no code implementations • 12 Feb 2023 • Xavier Amatriain, Ananth Sankar, Jie Bing, Praveen Kumar Bodigutla, Timothy J. Hazen, Michaeel Kazi
The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models.
no code implementations • 22 Nov 2020 • Yadollah Yaghoobzadeh, Alexandre Rochette, Timothy J. Hazen
Duplicate question detection (DQD) is important to increase efficiency of community and automatic question answering systems.
no code implementations • 9 Jul 2020 • Timothy J. Hazen, Alexandra Olteanu, Gabriella Kazai, Fernando Diaz, Michael Golebiewski
Past research shows that users benefit from systems that support them in their writing and exploration tasks.
no code implementations • 6 Nov 2019 • Timothy J. Hazen, Shehzaad Dhuliawala, Daniel Boies
This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains.
no code implementations • 6 Nov 2019 • Alexandre Rochette, Yadollah Yaghoobzadeh, Timothy J. Hazen
In this paper, we apply BERT to DQD and advance it by unsupervised adaptation to StackExchange domains using self-supervised learning.
no code implementations • WS 2019 • Peter Potash, Adam Ferguson, Timothy J. Hazen
We detail the process of extracting topical passages for queries submitted to a search engine, creating annotated sets of passages aligned to different stances on a topic, and assessing argument convincingness of passages using pairwise annotation.
1 code implementation • ACL 2019 • Yadollah Yaghoobzadeh, Katharina Kann, Timothy J. Hazen, Eneko Agirre, Hinrich Schütze
Word embeddings typically represent different meanings of a word in a single conflated vector.