论文标题
组成以任务为导向的解析作为抽象问题回答
Compositional Task-Oriented Parsing as Abstractive Question Answering
论文作者
论文摘要
以任务为导向的解析（顶部）旨在将自然语言转换为特定任务的机器可读表示，例如设置警报。最高的一种流行方法是应用SEQ2SEQ模型生成线性化解析树。最近的一项工作认为，预验证的SEQ2SEQ模型更好地可以产生自然语言的输出，因此它们用规范的自然语言释义代替了线性化的解析树，然后可以轻松地将其轻松地翻译成解析树，从而导致所谓的属性解析器。在这项工作中，我们继续通过提出从顶级到抽象性问题回答的总体减少到克服了规范释义的局限性来探索自然化的语义解析。实验结果表明，我们的基于质量检查的技术在全DATA设置中优于最先进的方法，同时在几个弹药设置中实现了巨大的改进。
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.
