# Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

arXiv: https://arxiv.org/abs/1601.06068 · HTML: https://mlsanity.com/paper/1601.06068/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2016.

## Built on by (most cited first, 5 of 5)

- [Focus Attention: Promoting Faithfulness and Diversity in Summarization](https://mlsanity.com/paper/2105.11921/) (2021)
- [ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization](https://mlsanity.com/paper/2201.05313/) (2022)
- [Knowledge Base Question Answering for Space Debris Queries](https://mlsanity.com/paper/2305.19734/) (2023)
- [Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation](https://mlsanity.com/paper/2301.02275/) (2023)
- [Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising](https://mlsanity.com/paper/2302.02780/) (2023)

## Abstract (arXiv)

One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser significantly improves over strong baselines.