Optimizing Machine Translation for Virtual Assistants: Multi-Variant Generation with VerbNet and Conditional Beam Search

Abstract

In this paper, we introduce a domain-adapted machine translation (MT) model for intelligent virtual assistants (IVA) designed to translate natural language understanding (NLU) training data sets. This work uses a constrained beam search to generate multiple valid translations for each input sentence. The search for the best translations in the presented translation algorithm is guided by a verb-frame ontology we derived from VerbNet. To assess the quality of the presented MT models, we train NLU models on these multiverb-translated resources and compare their performance to models trained on resources translated with a traditional single-best approach. Our experiments show that multi-verb translation improves intent classification accuracy by 3.8% relative compared to singlebest translation. We release five MT models that translate from English to Spanish, Polish, Swedish, Portuguese, and French, as well as an IVA verb ontology that can be used to evaluate the quality of IVA-adapted MT.