Sun
Findings of the Association for Computational Linguistics: ACL 2022

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.

2021

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From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Jialong Tang | Hongyu Lin | Meng Liao | Yaojie Lu | Xianpei Han | Le Sun | Weijian Xie | Jin Xu
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)

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance and extrinsic experimental results verify the value of the extracted event relations.

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Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Boxi Cao | Hongyu Lin | Xianpei Han | Le Sun | Lingyong Yan | Meng Liao | Tong Xue | Jin Xu
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)

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.

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Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Yaojie Lu | Hongyu Lin | Jin Xu | Xianpei Han | Jialong Tang | Annan Li | Le Sun | Meng Liao | Shaoyi Chen
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)

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

2012

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A Mixed Deterministic Model for Coreference Resolution
Bo Yuan | Qingcai Chen | Yang Xiang | Xiaolong Wang | Liping Ge | Zengjian Liu | Meng Liao | Xianbo Si
Joint Conference on EMNLP and CoNLL - Shared Task