most citedConversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models

36 citations · 39 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR20203 cited

Personalized TV Recommendation: Fusing User Behavior and Preferences

Sheng-Chieh Lin, Ting-Wei Lin, Jing-Kai Lou +2

In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channe…

cs.IR2020

Skewness Ranking Optimization for Personalized Recommendation

Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen +1

In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifi…

cs.CL2020

Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting

Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3

Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retriev…

cs.CL202036 cited

Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models

Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3

This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLM…

cs.CL2020

TTTTTackling WinoGrande Schemas

Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3

We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using…