Modeling Comparative Logical Relation with Contrastive Learning for Text Generation
East China Normal University（华东师范大学）；Shanghai Institute of Artificial Intelligence for Education, ECNU（华东师范大学上海人工智能教育研究院）；School of Computer Science and Technology, ECNU（华东师范大学计算机科学与技术学院）；Xiaohongshu Inc（小红书公司）；Alibaba Group（阿里巴巴集团）
Yuhao Dan, Junfeng Tian, Jie Zhou, Ming Yan, Ji Zhang, Qin Chen, Liang He
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among entities, while ignoring the deep comparative logical relations, such as A is better than B in a certain aspect with a corresponding opinion, which is quite common in our daily life. In this paper, we introduce a new D2T task named comparative logical relation generation (CLRG). Additionally, we propose a Comparative Logic (CoLo) based text generation method, which generates texts following specific comparative logical relations with contrastive learning. Specifically, we first construct various positive and negative samples by fine-grained perturbations in entities, aspects and opinions. Then, we perform contrastive learning in the encoder layer to have a better understanding of the comparative logical relations, and integrate it in the decoder layer to guide the model to correctly generate the relations. Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation Dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations. Extensive experiments show that our method achieves impressive performance in both automatic and human evaluations.
Logic-Oriented Retriever Enhancement via Contrastive Learning
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China（上海人工智能教育研究院，华东师范大学）；Education Technology, East China Normal University, China（教育技术，华东师范大学）
Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs
University of Bonn（波恩大学）；Zerotha Research（泽罗塔研究院）；Cerence GmbH（赛轮思有限公司）；University of Siegen（锡根大学）；Amazon（亚马逊）；Institute for Applied Informatics（应用信息学研究所）
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology（北京理工大学海量语言信息处理与云计算应用北京工程研究中心）
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer
Stevens Institute of Technology（史蒂文斯理工学院）；San Francisco State University（旧金山州立大学）；University of Minnesota（明尼苏达大学）；The Chinese University of Hong Kong（香港中文大学）；New York University（纽约大学）
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Strong AI Lab, NAOInstitute, Waipapa Taumata Rau - The University of Auckland（强人工智能实验室、NAO研究所、怀帕帕·陶马陶·劳 - 奥克兰大学）；Xtracta, New Zealand（新西兰Xtracta公司）；University of Cambridge, UK（英国剑桥大学）；Sun Yat-Sen University, China（中国中山大学）；The University of Auckland, New Zealand（新西兰奥克兰大学）
