nlp:compositional_generalization

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Table of Contents

Compositional Generalization

Compositional Generalization

Papers

SCAN dataset: Lake & Baroni 2017 - Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Chang et al 2018 - Automatically Composing Representation Transformations as a Means for Generalization Has a good section on what compositional generalization is. Positive results for curriculum learning
Lake 2019 - Compositional generalization through metasequence-to-sequence learning
COGS dataset: Kim & Linzen 2020 - COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
2019 - Measuring Compositional Generalization: A Comprehensive Method on Realistic Data Introduces MCD data split method
Chen et al 2020 - Compositional Generalization via Neural-Symbolic Stack Machines
Herzig & Berant 2020 - Span-based Semantic Parsing for Compositional Generalization
Shaw et al 2020 - Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? Code and data

Papers using Curriculum Learning

2020 - Compositional Generalization by Learning Analytical Expressions

Non-NLP Papers

Klinger et al 2020 - A Study of Compositional Generalization in Neural Models Has some negative results for curriculum learning

Datasets

Semantic Parsing
- SCAN dataset paper. Also introduces a machine translation dataset for compositional generalization
- COGS dataset paper
CFQ dataset paper
Question Answering
- ComQA: Compositional Question-Answering dataset paper
Machine Translation
- Small-scale MT dataset from the SCAN dataset paper
- CoGnition dataset paper

Related Pages

Curriculum Learning (often useful for compositional generalization)
Effects of the Random Seed

nlp/compositional_generalization.1635968734.txt.gz · Last modified: 2023/06/15 07:36 (external edit)