Papers with spoken dialogue system
Estimating User Communication Styles for Spoken Dialogue Systems (2020.lrec-1)
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| Challenge: | a neural network estimation system for spoken dialogues can be used to estimate the communication style of a user's interaction, but this is rarely implemented in a live system. |
| Approach: | They propose a neural network approach to estimate the communication style of spoken interaction, namely elaborateness and directness. |
| Outcome: | The proposed method can estimate the elaborateness and directness of spoken interaction and improve the results with additional linguistic features. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks?– a Study of the Effects of “?” for Spoken Dialogue Systems – (2024.lrec-main)
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Tomoya Mizumoto, Takato Yamazaki, Katsumasa Yoshikawa, Masaya Ohagi, Toshiki Kawamoto, Toshinori Sato
| Challenge: | Existing systems for spoken dialogues do not append question marks to recognized queries . lack of punctuation marks in spoken dialogue can negatively impact comprehension . |
| Approach: | They investigate the impact of question marks on spoken dialogue systems . they analyze examples to determine which types of utterances have the impact . |
| Outcome: | The proposed method shows that question marks have a significant impact on spoken dialogue systems. |
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors (2023.emnlp-main)
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| Challenge: | proposed method combines back transcription with fine-grained technique for categorizing speech recognition errors . proposed method relies on the use of synthesized speech in place of audio recording . |
| Approach: | They propose a method for investigating the impact of speech recognition errors on NLU models . they use a back transcription procedure and a fine-grained technique for categorizing errors . |
| Outcome: | The proposed method relies on synthesized speech in place of audio recording to evaluate the model. |