Search Results for author: Shoushan Li

Found 36 papers, 1 papers with code

基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints)

no code implementations CCL 2020 Mengyu Guan, Zhongqing Wang, Shoushan Li, Guodong Zhou

现有的对话系统中存在着生成“好的”、“我不知道”等无意义的安全回复问题。日常对话中, 对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型, 即在Seq2Seq模型的基础上, 结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束, 从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明, 该文提出的方法能有效地提高生成回复的质量。

Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection

no code implementations EMNLP 2021 Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou

Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).

Sentiment Analysis

End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

no code implementations COLING 2020 Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, Xiaoqiang Zhang

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages.

Emotion Cause Pair Extraction Emotion-Cause Pair Extraction

Sentiment Forecasting in Dialog

no code implementations COLING 2020 Zhongqing Wang, Xiujun Zhu, Yue Zhang, Shoushan Li, Guodong Zhou

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation.

Multimodal Topic-Enriched Auxiliary Learning for Depression Detection

no code implementations COLING 2020 Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou

To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i. e., texts and images) for depression detection.

Auxiliary Learning Depression Detection

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

no code implementations ACL 2020 Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.

Classification Document-level +3

Emotion Detection with Neural Personal Discrimination

no code implementations IJCNLP 2019 Xiabing Zhou, Zhongqing Wang, Shoushan Li, Guodong Zhou, Min Zhang

Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions.

Adversarial Attention Modeling for Multi-dimensional Emotion Regression

no code implementations ACL 2019 Suyang Zhu, Shoushan Li, Guodong Zhou

In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text.

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

no code implementations ACL 2019 Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.

Classification General Classification +2

Joint Learning for Emotion Classification and Emotion Cause Detection

no code implementations EMNLP 2018 Ying Chen, Wenjun Hou, Xiyao Cheng, Shoushan Li

We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis.

Classification Emotion Classification +2

Cross-media User Profiling with Joint Textual and Social User Embedding

no code implementations COLING 2018 Jingjing Wang, Shoushan Li, Mingqi Jiang, Hanqian Wu, Guodong Zhou

In realistic scenarios, a user profiling model (e. g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media.

Classification General Classification

Two-View Label Propagation to Semi-supervised Reader Emotion Classification

no code implementations COLING 2016 Shoushan Li, Jian Xu, Dong Zhang, Guodong Zhou

In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.

Classification Emotion Classification +1

Annotating Events in an Emotion Corpus

no code implementations LREC 2014 Sophia Lee, Shoushan Li, Chu-Ren Huang

This paper presents the development of a Chinese event-based emotion corpus.

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