Semantic Parsing with Less Prior and More Monolingual Data
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Semantic parsing is the task of converting natural language utterances to machine-understandable meaning representations, such as logic forms or programming languages. Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application usually require additional prior knowledge to be built into the architecture or algorithm. The increased dependency on human experts hinders automation and raises the development and maintenance costs in practice. This work investigates whether a generic transformer-based seq2seq model can achieve competitive performance with minimal semantic-parsing specific inductive bias design. By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 exact match accuracy on Django and 32.57 BLEU score on CoNaLa, both are SOTA to the best of our knowledge. This positive evidence highlights a potentially easier path toward building accurate semantic parsers in the wild.
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