Yangyang Kang


2023

pdf bib
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting
Qingting Xu | Yu Hong | Fubang Zhao | Kaisong Song | Yangyang Kang | Jiaxiang Chen | Guodong Zhou
Findings of the Association for Computational Linguistics: EMNLP 2023

Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. These quintuples include subject, object, shareable aspect, comparative opinion, and preference. The existing pipeline-based COQE method fails in error propagation. In addition, the complexity and insufficient amounts of annotated data hinder the performance of COQE models. In this paper, we introduce a novel approach called low-resource comparative opinion quintuple extraction by Data Augmentation with Prompting (DAP). Firstly, we present an end-to-end model architecture better suited to the data augmentation method from triplets to quintuples and can effectively avoid error propagation. Additionally, we introduce a data-centric augmentation approach that leverages the robust generative abilities of ChatGPT and integrates transfer learning techniques. Experimental results over three datasets (Camera, Car, Ele) demonstrate that our approach yields substantial improvements and achieves state-of-the-art results. The source code and data are publicly released at: https://github.com/qtxu-nlp/COQE-DAP.

pdf bib
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
Chengyuan Liu | Fubang Zhao | Yangyang Kang | Jingyuan Zhang | Xiang Zhou | Changlong Sun | Kun Kuang | Fei Wu
Findings of the Association for Computational Linguistics: EMNLP 2023

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. Previous works have achieved success by unifying a few tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), while they fall short of being true UIE models particularly when extracting other general schemas such as quadruples and quintuples. Additionally, these models used an implicit structural schema instructor, which could lead to incorrect links between types, hindering the model’s generalization and performance in low-resource scenarios. In this paper, we redefine the true UIE with a formal formulation that covers almost all extraction schemas. To the best of our knowledge, we are the first to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE. To avoid interference between different types, we reset the position ids and attention mask matrices. RexUIE shows strong performance under both full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

pdf bib
Content- and Topology-Aware Representation Learning for Scientific Multi-Literature
Kai Zhang | Kaisong Song | Yangyang Kang | Xiaozhong Liu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Representation learning forms an essential building block in the development of natural language processing architectures. To date, mainstream approaches focus on learning textual information at the sentence- or document-level, unfortunately, overlooking the inter-document connections. This omission decreases the potency of downstream applications, particularly in multi-document settings. To address this issue, embeddings equipped with latent semantic and rich relatedness information are needed. In this paper, we propose SMRC2, which extends representation learning to the multi-document level. Our model jointly learns latent semantic information from content and rich relatedness information from topological networks. Unlike previous studies, our work takes multi-document as input and integrates both semantic and relatedness information using a shared space via language model and graph structure. Our extensive experiments confirm the superiority and effectiveness of our approach. To encourage further research in scientific multi-literature representation learning, we will release our code and a new dataset from the biomedical domain.

pdf bib
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion
Xurui Li | Yue Qin | Rui Zhu | Tianqianjin Lin | Yongming Fan | Yangyang Kang | Kaisong Song | Fubang Zhao | Changlong Sun | Haixu Tang | Xiaozhong Liu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.

2021

pdf bib
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning
Fubang Zhao | Zhuoren Jiang | Yangyang Kang | Changlong Sun | Xiaozhong Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
Jiawei Liu | Kaisong Song | Yangyang Kang | Guoxiu He | Zhuoren Jiang | Changlong Sun | Wei Lu | Xiaozhong Liu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia. In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information. Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared encoder. Extensive experiments on two public datasets demonstrate the effectiveness of our model.

2020

pdf bib
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
WeiSheng Zhang | Kaisong Song | Yangyang Kang | Zhongqing Wang | Changlong Sun | Xiaozhong Liu | Shoushan Li | Min Zhang | Luo Si
Findings of the Association for Computational Linguistics: EMNLP 2020

As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

pdf bib
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning
Zhuoren Jiang | Zhe Gao | Yu Duan | Yangyang Kang | Changlong Sun | Qiong Zhang | Xiaozhong Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. A “self-diversity” criterion is proposed for measuring the “worthiness” of a candidate for annotation. A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation. The preliminary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the performance of a series of conventional active learning models for Chinese spam detection task. To the best of our knowledge, this is the first work to integrate active learning and semi-supervised generative learning for text spam detection.

2019

pdf bib
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
Zhuoren Jiang | Zhe Gao | Guoxiu He | Yangyang Kang | Changlong Sun | Qiong Zhang | Luo Si | Xiaozhong Liu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.

2018

pdf bib
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
Chenlin Shen | Changlong Sun | Jingjing Wang | Yangyang Kang | Shoushan Li | Xiaozhong Liu | Luo Si | Min Zhang | Guodong Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.