Recent research showed promising results on combining pretrained language models (LMs)
with canonical utterance for few-shot semantic parsing. The canonical utterance is often …

Localizing a semantic parser to support new languages requires effective cross-lingual
generalization. Recent work has found success with machine-translation or zero-shot …

The availability of source code has been put forward as one of the most critical factors for
improving the reproducibility of scientific research. This work studies trends in source code …

Although sequence-to-sequence models often achieve good performance in semantic
parsing for iid data, their performance is still inferior in compositional generalization. Several …

Semantic parsing (SP) allows humans to leverage vast knowledge resources through
natural interaction. However, parsers are mostly designed for and evaluated on English …

In recent years, significant progress has been made in semantic parsing tasks due to the
introduction of pre-trained language models. However, there remains a notable gap …

Recent years have witnessed substantial growth in Machine Learning (ML) and Natural
Language Processing (NLP), largely fueled by the accessibility and openness of data and …

Semantic parsing maps natural language utterances to logical form representations of
meaning (eg, lambda calculus or SQL). A semantic parser functions as a human-computer …

Current trends in semantic parsing primarily use large, pre-trained neural language models.
These models achieve impressive scores but also present some drawbacks. They require …