Yong Guan


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Trigger-Argument based Explanation for Event Detection
Yong Guan | Jiaoyan Chen | Freddy Lecue | Jeff Pan | Juanzi Li | Ru Li
Findings of the Association for Computational Linguistics: ACL 2023

Event Detection (ED) is a critical task that aims to identify events of certain types in plain text. Neural models have achieved great success on ED, thus coming with a desire for higher interpretability. Existing works mainly exploit words or phrases of the input text to explain models’ inner mechanisms. However, for ED, the event structure, comprising of an event trigger and a set of arguments, are more enlightening clues to explain model behaviors. To this end, we propose a Trigger-Argument based Explanation method (TAE), which can utilize event structure knowledge to uncover a faithful interpretation for the existing ED models at neuron level. Specifically, we design group, sparsity, support mechanisms to construct the event structure from structuralization, compactness, and faithfulness perspectives. We evaluate our model on the large-scale MAVEN and the widely-used ACE 2005 datasets, and observe that TAE is able to reveal the process by which the model predicts. Experimental results also demonstrate that TAE can not only improve the interpretability on standard evaluation metrics, but also effectively facilitate the human understanding.


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Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Yong Guan | Shaoru Guo | Ru Li | Xiaoli Li | Hu Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.

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Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
Yong Guan | Shaoru Guo | Ru Li | Xiaoli Li | Hongye Tan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.


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多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)
Yu Ji (吉宇) | Xiaoyue Wang (王笑月) | Ru Li (李茹) | Shaoru Guo (郭少茹) | Yong Guan (关勇)
Proceedings of the 19th Chinese National Conference on Computational Linguistics


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A Frame-based Sentence Representation for Machine Reading Comprehension
Shaoru Guo | Ru Li | Hongye Tan | Xiaoli Li | Yong Guan | Hongyan Zhao | Yueping Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

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Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Shaoru Guo | Yong Guan | Ru Li | Xiaoli Li | Hongye Tan
Proceedings of the 28th International Conference on Computational Linguistics

Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.


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Personalising Speech-To-Speech Translation in the EMIME Project
Mikko Kurimo | William Byrne | John Dines | Philip N. Garner | Matthew Gibson | Yong Guan | Teemu Hirsimäki | Reima Karhila | Simon King | Hui Liang | Keiichiro Oura | Lakshmi Saheer | Matt Shannon | Sayaki Shiota | Jilei Tian
Proceedings of the ACL 2010 System Demonstrations