Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data

• URL: http://arxiv.org/abs/2109.04319v1
• Date: Thu, 9 Sep 2021 14:51:11 GMT
• Title: Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data
• Authors: Massimo Nicosia, Zhongdi Qu and Yasemin Altun
• Abstract summary: We propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parsing task. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems.
• Score: 2.225882303328135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available. In this paper, we propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parser. This method simplifies the popular Translate-Align-Project (TAP) pipeline and consists of a sequence-to-sequence filler model that constructs a full parse conditioned on an utterance and a view of the same parse. Our filler is trained on English data only but can accurately complete instances in other languages (i.e., translations of the English training utterances), in a zero-shot fashion. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems which rely on traditional alignment techniques.

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