Search Results for author: Ruifeng Xu

Found 62 papers, 21 papers with code

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing

no code implementations EMNLP 2021 Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).

Entity Typing

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection

1 code implementation ACL 2022 Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu

In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.

Contrastive Learning Stance Detection

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge

2 code implementations EMNLP 2021 Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.

graph construction Sentiment Analysis

基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)

no code implementations CCL 2020 Wangda Luo, YuHan Liu, Bin Liang, Ruifeng Xu

针对问答立场任务中, 现有方法难以提取问答文本间的依赖关系问题, 本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式, 基于交互注意力机制和循环迭代方法, 有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外, 该方法将问题进行陈述化表示, 有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明, 本文方法取得了比现有模型方法更好的效果, 同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph

no code implementations EMNLP 2021 Jianzhu Bao, Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu

In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage.

Argument Pair Extraction (APE) Relation Classification

Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training

1 code implementation EMNLP 2020 Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu

Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.

Task-Oriented Dialogue Systems

Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing

1 code implementation COLING 2022 Zeyi Zhong, Min Yang, Ruifeng Xu

In this paper, we propose a novel Spurious Correlation reduction method to improve the robustness of the neural ANswer selection models (SCAN) from the sample and feature perspectives by removing the feature dependencies and language biases in answer selection.

Answer Selection Contrastive Learning

面向话题的讽刺识别:新任务、新数据和新方法(Topic-Oriented Sarcasm Detection: New Task, New Dataset and New Method)

no code implementations CCL 2022 Bin Liang, Zijie Lin, Bing Qin, Ruifeng Xu

“现有的文本讽刺识别研究通常只停留在句子级别的讽刺表达分类, 缺乏考虑讽刺对象对讽刺表达的影响。针对这一问题, 本文提出一个新的面向话题的讽刺识别任务。该任务通过话题的引入, 以话题作为讽刺对象, 有助于更好地理解和建模讽刺表达。对应地, 本文构建了一个新的面向话题的讽刺识别数据集。这个数据集包含了707个话题, 以及对应的4871个话题-评论对组。在此基础上, 基于提示学习和大规模预训练语言模型, 提出了一种面向话题的讽刺表达提示学习模型。在本文构建的面向话题讽刺识别数据集上的实验结果表明, 相比基线模型, 本文所提出的面向话题的讽刺表达提示学习模型取得了更优的性能。同时, 实验分析也表明本文提出的面向话题的讽刺识别任务相比传统的句子级讽刺识别任务更具挑战性。”

Sarcasm Detection

基于主题提示学习的零样本立场检测方法(A Topic-based Prompt Learning Method for Zero-Shot Stance Detection)

no code implementations CCL 2022 Zixiao Chen, Bin Liang, Ruifeng Xu

“零样本立场检测目的是针对未知目标数据进行立场极性预测。一般而言, 文本的立场表达是与所讨论的目标主题是紧密联系的。针对未知目标的立场检测, 本文将立场表达划分为两种类型:一类在说话者面向不同的主题和讨论目标时表达相同的立场态度, 称之为目标无关的表达;另一类在说话者面向特定主题和讨论目标时才表达相应的立场态度, 本文称之为目标依赖的表达。对这两种表达进行区分, 有效学习到目标无关的表达方式并忽略目标依赖的表达方式, 有望强化模型的可迁移能力, 使其更加适应零样本立场检测任务。据此, 本文提出了一种基于主题提示学习的零样本立场检测方法。具体而言, 受自监督学习的启发, 本文为了零样本立场检测设置了一个代理任务框架。其中, 代理任务通过掩盖上下文中的目标主题词生成辅助样本, 并基于提示学习分别预测原样本和辅助样本的立场表达, 随后判断原样本和辅助样本的立场表达是否一致, 从而在无需人工标注的情况下判断样本的立场表达是否依赖于目标的代理标签。然后, 将此代理标签提供给立场检测模型, 对应学习可迁移的立场检测特征。在两个基准数据集上的大量实验表明, 本文提出的方法在零样本立场检测任务中相比基线模型取得了更优的性能。”

Stance Detection

HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis

no code implementations SemEval (NAACL) 2022 Yihui Li, Yifan Yang, Yice Zhang, Ruifeng Xu

This paper describes our system that participated in the SemEval-2022 Task 10: Structured Sentiment Analysis, which aims to extract opinion tuples from texts. A full opinion tuple generally contains an opinion holder, an opinion target, the sentiment expression, and the corresponding polarity. The complex structure of the opinion tuple makes the task challenging. To address this task, we formalize it as a span-relation extraction problem and propose a two-stage extraction framework accordingly. In the first stage, we employ the span module to enumerate spans and then recognize the type of every span. In the second stage, we employ the relation module to determine the relation between spans. Our system achieves competitive results and ranks among the top-10 systems in almost subtasks.

Relation Extraction Sentiment Analysis

Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme

no code implementations EMNLP 2020 Chaofa Yuan, Chuang Fan, Jianzhu Bao, Ruifeng Xu

The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts.

Emotion-Cause Pair Extraction

Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing

no code implementations ACL 2022 Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu

In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.

