Challenge: Existing methods to train a parser to perform zero-shot learning are limited by the lack of training data.
Approach: They propose a decomposition-based method to unify the sentence structures of questions . their method can generalize to natural questions with novel text expressions .
Outcome: The proposed method improves on synthetic data and on complex web questions with novel expressions.

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Complex Question Decomposition for Semantic Parsing (P19-1)

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Challenge: Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing.
Approach: They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring.
Outcome: The proposed method improves on a large scale complex question semantic parsing dataset.
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)

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Challenge: Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents.
Approach: They propose a generative model to map natural language questions into SQL queries.
Outcome: The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset.
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization (2021.emnlp-main)

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Challenge: Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored.
Approach: They propose to generate synthetic utterance-program pairs for improving compositional generalization in semantic parsing by using structurally-diverse examples.
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Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts (2022.emnlp-main)

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Challenge: Existing approaches to end-to-end questionanswering assume that pre-trained language can decompose complex tasks into more straightforward sub-tasks.
Approach: They propose to use distant supervision to train decomposition-based transformers for large-scale parallel news.
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Is a Question Decomposition Unit All We Need? (2022.emnlp-main)

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Challenge: Large Language Models (LMs) have achieved state-of-the-art performance on many NLP benchmarks.
Approach: They propose to decompose a hard question into simpler questions that are easier for models to answer.
Outcome: The proposed approach significantly improves model performance (24% for GPT3 and 29% for RoBERTa-SQuAD along with a symbolic calculator) by decomposing a hard question into simpler questions that are easier for models to answer.
Semantic Decomposition of Question and SQL for Text-to-SQL Parsing (2023.findings-emnlp)

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Challenge: Existing text-to-SQL models for complex queries are limited by the syntactic complexity of SQL.
Approach: They propose a question decomposition language that decomposes SQL queries into simple and regular sub-queries.
Outcome: The proposed language decomposes SQL queries into simple and regular sub-queries . it is more accessible to non-experts for complex queries, enabling interpretable output .
Generating Complex Question Decompositions in the Face of Distribution Shifts (2025.naacl-long)

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Challenge: Question decomposition has been found to improve large language models’ (LLMs) performance on complex question answering (QA) however, performance on the task remains dominated by supervised approaches, suggesting room for making LLMs better decomposers.
Approach: They propose to generate synthetic decomposition data with only five annotated examples by extending recent advances in using LLM-as-judge and for reranking in novel ways.
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Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)

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Challenge: Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data.
Approach: They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing.
Outcome: The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task.
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)

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Challenge: Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data.
Approach: They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions .
Outcome: The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data.
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)

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Challenge: Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance .
Approach: They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation .
Outcome: The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks.

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