K
Kechen Qin
Researcher at Northeastern University
Publications - 17
Citations - 132
Kechen Qin is an academic researcher from Northeastern University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 5, co-authored 14 publications receiving 97 citations. Previous affiliations of Kechen Qin include Wuhan University.
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Journal ArticleDOI
Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes
TL;DR: A predictive model of debate is proposed that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two, and finds that winning sides employ stronger arguments.
Proceedings ArticleDOI
Joint Modeling of Content and Discourse Relations in Dialogues
Kechen Qin,Lu Wang,Joseph Kim +2 more
TL;DR: The authors presented a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns, which outperformed state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks.
Proceedings ArticleDOI
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction
TL;DR: This work presents an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence, and provides a new training objective that maximizes this set probability and a new prediction objective that finds the most probable set on a test document.
Proceedings ArticleDOI
Ranking-Based Autoencoder for Extreme Multi-label Classification
TL;DR: In this article, a ranking-based autoencoder is proposed for extreme multi-label classification, which projects labels and features onto a common embedding space and improves feature representation by highlighting feature importance.
Proceedings Article
Improving Query Graph Generation for Complex Question Answering over Knowledge Base
TL;DR: The authors proposed a new solution to query graph generation that works in the opposite manner: they start with the entire knowledge base and gradually shrink it to the desired query graph, which improves both the efficiency and the accuracy of query graph generator.