no code implementations • 3 Aug 2023 • Omkar Patil, Lena Reed, Kevin K. Bowden, Juraj Juraska, Wen Cui, Vrindavan Harrison, Rishi Rajasekaran, Angela Ramirez, Cecilia Li, Eduardo Zamora, Phillip Lee, Jeshwanth Bheemanpally, Rohan Pandey, Adwait Ratnaparkhi, Marilyn Walker
Conversational agents are consistently growing in popularity and many people interact with them every day.
1 code implementation • 9 Feb 2023 • Vrindavan Harrison, Rishi Rajasekaran, Marilyn Walker
First, we collect a corpus of Athena conversations with live human traffic, where potential responses from all enabled response generators are logged and subsequently annotated for response quality.
no code implementations • EMNLP (ACL) 2021 • Juraj Juraska, Kevin K. Bowden, Lena Reed, Vrindavan Harrison, Wen Cui, Omkar Patil, Rishi Rajasekaran, Angela Ramirez, Cecilia Li, Eduardo Zamora, Phillip Lee, Jeshwanth Bheemanpally, Rohan Pandey, Adwait Ratnaparkhi, Marilyn Walker
Athena 2. 0 is an Alexa Prize SocialBot that has been a finalist in the last two Alexa Prize Grand Challenges.
no code implementations • 21 Nov 2020 • Vrindavan Harrison, Juraj Juraska, Wen Cui, Lena Reed, Kevin K. Bowden, Jiaqi Wu, Brian Schwarzmann, Abteen Ebrahimi, Rishi Rajasekaran, Nikhil Varghese, Max Wechsler-Azen, Steve Whittaker, Jeffrey Flanigan, Marilyn Walker
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
no code implementations • SIGDIAL (ACL) 2020 • Lena Reed, Vrindavan Harrison, Shereen Oraby, Dilek Hakkani-Tur, Marilyn Walker
Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology.
no code implementations • 13 Aug 2019 • Kevin K. Bowden, Jiaqi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nicholas Santer, Steve Whittaker, Marilyn Walker
In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven.
no code implementations • WS 2019 • Vrindavan Harrison, Lena Reed, Shereen Oraby, Marilyn Walker
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant.
no code implementations • 22 Jul 2019 • Kevin K. Bowden, Jiaqi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nick Santer, Marilyn Walker
One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure.
no code implementations • ACL 2019 • Shereen Oraby, Vrindavan Harrison, Abteen Ebrahimi, Marilyn Walker
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.
no code implementations • WS 2018 • Vrindavan Harrison, Marilyn Walker
Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels.
no code implementations • LREC 2018 • Marilyn A. Walker, Albry Smither, Shereen Oraby, Vrindavan Harrison, Hadar Shemtov
Dialogue systems for hotel and tourist information have typically simplified the richness of the domain, focusing system utterances on only a few selected attributes such as price, location and type of rooms.
no code implementations • WS 2016 • Shereen Oraby, Vrindavan Harrison, Lena Reed, Ernesto Hernandez, Ellen Riloff, Marilyn Walker
The use of irony and sarcasm in social media allows us to study them at scale for the first time.
no code implementations • WS 2017 • Shereen Oraby, Vrindavan Harrison, Amita Misra, Ellen Riloff, Marilyn Walker
We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0. 76 F1 for "sarcastic" and 0. 77 F1 for "other" in forums, and 0. 83 F1 for both "sarcastic" and "other" in tweets.