  Task-oriented parsing (TOP) aims to convert natural language into
machine-readable representations of specific tasks, such as setting an alarm. A
popular approach to TOP is to apply seq2seq models to generate linearized parse
trees. A more recent line of work argues that pretrained seq2seq models are
better at generating outputs that are themselves natural language, so they
replace linearized parse trees with canonical natural-language paraphrases that
can then be easily translated into parse trees, resulting in so-called
naturalized parsers. In this work we continue to explore naturalized semantic
parsing by presenting a general reduction of TOP to abstractive question
answering that overcomes some limitations of canonical paraphrasing.
Experimental results show that our QA-based technique outperforms
state-of-the-art methods in full-data settings while achieving dramatic
improvements in few-shot settings.
