Abstract
In this paper, we address semantic parsing in a multilingual context. We train one multilingual model that is capable of parsing natural language sentences from multiple different languages into their corresponding formal semantic representations. We extend an existing sequence-to-tree model to a multi-task learning framework which shares the decoder for generating semantic representations. We report evaluation results on the multilingual GeoQuery corpus and introduce a new multilingual version of the ATIS corpus.
Highlights
We address multilingual semantic parsing – the task of mapping natural language sentences coming from multiple different languages into their corresponding formal semantic representations
We extend an existing sequence-totree model (Dong and Lapata, 2016) to a multitask learning framework, motivated by its success in other fields, e.g., neural machine translation (MT) (Dong et al, 2015; Firat et al, 2016)
Our model consists of multiple encoders, one for each language, and one decoder that is shared across source languages for generating semantic representations
Summary
We consider two multilingual scenarios: 1) the single-source setting, where the input consists of a single sentence in a single language, and 2) the multi-source setting, where the input consists of parallel sentences in multiple languages Previous work handled the former by means of monolingual models (Wong and Mooney, 2006; Lu et al, 2008; Jones et al, 2012), while the latter has only been explored by Jie and Lu (2014) who ensembled many monolingual models together. Our model consists of multiple encoders, one for each language, and one decoder that is shared across source languages for generating semantic representations. In this way, the proposed model potentially benefits from having a generic decoder that works well across languages. We release a new ATIS semantic dataset annotated in two new languages
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