Natural Language Generation in Dialogue Systems for Customer Care
| Autor: | Di Lascio, M., MANUELA SANGUINETTI, Anselma, L., Mana, D., Mazzei, A., Patti, V., Simeoni, R. |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Twitter during Pandemic
Automatic Sarcasm Detection Linguistic Ostracism in Social Networks AriEmozione COVID-19 Linguistics LAN000000 Quantitative Linguistic Investigations Fine-grained sentiment analysis Online Hate Speech Computational Linguistics DistilBERT Depression from Social Media Distributional Semantics Gender Bias CBX AEREST E3C Project Multilingual NLU TrAVaSI |
| Zdroj: | Scopus-Elsevier |
| Popis: | In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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