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Found 62 papers, 21 papers with code
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing
Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations
The emotion cause is a stimulus for human emotions.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge
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.
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)
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
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.
Ranked #1 on
Argument Pair Extraction (APE)
on RR
Progressive Self-Training with Discriminator for Aspect Term Extraction
In this paper, we use two means to alleviate the noise in the pseudo-labels.
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training
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.
Ranked #5 on
Task-Oriented Dialogue Systems
on KVRET
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing
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.
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)
“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。”
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)
“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。”
HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis
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.
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme
The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts.
Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension
Argument pair extraction (APE) aims to automatically mine argument pairs from two interrelated argumentative documents.
Argument Pair Extraction (APE)
Machine Reading Comprehension
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing
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.
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
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.
Self-Critique Prompting with Large Language Models for Inductive Instructions
Numerous works are proposed to improve or evaluate the capabilities of Large language models (LLMs) to fulfill user instructions.
Chain-of-thought prompting for responding to in-depth dialogue questions with LLM
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}).
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation
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.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: A Preliminary Empirical Study
Evaluating the quality of generated text is a challenging task in natural language processing.
Exploring the Feasibility of ChatGPT for Event Extraction
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.
Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning
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.
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction.
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.
Interpretable Proof Generation via Iterative Backward Reasoning
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.
Generating Tips from Song Reviews: A New Dataset and Framework
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
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.
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Representations of events described in text are important for various tasks.
A Neural Transition-based Model for Argumentation Mining
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.
HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection.
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
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.
REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation
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.
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis
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.
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems
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.
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance
In this way, our model can learn from different teacher layers adaptively for various NLP tasks.
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.
Ranked #2 on
Emotion-Cause Pair Extraction
on ECPE-FanSplit
Target-based Sentiment Annotation in Chinese Financial News
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.
The Design and Construction of a Chinese Sarcasm Dataset
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
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.
Neural Topic Model with Reinforcement Learning
In recent years, advances in neural variational inference have achieved many successes in text processing.
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on MAMS
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.
Ranked #2 on
Emotion Cause Extraction
on ECE
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis
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).
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates
Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining.
Variational Autoregressive Decoder for Neural Response Generation
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.
A Question Answering Approach for Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
A Question Answering Approach to Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
Ranked #8 on
Emotion Cause Extraction
on ECE
