Compositional Task-Oriented Parsing as Abstractive Question Answering
| Autor: | Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, Claire Cardie |
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| Rok vydání: | 2022 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2205.02068 |
| Popis: | 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. Comment: accepted at NAACL'22 |
| Databáze: | OpenAIRE |
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