Entity Typing

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network

1 code implementation ACL 2022 Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.

Sarcasm Detection

Self-Critique Prompting with Large Language Models for Inductive Instructions

1 code implementation23 May 2023 Rui Wang, Hongru Wang, Fei Mi, Yi Chen, Ruifeng Xu, Kam-Fai Wong

Numerous works are proposed to improve or evaluate the capabilities of Large language models (LLMs) to fulfill user instructions.

Fact Checking

Chain-of-thought prompting for responding to in-depth dialogue questions with LLM

1 code implementation19 May 2023 Hongru Wang, Rui Wang, Fei Mi, Zezhong Wang, Ruifeng Xu, Kam-Fai Wong

To this end, we first construct a benchmark of 6 dialogue or question-answering datasets in both English and Chinese, covering 3 different aspects of user status (\textit{including} \textit{personality}, \textit{emotion}, and \textit{psychology}).

Question Answering Semantic Similarity +1

SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation

no code implementations4 May 2023 Xingwei Liang, You Zou, Ruifeng Xu

Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history.

Emotion Recognition in Conversation Opinion Mining

Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary

1 code implementation20 Apr 2023 Xiaokang Liu, Jianquan Li, Jingjing Mu, Min Yang, Ruifeng Xu, Benyou Wang

In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.

Contrastive Learning intent-classification +1

Exploring the Feasibility of ChatGPT for Event Extraction

no code implementations7 Mar 2023 Jun Gao, Huan Zhao, Changlong Yu, Ruifeng Xu

While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction.

Event Extraction Machine Translation +2

Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning

no code implementations6 Mar 2023 Mingshan Chang, Min Yang, Qingshan Jiang, Ruifeng Xu

Concretely, we employ the Variational Information Bottleneck (VIB) principle to learn an informative and compressed network (self-pruned network) from the original network, which discards the superfluous patterns or spurious correlations between input features and prediction labels.

Aspect-Based Sentiment Analysis (ABSA) Contrastive Learning

A Generative Approach for Script Event Prediction via Contrastive Fine-tuning

1 code implementation7 Dec 2022 Fangqi Zhu, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang, Ruifeng Xu

First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.

Language Modelling

Interpretable Proof Generation via Iterative Backward Reasoning

1 code implementation NAACL 2022 Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu

We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer.

Question Answering

Generating Tips from Song Reviews: A New Dataset and Framework

no code implementations14 May 2022 Jingya Zang, Cuiyun Gao, Yupan Chen, Ruifeng Xu, Lanjun Zhou, Xuan Wang

However, reviews of music songs are generally long in length and most of them are non-informative for users.

Empathetic Response Generation with State Management

1 code implementation7 May 2022 YuHan Liu, Jun Gao, Jiachen Du, Lanjun Zhou, Ruifeng Xu

The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking.

Dialogue Management Empathetic Response Generation +3

A Neural Transition-based Model for Argumentation Mining

no code implementations ACL 2021 Jianzhu Bao, Chuang Fan, Jipeng Wu, Yixue Dang, Jiachen Du, Ruifeng Xu

Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation.

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

1 code implementation ACL 2021 Binzong Geng, Fajie Yuan, Qiancheng Xu, Ying Shen, Ruifeng Xu, Min Yang

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.

Continual Learning Network Pruning

Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification

1 code implementation21 Jun 2021 Binzong Geng, Min Yang, Fajie Yuan, Shupeng Wang, Xiang Ao, Ruifeng Xu

In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.

Network Pruning Sentiment Analysis +1

REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation

no code implementations30 May 2021 Jun Gao, Wei Bi, Ruifeng Xu, Shuming Shi

We first clarify an assumption on reference-based metrics that, if more high-quality references are added into the reference set, the reliability of the metric will increase.

Open-Domain Dialog

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

1 code implementation COLING 2020 Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects.

Sentiment Analysis

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

1 code implementation COLING 2020 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.

Target-based Sentiment Annotation in Chinese Financial News

no code implementations LREC 2020 Chaofa Yuan, Yu-Han Liu, Rongdi Yin, Jun Zhang, Qinling Zhu, Ruibin Mao, Ruifeng Xu

Based on high quality annotation guideline and effective quality control strategy, a corpus with 8, 314 target-level sentiment annotation is constructed on 6, 336 paragraphs from Chinese financial news text.

Sentiment Analysis

The Design and Construction of a Chinese Sarcasm Dataset

no code implementations LREC 2020 Xiaochang Gong, Qin Zhao, Jun Zhang, Ruibin Mao, Ruifeng Xu

Thus, the detection and processing of sarcasm is important to social media analysis. However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset.

Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

1 code implementation16 Dec 2019 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.

Dialogue Generation Knowledge Base Question Answering +1

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

no code implementations ACL 2019 Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA).

Aspect-Based Sentiment Analysis (ABSA)

Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates

no code implementations EMNLP 2018 Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu

Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining.

Natural Language Inference Sentiment Analysis

Variational Autoregressive Decoder for Neural Response Generation

no code implementations EMNLP 2018 Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.

Response Generation

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