Ru Li


2022

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基于GCN和门机制的汉语框架排歧方法(Chinese Frame Disambiguation Method Based on GCN and Gate Mechanism)
Yanan You (游亚男) | Ru Li (李茹) | Xuefeng Su (苏雪峰) | Zhichao Yan (闫智超) | Minshuai Sun (孙民帅) | Chao Wang (王超)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“汉语框架排歧旨在候选框架中给句子中的目标词选择一个符合其语义场景的框架。目前研究方法存在隐层向量的计算与目标词无关,并且忽略了句法结构信息对框架排歧的影响等缺陷。针对上述问题,使用GCN对句法结构信息进行建模;引入门机制过滤隐层向量中与目标词无关的噪声信息;并在此基础上,提出一种约束机制来约束模型的学习,改进向量表示。该模型在CFN、FN1.5和FN1.7数据集上优于当前最好模型,证明了方法的有效性。”

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基于框架语义映射和类型感知的篇章事件抽取(Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness)
Jiang Lu (卢江) | Ru Li (李茹) | Xuefeng Su (苏雪峰) | Zhichao Yan (闫智超) | Jiaxing Chen (陈加兴)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“篇章事件抽取是从给定的文本中识别其事件类型和事件论元。目前篇章事件普遍存在数据稀疏和多值论元耦合的问题。基于此,本文将汉语框架网(CFN)与中文篇章事件建立映射,同时引入滑窗机制和触发词释义改善了事件检测的数据稀疏问题;使用基于类型感知标签的多事件分离策略缓解了论元耦合问题。为了提升模型的鲁棒性,进一步引入对抗训练。本文提出的方法在DuEE-Fin和CCKS2021数据集上实验结果显著优于现有方法。”

2021

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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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A Knowledge-Guided Framework for Frame Identification
Xuefeng Su | Ru Li | Xiaoli Li | Jeff Z. Pan | Hu Zhang | Qinghua Chai | Xiaoqi Han
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)

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.

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GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation
Hongye Tan | Xiaoyue Wang | Yu Ji | Ru Li | Xiaoli Li | Zhiwei Hu | Yunxiao Zhao | Xiaoqi Han
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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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

机器阅读理解作为自然语言理解的关键任务,受到国内外学者广泛关注。针对多项选择型阅读理解中无线索标注且涉及多步推理致使候选句抽取困难的问题,本文提出一种基于多模块联合的候选句抽取模型。首先采用部分标注数据微调预训练模型;其次通过TF-IDF递归式抽取多跳推理问题中的候选句;最后结合无监督方式进一步筛选模型预测结果降低冗余性。本文在高考语文选择题及RACE数据集上进行验证,在候选句抽取中,本文方法相比于最优基线模型F1值提升3.44%,在下游答题任务中采用候选句作为模型输入较全文输入时准确率分别提高3.68%和3.6%,上述结果证实本文所提方法有效性。

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基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention)
Xiaohui Wang (王晓晖) | Ru Li (李茹) | Zhiqiang Wang (王智强) | Qinghua Chai (柴清华) | Xiaoqi Han (韩孝奇)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性,目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM,虽然可以获取句子中的长距离依赖信息,但无法很好获取句子中的句法信息。因此,引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明,该模型在CFN(Chinese FrameNet,汉语框架网)数据集上的F1达到83.77%,提升了近11%。

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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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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.

2015

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Implicit Role Linking on Chinese Discourse: Exploiting Explicit Roles and Frame-to-Frame Relations
Ru Li | Juan Wu | Zhiqiang Wang | Qinghua Chai
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2013

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SXUCFN-Core: STS Models Integrating FrameNet Parsing Information
Sai Wang | Ru Li | Ruibo Wang | Zhiqiang Wang | Xia Zhang
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2010

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Chinese Frame Identification using T-CRF Model
Ru Li | Haijing Liu | Shuanghong Li
Coling 2010: Posters