The quality of Natural Language Understanding (NLU) models is typically evaluated using aggregated metrics on a large number of utterances. In a dialog system, though, the manual analysis of failures on specific utterances is a time-consuming and yet critical endeavor to guarantee a high-quality customer experience. A crucial question for this analysis is how to create a test set of utterances that covers a diversity of possible customer requests. In this paper, we introduce the task of generating a test set with high semantic diversity for NLU evaluation in dialog
systems and we describe an approach to address it. The approach starts by extracting high-traffic utterance patterns. Then, for each pattern, it achieves high diversity selecting utterances from different regions of the utterance embedding space. We compare three selection strategies based on clustering of utterances in the embedding space, on solving the maximum distance optimization problem and on simple heuristics such as random uniform sampling and popularity. The evaluation
shows that the highest semantic and lexicon diversity is obtained by a greedy maximum sum of distance solver in a comparable runtime with the clustering and the heuristics approaches.

Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems

IND1: Dialogue

technical paper

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Enrico Palumbo

1

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Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems

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