no code implementations • EMNLP 2021 • Kechen Qin, Cheng Li, Virgil Pavlu, Javed Aslam
Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer.
no code implementations • 22 May 2020 • Kechen Qin, Yu Wang, Cheng Li, Kalpa Gunaratna, Hongxia Jin, Virgil Pavlu, Javed A. Aslam
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding.
no code implementations • NAACL 2019 • Bingyu Wang, Li Chen, Wei Sun, Kechen Qin, Kefeng Li, Hui Zhou
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.
no code implementations • NAACL 2019 • Kechen Qin, Cheng Li, Virgil Pavlu, Javed A. Aslam
Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways.
no code implementations • TACL 2017 • Lu Wang, Nick Beauchamp, Sarah Shugars, Kechen Qin
Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%).
no code implementations • ACL 2017 • Kechen Qin, Lu Wang, Joseph Kim
We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns.