Search Results for author: Guodong Zhou

Found 97 papers, 16 papers with code

Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation

1 code implementation EMNLP 2021 Xin Tan, Longyin Zhang, Guodong Zhou

Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora.

Machine Translation Question Answering +2

EDTC: A Corpus for Discourse-Level Topic Chain Parsing

1 code implementation Findings (EMNLP) 2021 Longyin Zhang, Xin Tan, Fang Kong, Guodong Zhou

Discourse analysis has long been known to be fundamental in natural language processing.

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。

Response Generation

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

1 code implementation 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 Sentiment Classification

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning

no code implementations COLING 2022 Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

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).

Data Augmentation Machine Reading Comprehension +4

OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch

1 code implementation19 Sep 2023 Juntao Li, Zecheng Tang, Yuyang Ding, Pinzheng Wang, Pei Guo, Wangjie You, Dan Qiao, Wenliang Chen, Guohong Fu, Qiaoming Zhu, Guodong Zhou, Min Zhang

This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.

Opinion Tree Parsing for Aspect-based Sentiment Analysis

1 code implementation15 Jun 2023 Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang, Yue Zhang, Guodong Zhou

To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure.

Sentiment Analysis

Discourse Cohesion Evaluation for Document-Level Neural Machine Translation

no code implementations19 Aug 2022 Xin Tan, Longyin Zhang, Guodong Zhou

It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent.

Machine Translation NMT +1

Joint Learning-based Causal Relation Extraction from Biomedical Literature

no code implementations2 Aug 2022 Dongling Li, Pengchao Wu, Yuehu Dong, Jinghang Gu, Longhua Qian, Guodong Zhou

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions.

Relation Extraction

Pre-trained Token-replaced Detection Model as Few-shot Learner

1 code implementation COLING 2022 Zicheng Li, Shoushan Li, Guodong Zhou

In this paper, as an alternative, we propose a novel approach to few-shot learning with pre-trained token-replaced detection models like ELECTRA.

Few-Shot Learning

Adversarial Learning for Discourse Rhetorical Structure Parsing

1 code implementation ACL 2021 Longyin Zhang, Fang Kong, Guodong Zhou

Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data.

DRS Parsing

Coreference Resolution: Are the eliminated spans totally worthless?

no code implementations4 Jan 2021 Xin Tan, Longyin Zhang, Guodong Zhou

Various neural-based methods have been proposed so far for joint mention detection and coreference resolution.


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.

NUT-RC: Noisy User-generated Text-oriented Reading Comprehension

1 code implementation COLING 2020 Rongtao Huang, Bowei Zou, Yu Hong, Wei zhang, AiTi Aw, Guodong Zhou

Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.

Answer Selection Multi-Task Learning +1

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

Improving AMR Parsing with Sequence-to-Sequence Pre-training

1 code implementation EMNLP 2020 Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.

Ranked #13 on AMR Parsing on LDC2017T10 (using extra training data)

AMR Parsing Machine Translation +1

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 General Classification +3

A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure

1 code implementation ACL 2020 Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years.

Discourse Parsing DRS Parsing +1

A Discrete CVAE for Response Generation on Short-Text Conversation

no code implementations IJCNLP 2019 Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.

Response Generation Short-Text Conversation +1

Negative Focus Detection via Contextual Attention Mechanism

no code implementations IJCNLP 2019 Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw

For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

1 code implementation IJCNLP 2019 Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.

AMR-to-Text Generation Text Generation

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.

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.

General Classification Question Answering +2

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.


Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese

no code implementations ACL 2019 Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou

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.

Tensor Networks

Document-Level Event Factuality Identification via Adversarial Neural Network

no code implementations NAACL 2019 Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP).

Stance Detection with Hierarchical Attention Network

no code implementations COLING 2018 Qingying Sun, Zhongqing Wang, Qiaoming Zhu, Guodong Zhou

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.

Feature Engineering Opinion Mining +1

MCDTB: A Macro-level Chinese Discourse TreeBank

no code implementations COLING 2018 Feng Jiang, Sheng Xu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Guodong Zhou

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.

Reading Comprehension

Adversarial Feature Adaptation for Cross-lingual Relation Classification

1 code implementation COLING 2018 Bowei Zou, Zengzhuang Xu, Yu Hong, Guodong Zhou

In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.

Classification Domain Adaptation +5

Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition

no code implementations COLING 2018 Yu Hong, Yang Xu, Huibin Ruan, Bowei Zou, Jianmin Yao, Guodong Zhou

In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.

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 Gender Classification +2

Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse

no code implementations COLING 2018 Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu

The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information.

Question Answering Text Matching

Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

1 code implementation ACL 2018 Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu

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.

Event Detection Feature Engineering

Modeling Source Syntax for Neural Machine Translation

no code implementations ACL 2017 Junhui Li, Deyi Xiong, Zhaopeng Tu, Muhua Zhu, Min Zhang, Guodong Zhou

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.

Machine Translation NMT +1

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 +2

Global Inference to Chinese Temporal Relation Extraction

no code implementations COLING 2016 Peifeng Li, Qiaoming Zhu, Guodong Zhou, Hongling Wang

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.

Question Answering Temporal Relation Extraction +1

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