SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems

Ashraf Mahgoub, Youssef Shahin, Riham Mansour, Saurabh Bagchi


Abstract
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.
Anthology ID:
K19-1066
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
708–717
Language:
URL:
https://www.aclweb.org/anthology/K19-1066
DOI:
10.18653/v1/K19-1066
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1066.pdf