56 citations · 94 across the 16 of their papers we have counts for
16 papers
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation
Chen Xu, Tian Lan, Yu Ji +8
Constrained decoding approaches aim to control the meaning or style of text generated by the pre-trained large language models (LLMs or also PLMs) for various tasks at inference ti…
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
Jun Gao, Huan Zhao, Wei Wang +2
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision wi…
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction
Jun Gao, Huan Zhao, Yice Zhang +3
Information Extraction (IE) is an essential task in Natural Language Processing. Traditional methods have relied on coarse-grained extraction with simple instructions. However, wit…
Exploring the Feasibility of ChatGPT for Event Extraction
Jun Gao, Huan Zhao, Changlong Yu +1
Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challeng…
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
Jun Gao, Changlong Yu, Wei Wang +2
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can genera…
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
Fangqi Zhu, Jun Gao, Changlong Yu +5
Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted t…
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset
Haolin Deng, Yanan Zhang, Yangfan Zhang +9
Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and…
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery
Changlong Yu, Weiqi Wang, Xin Liu +6
Understanding users' intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework to…
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks
Changlong Yu, Tianyi Xiao, Lingpeng Kong +2
Though linguistic knowledge emerges during large-scale language model pretraining, recent work attempt to explicitly incorporate human-defined linguistic priors into task-specific…
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Jun Gao, Wei Wang, Changlong Yu +3
Representations of events described in text are important for various tasks. In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering fra…
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse Relations
Changlong Yu, Hongming Zhang, Yangqiu Song +1
Large-scale pre-trained language models have demonstrated strong knowledge representation ability. However, recent studies suggest that even though these giant models contains rich…
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
Changlong Yu, Jialong Han, Peifeng Wang +4
We address hypernymy detection, i.e., whether an is-a relationship exists between words (x, y), with the help of large textual corpora. Most conventional approaches to this task ha…
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
Fuyu Lv, Mengxue Li, Tonglei Guo +4
Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based Retrieval (EBR)…
Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
Changlong Yu, Hongming Zhang, Yangqiu Song +2
Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities. In this…
Multiplex Word Embeddings for Selectional Preference Acquisition
Hongming Zhang, Jiaxin Bai, Yan Song +5
Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such repr…
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
Fuyu Lv, Taiwei Jin, Changlong Yu +4
Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching appr…