17 citations · 42 across the 10 of their papers we have counts for
10 papers
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…
Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs
Da Yan, James Cheng, M. Tamer Özsu +5
Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming…
Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation
Da Yan, James Cheng, Yi Lu +1
Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing syst…