Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Language Science and Technology | en_US |
| dc.contributor.advisor | Huang, Chu-ren (LST) | en_US |
| dc.creator | Li, Junlin | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14619 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Enhancing empathy with pragmatics through computational modeling of conversation | en_US |
| dcterms.abstract | Empathy is inseparable from dialogic context. Empathetic dialogue systems generate empathetic responses based on dialogic context. From the perspective of linguistics, especially pragmatics, the expression of empathy requires human-aligned knowledge from multiple pragmatic domains, such as micropragmatics (e.g. speech acts), to macropragmatics (e.g. conversation maxims and crosslingual pragmatics). However, the linguistic-pragmatic enhancement of empathetic dialogue systems has been severely ignored in previous studies, leaving an enormous knowledge gap regarding how linguistic-pragmatics may improve the empathetic capacity of automatic conversation systems. | en_US |
| dcterms.abstract | This thesis presents four language-engineering studies that incorporate pragmatic-inspired constructs into pre-trained and large language models. Through automatic and human evaluations, we demonstrate profound enhancement of empathetic capability due to the computational modeling of representative pragmatic knowledge, including the expression of speech acts, the "effect-effort" trade-off of relevance, the programming of quantity maxims, and also the universal representation of empathy for crosslingual transfer. | en_US |
| dcterms.abstract | The current thesis seals the longstanding research gap with respect to the contribution of linguistic-pragmatic knowledge to empathetic chatbots and human-AI interaction. It offers empirical insights for future studies to extend the application of a wider diversity of linguistic knowledge and theories to enhance the conversational competence of affective dialogue systems and AI chatbots for social good. | en_US |
| dcterms.extent | xvii, 152 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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