Jeff Dalton


2021

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Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
Jarana Manotumruksa | Jeff Dalton | Edgar Meij | Emine Yilmaz
Findings of the Association for Computational Linguistics: EMNLP 2021

While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy. Although several approaches recently propose augmenting the training set by copying user utterances and replacing the real slot values with other possible or even similar values, they are not effective at improving the performance of existing DST models. To address these challenges, we propose a Turn-based Loss Function (TLF) that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns in order to improve joint goal accuracy. We also propose a simple but effective Sequential Data Augmentation (SDA) algorithm to generate more complex user utterances and system responses to effectively train existing DST models. Experimental results on two standard DST benchmark collections demonstrate that our proposed TLF and SDA techniques significantly improve the effectiveness of the state-of-the-art DST model by approximately 7-8% relative reduction in error and achieves a new state-of-the-art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOZ2.2, respectively.

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Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Mohammad Aliannejadi | Julia Kiseleva | Aleksandr Chuklin | Jeff Dalton | Mikhail Burtsev
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.

2020

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Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)
Jeff Dalton | Aleksandr Chuklin | Julia Kiseleva | Mikhail Burtsev
Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)

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MusicBERT - learning multi-modal representations for music and text
Federico Rossetto | Jeff Dalton
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)

2018

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Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
Aleksandr Chuklin | Jeff Dalton | Julia Kiseleva | Alexey Borisov | Mikhail Burtsev
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

2015

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Relation Extraction from Community Generated Question-Answer Pairs
Denis Savenkov | Wei-Lwun Lu | Jeff Dalton | Eugene Agichtein
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop