# Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction arXiv: https://arxiv.org/abs/2110.08345 ยท HTML: https://mlsanity.com/paper/2110.08345/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 64 references that are in the map. - First posted on arXiv 15 Oct 2021. ## Builds on (most cited first, 30 of 33) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [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) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018) - [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018) - [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018) - [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019) - [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016) - [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021) - [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019) - [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019) - [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020) - [SParC: Cross-Domain Semantic Parsing in Context](https://mlsanity.com/paper/1906.02285/) (2019) - [ParlAI: A Dialog Research Software Platform](https://mlsanity.com/paper/1705.06476/) (2017) - [Break It Down: A Question Understanding Benchmark](https://mlsanity.com/paper/2001.11770/) (2020) - [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020) - [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://mlsanity.com/paper/1906.02916/) (2019) - [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021) - [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021) - [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018) - [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020) - [Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study](https://mlsanity.com/paper/1910.05389/) (2019) - [UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering](https://mlsanity.com/paper/1904.01246/) (2019) - [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](https://mlsanity.com/paper/1603.06807/) (2016) - [NL-EDIT: Correcting semantic parse errors through natural language interaction](https://mlsanity.com/paper/2103.14540/) (2021) - [Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks](https://mlsanity.com/paper/2004.06015/) (2020) - [Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types](https://mlsanity.com/paper/1802.06842/) (2018) - [Multi-Channel Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2108.12953/) (2021) - [Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1808.06740/) (2018) ## Built on by (most cited first, 9 of 9) - [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022) - [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023) - [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022) - [Text-to-SQL Error Correction with Language Models of Code](https://mlsanity.com/paper/2305.13073/) (2023) - [Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations](https://mlsanity.com/paper/2305.07372/) (2023) - [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023) - [Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL](https://mlsanity.com/paper/2205.12422/) (2022) - [Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System](https://mlsanity.com/paper/2307.16081/) (2023) - [Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding](https://mlsanity.com/paper/2305.19974/) (2023) ## Abstract (arXiv) Existing studies on semantic parsing focus primarily on mapping a natural-language utterance to a corresponding logical form in one turn. However, because natural language can contain a great deal of ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework that explains the predicted logical form step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user appropriately trust the final answer. To do so, we construct INSPIRED, a crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset. Our experiments show that the interactive framework with human feedback has the potential to greatly improve overall parse accuracy. Furthermore, we develop a pipeline for dialogue simulation to evaluate our framework w.r.t. a variety of state-of-the-art KBQA models without involving further crowdsourcing effort. The results demonstrate that our interactive semantic parsing framework promises to be effective across such models.