1 code implementation • 26 Apr 2023 • Jia-Huei Ju, Sheng-Chieh Lin, Ming-Feng Tsai, Chuan-Ju Wang
This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach.
no code implementations • 8 Dec 2021 • Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu, Ming-Feng Tsai, Chih-Jen Lin
In practice such ground truth information is rarely available, but we point out that such an inappropriate setting is now ubiquitous in this research area.
no code implementations • COLING 2020 • Jheng-Hong Yang, Sheng-Chieh Lin, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin
While internalized {``}implicit knowledge{''} in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question.
no code implementations • 30 Aug 2020 • Sheng-Chieh Lin, Ting-Wei Lin, Jing-Kai Lou, Ming-Feng Tsai, Chuan-Ju Wang
In this paper, we propose a two-stage ranking approach for recommending linear TV programs.
no code implementations • 23 May 2020 • Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen, Ming-Feng Tsai
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.
no code implementations • 5 May 2020 • Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin
Conversational search plays a vital role in conversational information seeking.
no code implementations • 4 Apr 2020 • Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin
This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs).
no code implementations • 18 Mar 2020 • Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin
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 the probabilities assigned to the "entailment" token as a score of the hypothesis.
Ranked #17 on Coreference Resolution on Winograd Schema Challenge
2 code implementations • 17 Feb 2019 • Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang
We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation.
Ranked #1 on Recommendation Systems on MovieLens-Latest
no code implementations • 28 Aug 2018 • Chih-Chun Hsia, Kwei-Herng Lai, Yi-An Chen, Chuan-Ju Wang, Ming-Feng Tsai
Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation.
no code implementations • 28 Aug 2018 • Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi, Yi-An Chen, Ming-Feng Tsai, Chuan-Ju Wang
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains.
no code implementations • NAACL 2018 • Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, Ming-Feng Tsai
This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports.
no code implementations • 1 Nov 2017 • Chih-Ming Chen, Yi-Hsuan Yang, Yi-An Chen, Ming-Feng Tsai
Many existing methods adopt a uniform sampling method to reduce learning complexity, but when the network is non-uniform (i. e. a weighted network) such uniform sampling incurs information loss.