L
Liu Yang
Researcher at University of Massachusetts Amherst
Publications - 61
Citations - 3378
Liu Yang is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Question answering & Conversation. The author has an hindex of 24, co-authored 61 publications receiving 2153 citations. Previous affiliations of Liu Yang include Google & Singapore Management University.
Papers
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Proceedings ArticleDOI
Towards Conversational Search and Recommendation: System Ask, User Respond
TL;DR: This paper proposes a System Ask -- User Respond (SAUR) paradigm for conversational search, defines the major components of the paradigm, and designs a unified implementation of the framework for product search and recommendation in e-commerce.
Posted Content
Long Range Arena: A Benchmark for Efficient Transformers
Yi Tay,Mostafa Dehghani,Samira Abnar,Yikang Shen,Dara Bahri,Philip Pham,Jinfeng Rao,Liu Yang,Sebastian Ruder,Donald Metzler +9 more
TL;DR: A systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios, paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle.
Journal ArticleDOI
A Deep Look into neural ranking models for information retrieval
Jiafeng Guo,Yixing Fan,Liang Pang,Liu Yang,Qingyao Ai,Hamed Zamani,Chen Wu,W. Bruce Croft,Xueqi Cheng +8 more
TL;DR: A deep look into the neural ranking models from different dimensions is taken to analyze their underlying assumptions, major design principles, and learning strategies to obtain a comprehensive empirical understanding of the existing techniques.
Proceedings ArticleDOI
CQArank: jointly model topics and expertise in community question answering
TL;DR: This work proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis, and proposed CQARank to measure user interests and expertise score under different topics.
Proceedings ArticleDOI
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
TL;DR: This article proposed an attention-based neural matching model for ranking short answer text, which adopts value-shared weighting scheme instead of position shared weighting for combining different matching signals and incorporate question term importance learning using question attention network.