Papers by Anton Leuski
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? (2020.lrec-1)
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| Challenge: | End-to-end neural network models of conversational dialogue are popular for conversational tasks, but there are still questions about how well they work for real applications and how much data is needed to achieve acceptable performance. |
| Approach: | They compare two different kinds of end-to-end dialogue models based on cross-language relevance and cross-linguistic LSTM models for corpus-based selection of dialogue responses. |
| Outcome: | The proposed models perform well on a large corpus, but are dominated by a more moderate-sized corpus. |
ScoutBot: A Dialogue System for Collaborative Navigation (P18-4)
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Stephanie M. Lukin, Felix Gervits, Cory J. Hayes, Pooja Moolchandani, Anton Leuski, John G. Rogers III, Carlos Sanchez Amaro, Matthew Marge, Clare R. Voss, David Traum
| Challenge: | Demo will allow users to issue unconstrained spoken language commands to ScoutBot. |
| Approach: | The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. |
| Outcome: | The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. |
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains (2020.lrec-1)
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| Challenge: | a recent study evaluated off-the-shelf automatic speech recognition systems . current state-of-the art systems perform poorly in domains that require special vocabulary and language models . |
| Approach: | They evaluate off-the-shelf automatic speech recognition systems across different dialogue domains . they use data collected from deployed spoken dialogue systems and human-human conversations . |
| Outcome: | The evaluation is aimed at non-experts with limited experience in speech recognition . the results show that the performance of each speech recognizer can vary significantly depending on the domain . |
The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents (L18-1)
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Ron Artstein, Jill Boberg, Alesia Gainer, Jonathan Gratch, Emmanuel Johnson, Anton Leuski, Gale Lucas, David Traum
| Challenge: | Niki and Julie corpus contains more than 600 dialogues between humans and robots . corpus includes audio and video recordings, results of ranking tasks, questionnaire responses . |
| Approach: | the corpus contains more than 600 dialogues between human participants and a robot . the dialogues are part of a collaborative item-ranking task designed to measure influence . |
| Outcome: | the corpus contains more than 600 dialogues between human participants and a robot or virtual agent . the dialogues contain conversational errors by the robot, which simulates typical of modern automated agents . |
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)
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Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor A. Hudson, Cory J. Hayes, Kimberly Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare Voss
| Challenge: | The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
| Approach: | They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. |
| Outcome: | The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |