Yu, Mingzhi
(2022)
Entrainment in Human-to-Human Dialogue and its Application in End-to-End Dialogue
Systems.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Entrainment is a linguistic phenomenon in which people mimic each other in their conversations. It occurs in a wide range of linguistic dimensions. Entrainment has been exploited in various natural language processing tasks related to dialogue, such as dialogue outcome prediction and dialogue response generation. However, only a few studies have attempted to incorporate entrainment into neural network-based dialogue systems systematically. The present thesis aims to build a neural network-based end-to-end response generation model capable of generating diverse responses by leveraging lexical entrainment, a type of entrainment based on text features. We first demonstrate an automatic entrainment measure relying on conventional similarity metrics based on a bag-of-words approach. Then we show an alternative neural network-based approach to perform the same core similarity measure for entrainment quantification. Lastly, we proposed an end-to-end dialogue response generation model that controls entrainment degree to aid response diversity. We will focus on investigating the effect of incorporating lexical entrainment in the end-to-end dialogue response generation model.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
17 January 2022 |
| Date Type: |
Publication |
| Defense Date: |
5 November 2021 |
| Approval Date: |
17 January 2022 |
| Submission Date: |
30 November 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
132 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Computing and Information > Computer Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Entrainment, Dialogue system, Dialogue response generation, Neural network model |
| Date Deposited: |
17 Jan 2022 15:01 |
| Last Modified: |
17 Jan 2022 15:01 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/41983 |
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