SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems
| Autor: | Saurabh Bagchi, Riham Mansour, Ashraf Mahgoub, Youssef Shahin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Supervised learning Intelligent decision support system 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences 020204 information systems Metric (mathematics) Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Artificial intelligence business Representation (mathematics) computer Sentence Natural language processing Utterance 0105 earth and related environmental sciences |
| Zdroj: | CoNLL |
| Popis: | Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user’s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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