# Few-Shot Semantic Parsing for New Predicates

arXiv: https://arxiv.org/abs/2101.10708 · HTML: https://mlsanity.com/paper/2101.10708/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- Builds on 39 of its 62 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 30 of 39)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Improving Text-to-SQL Evaluation Methodology](https://mlsanity.com/paper/1806.09029/) (2018)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://mlsanity.com/paper/1909.00786/) (2019)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [Deriving Machine Attention from Human Rationales](https://mlsanity.com/paper/1808.09367/) (2018)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Natural Language to Structured Query Generation via Meta-Learning](https://mlsanity.com/paper/1803.02400/) (2018)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [Neural Machine Translation with Supervised Attention](https://mlsanity.com/paper/1609.04186/) (2016)
- [Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions](https://mlsanity.com/paper/1710.10304/) (2017)
- [Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing](https://mlsanity.com/paper/1908.09940/) (2019)
- [Grammar-based Neural Text-to-SQL Generation](https://mlsanity.com/paper/1905.13326/) (2019)
- [StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing](https://mlsanity.com/paper/1806.07832/) (2018)
- [Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing](https://mlsanity.com/paper/1809.00773/) (2018)
- [Cross-domain Semantic Parsing via Paraphrasing](https://mlsanity.com/paper/1704.05974/) (2017)
- [Learning Programmatic Idioms for Scalable Semantic Parsing](https://mlsanity.com/paper/1904.09086/) (2019)

## Built on by (most cited first, 7 of 7)

- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [WildQA: In-the-Wild Video Question Answering](https://mlsanity.com/paper/2209.06650/) (2022)
- [Paraphrasing Techniques for Maritime QA system](https://mlsanity.com/paper/2203.10854/) (2022)
- [Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach](https://mlsanity.com/paper/2312.11276/) (2023)
- [When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems](https://mlsanity.com/paper/2205.12228/) (2022)

## Abstract (arXiv)

In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with utterance-logical form pairs per new predicate. The state-of-the-art neural semantic parsers achieve less than 25% accuracy on benchmark datasets when k= 1. To tackle this problem, we proposed to i) apply a designated meta-learning method to train the model; ii) regularize attention scores with alignment statistics; iii) apply a smoothing technique in pre-training. As a result, our method consistently outperforms all the baselines in both one and two-shot settings.