1 code implementation • EACL (BEA) 2021 • Katherine Stasaski, Manav Rathod, Tony Tu, Yunfang Xiao, Marti A. Hearst
Automated question generation has the potential to greatly aid in education applications, such as online study aids to check understanding of readings.
no code implementations • Findings (EMNLP) 2021 • Hwiyeol Jo, Dongyeop Kang, Andrew Head, Marti A. Hearst
Natural language models often fall short when understanding and generating mathematical notation.
no code implementations • 19 Feb 2024 • Anna Martin-Boyle, Aahan Tyagi, Marti A. Hearst, Dongyeop Kang
Numerous AI-assisted scholarly applications have been developed to aid different stages of the research process.
no code implementations • 27 Sep 2023 • Philippe Laban, Jesse Vig, Marti A. Hearst, Caiming Xiong, Chien-Sheng Wu
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing.
1 code implementation • 24 May 2023 • Anna Martin-Boyle, Andrew Head, Kyle Lo, Risham Sidhu, Marti A. Hearst, Dongyeop Kang
We also introduce a new definition extraction method that masks mathematical symbols, creates a copy of each sentence for each symbol, specifies a target symbol, and predicts its corresponding definition spans using slot filling.
no code implementations • 6 Apr 2023 • Katherine Stasaski, Marti A. Hearst
To remedy this, we propose the notion of Pragmatically Appropriate Diversity, defined as the extent to which a conversation creates and constrains the creation of multiple diverse responses.
no code implementations • 25 Mar 2023 • Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie Yu-Yen Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, Fangzhou Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Kinney, Aniket Kittur, Hyeonsu Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita Rao, Paul Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld
Scholarly publications are key to the transfer of knowledge from scholars to others.
no code implementations • NAACL 2022 • Katherine Stasaski, Marti A. Hearst
Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation, which results in an average 137% increase in NLI Diversity compared to standard generation procedures.
1 code implementation • 28 Feb 2022 • Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo
When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature.
no code implementations • 15 Feb 2022 • Philippe Laban, Elicia Ye, Srujay Korlakunta, John Canny, Marti A. Hearst
News podcasts are a popular medium to stay informed and dive deep into news topics.
2 code implementations • 18 Nov 2021 • Philippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. Hearst
In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level).
no code implementations • ACL 2021 • Philippe Laban, Luke Dai, Lucas Bandarkar, Marti A. Hearst
The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text.
1 code implementation • ACL 2021 • Philippe Laban, Luke Dai, Lucas Bandarkar, Marti A. Hearst
The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text.
1 code implementation • ACL 2021 • Philippe Laban, Tobias Schnabel, Paul Bennett, Marti A. Hearst
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity.
no code implementations • ACL 2020 • Philippe Laban, John Canny, Marti A. Hearst
This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news.
1 code implementation • NAACL 2021 • Philippe Laban, Lucas Bandarkar, Marti A. Hearst
Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks.
1 code implementation • ACL 2020 • Philippe Laban, Andrew Hsi, John Canny, Marti A. Hearst
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint.
Ranked #51 on Abstractive Text Summarization on CNN / Daily Mail
1 code implementation • EMNLP (sdp) 2020 • Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst
Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.
1 code implementation • 29 Sep 2020 • Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst
We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols.
no code implementations • ACL 2020 • Katherine Stasaski, Grace Hui Yang, Marti A. Hearst
Automated generation of conversational dialogue using modern neural architectures has made notable advances.
no code implementations • WS 2020 • Katherine Stasaski, Kimberly Kao, Marti A. Hearst
To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns.
no code implementations • EMNLP 2020 • Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel S. Weld, Marti A. Hearst, Jevin West
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.
no code implementations • WS 2019 • Orianna Demasi, Marti A. Hearst, Benjamin Recht
A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation.
no code implementations • WS 2017 • Katherine Stasaski, Marti A. Hearst
An in-depth analysis of the teachers{'} comments yields useful insights for any researcher working on automated question generation for educational applications.
no code implementations • 24 Oct 2014 • Christoph Riedl, Richard Zanibbi, Marti A. Hearst, Siyu Zhu, Michael Menietti, Jason Crusan, Ivan Metelsky, Karim R. Lakhani
We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO).