Refer, Reuse, Reduce
| Autor: | Takmaz, E., Giulianelli, M., Pezzelle, S., Sinclair, A., Fernández, R., Webber, B., Cohn, T., He, Y., Liu, Y. |
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| Přispěvatelé: | Language and Computation (ILLC, FNWI/FGw), ILLC (FNWI), Brain and Cognition, Faculty of Science |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Context (language use) 02 engineering and technology Reuse computer.software_genre 050105 experimental psychology Group cohesiveness 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Conversation media_common Computer Science - Computation and Language business.industry 05 social sciences Common ground Resolution (logic) 020201 artificial intelligence & image processing Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) EMNLP (1) 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020, 4350-4368 STARTPAGE=4350;ENDPAGE=4368;TITLE=2020 Conference on Empirical Methods in Natural Language Processing |
| Popis: | Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness. Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions. In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue. We propose a generation model that produces referring utterances grounded in both the visual and the conversational context. To assess the referring effectiveness of its output, we also implement a reference resolution system. Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans. Comment: In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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