Search Results for author: David Traum

Found 47 papers, 0 papers with code

Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains

no code implementations IWCS (ACL) 2021 Claire Bonial, Mitchell Abrams, David Traum, Clare Voss

We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors.

AMR Parsing Natural Language Understanding

Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain

no code implementations LREC 2022 Divya Tadimeti, Kallirroi Georgila, David Traum

We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems on dialogue agent-directed English speech from speakers with General American vs. non-American accents.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis

no code implementations LREC 2022 Ada Tur, David Traum

In this paper, we compare two different approaches to language understanding for a human-robot interaction domain in which a human commander gives navigation instructions to a robot.

Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release

no code implementations26 Oct 2023 Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Taylor Hudson, Ron Arstein, Clare Voss, David Traum

Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans.

Interactive Evaluation of Dialog Track at DSTC9

no code implementations LREC 2022 Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi, David Traum, Maxine Eskenazi

Our track challenges participants to develop strong response generation models and explore strategies that extend them to back-and-forth interactions with real users.

Interactive Evaluation of Dialog Open-Domain Dialog +1

Dialogue-AMR: Abstract Meaning Representation for Dialogue

no code implementations LREC 2020 Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron artstein, David Traum, Clare Voss

This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.

Natural Language Understanding

Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers

no code implementations LREC 2020 Kallirroi Georgila, Carla Gordon, Volodymyr Yanov, David Traum

We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions.

Dialogue Evaluation

Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?

no code implementations LREC 2020 Seyed Hossein Alavi, Anton Leuski, David Traum

We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model.

Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains

no code implementations LREC 2020 Kallirroi Georgila, Anton Leuski, Volodymyr Yanov, David Traum

We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems across diverse dialogue domains (in US-English).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Exploring a Choctaw Language Corpus with Word Vectors and Minimum Distance Length

no code implementations LREC 2020 Jacqueline Brixey, David Sides, Timothy Vizthum, David Traum, Khalil Iskarous

This work introduces additions to the corpus ChoCo, a multimodal corpus for the American indigenous language Choctaw.

Augmenting Abstract Meaning Representation for Human-Robot Dialogue

no code implementations WS 2019 Claire Bonial, Lucia Donatelli, Stephanie M. Lukin, Stephen Tratz, Ron artstein, David Traum, Clare Voss

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance.

A Blissymbolics Translation System

no code implementations WS 2019 Usman Sohail, David Traum

Bliss attempts to create a writing system that makes words easier to distinguish by using pictographic symbols that encapsulate meaning rather than sound, as the English alphabet does for example.

Translation

Towards Automatic Identification of Effective Clues for Team Word-Guessing Games

no code implementations LREC 2016 Eli Pincus, David Traum

Team word-guessing games where one player, the clue-giver, gives clues attempting to elicit a target-word from another player, the receiver, are a popular form of entertainment and also used for educational purposes.

The Distress Analysis Interview Corpus of human and computer interviews

no code implementations LREC 2014 Jonathan Gratch, Ron artstein, Gale Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David DeVault, Stacy Marsella, David Traum, Skip Rizzo, Louis-Philippe Morency

The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder.

The Twins Corpus of Museum Visitor Questions

no code implementations LREC 2012 Priti Aggarwal, Ron artstein, Jillian Gerten, Athanasios Katsamanis, Shrikanth Narayanan, Angela Nazarian, David Traum

In addition to speech recordings, the corpus contains the outputs of speech recognition performed at the time of utterance as well as the system interpretation of the utterances.

Dialogue Management Natural Language Understanding +3

Practical Evaluation of Human and Synthesized Speech for Virtual Human Dialogue Systems

no code implementations LREC 2012 Kallirroi Georgila, Alan Black, Kenji Sagae, David Traum

To determine the best trade-off between performance and cost, we perform a systematic evaluation of human and synthesized voices with regard to naturalness, conversational aspect, and likability.

Speech Synthesis

ISO 24617-2: A semantically-based standard for dialogue annotation

no code implementations LREC 2012 Harry Bunt, Alex, Jan ersson, Jae-Woong Choe, Alex Chengyu Fang, Koiti Hasida, Volha Petukhova, Andrei Popescu-Belis, David Traum

This paper summarizes the latest, final version of ISO standard 24617-2 ``Semantic annotation framework, Part 2: Dialogue acts''''''''.

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