# Compositional Task-Oriented Parsing as Abstractive Question Answering

arXiv: https://arxiv.org/abs/2205.02068 · HTML: https://mlsanity.com/paper/2205.02068/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 22 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Constrained Language Models Yield Few-Shot Semantic Parsers](https://mlsanity.com/paper/2104.08768/) (2021)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018)
- [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017)
- [Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2010.03546/) (2020)
- [Dialog State Tracking: A Neural Reading Comprehension Approach](https://mlsanity.com/paper/1908.01946/) (2019)
- [Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2001.11458/) (2020)
- [Conversational Semantic Parsing](https://mlsanity.com/paper/2009.13655/) (2020)
- [Language Model is All You Need: Natural Language Understanding as Question Answering](https://mlsanity.com/paper/2011.03023/) (2020)
- [FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models](https://mlsanity.com/paper/2109.01951/) (2021)
- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2104.07275/) (2021)
- [Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling](https://mlsanity.com/paper/2104.07224/) (2021)
- [Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder](https://mlsanity.com/paper/2109.04500/) (2021)
- [Training Naturalized Semantic Parsers with Very Little Data](https://mlsanity.com/paper/2204.14243/) (2022)

## Built on by (most cited first, 10 of 10)

- [FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering](https://mlsanity.com/paper/2305.04329/) (2023)
- [HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models](https://mlsanity.com/paper/2208.08232/) (2022)
- [FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering](https://mlsanity.com/paper/2306.05523/) (2023)
- [InstructExcel: A Benchmark for Natural Language Instruction in Excel](https://mlsanity.com/paper/2310.14495/) (2023)
- [Semantic Decomposition of Question and SQL for Text-to-SQL Parsing](https://mlsanity.com/paper/2310.13575/) (2023)
- [ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models](https://mlsanity.com/paper/2212.10815/) (2022)
- [Dense Retrieval as Indirect Supervision for Large-space Decision Making](https://mlsanity.com/paper/2310.18619/) (2023)
- [kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning](https://mlsanity.com/paper/2312.10771/) (2023)
- [Low-Resource Compositional Semantic Parsing with Concept Pretraining](https://mlsanity.com/paper/2301.09809/) (2023)
- [Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases](https://mlsanity.com/paper/2312.14798/) (2023)

## Abstract (arXiv)

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.