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Found 38 papers, 2 papers with code
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence
In this paper, we focus on multi-label emotion detection in a multi-modal scenario.
A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing
Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation.
基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster Chinese Events on Social Streams)
面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前, 事件聚类模型主要分为无监督模型和有监督模型。无监督模型聚类效果较差, 有监督模型依赖大量标注数据。基于此, 本文提出了一种半监督事件聚类模型(SemiEC), 该模型在小规模标注数据的基础上, 利用LSTM表征事件, 利用线性模型计算文本相似度, 进行增量聚类, 利用增量聚类产生的标注数据对模型再训练, 结束后对不确定样本再聚类。实验表明, SemiEC的性能相比其他模型均有所提高。
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation
Therefore, we first view IDRR as a generation task and further propose a method joint modeling of the classification and generation.
Winnowing Knowledge for Multi-choice Question Answering
However, the current reasoning models suffer from the noises in the retrieved knowledge.
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP).
Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark
Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings.
Multi-Granularity Prompts for Topic Shift Detection in Dialogue
The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change.
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing
Discourse parsing, the task of analyzing the internal rhetorical structure of texts, is a challenging problem in natural language processing.
A Hybrid Model of Classification and Generation for Spatial Relation Extraction
Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information.
More than Text: Multi-modal Chinese Word Segmentation
Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing.
Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading
Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English.
Negative Focus Detection via Contextual Attention Mechanism
For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations
On the one hand, our approach represents each utterance and each speaker as a node.
Ranked #43 on
Emotion Recognition in Conversation
on MELD
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.
Document-Level Event Factuality Identification via Adversarial Neural Network
Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP).
MCDTB: A Macro-level Chinese Discourse TreeBank
In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus.
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Stance Detection with Hierarchical Attention Network
In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information.
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection
Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent.
Global Inference to Chinese Temporal Relation Extraction
Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences.
