Search Results for author: Vrindavan Harrison

Found 13 papers, 1 papers with code

A Transformer-based Response Evaluator for Open-Domain Spoken Conversation

1 code implementation9 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.

Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation

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.

Dialogue Generation

Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System

no code implementations13 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.

Scheduling Topic coverage

Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast

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.

SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre

no code implementations22 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.

Exploring Conversational Language Generation for Rich Content about Hotels

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.

Text Generation

Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog

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.

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