SUBS: Subtree Substitution for Compositional Semantic Parsing

05/03/2022
by   Jingfeng Yang, et al.
0

Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Several data augmentation methods have been proposed to alleviate this problem. However, prior work only leveraged superficial grammar or rules for data augmentation, which resulted in limited improvement. We propose to use subtree substitution for compositional data augmentation, where we consider subtrees with similar semantic functions as exchangeable. Our experiments showed that such augmented data led to significantly better performance on SCAN and GeoQuery, and reached new SOTA on compositional split of GeoQuery.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/14/2021

Improving Compositional Generalization with Latent Structure and Data Augmentation

Generic unstructured neural networks have been shown to struggle on out-...
research
11/18/2020

Sequence-Level Mixed Sample Data Augmentation

Despite their empirical success, neural networks still have difficulty c...
research
07/25/2023

Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm

In this paper, we conduct a holistic exploration of the Universal Decomp...
research
06/05/2023

Learning to Substitute Spans towards Improving Compositional Generalization

Despite the rising prevalence of neural sequence models, recent empirica...
research
04/21/2019

Good-Enough Compositional Data Augmentation

We propose a simple data augmentation protocol aimed at providing a comp...
research
03/16/2022

Structurally Diverse Sampling Reduces Spurious Correlations in Semantic Parsing Datasets

A rapidly growing body of research has demonstrated the inability of NLP...
research
01/30/2022

Compositionality as Lexical Symmetry

Standard deep network models lack the inductive biases needed to general...

Please sign up or login with your details

Forgot password? Click here to reset