| Challenge: | a proposed data augmentation protocol provides a compositional inductive bias in conditional and unconditional sequence models. |
| Approach: | They propose a data augmentation protocol that provides a compositional inductive bias in conditional and unconditional sequence models by replacing discontinuous fragments with other fragments that appear in at least one similar environment. |
| Outcome: | The proposed protocol reduces error rate by 87% on diagnostic tasks and 16% on semantic parsing tasks. |
Similar Papers
LexSym: Compositionality as Lexical Symmetry (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to generalize compositional models fail to generalise from small datasets. |
| Approach: | They propose a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. |
| Outcome: | The proposed procedure matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and Alchemy instruction following, and CLEVR-CoGenT visual question answering datasets. |
Sequence-Level Mixed Sample Data Augmentation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. |
| Approach: | They propose a data augmentation approach to encourage compositional behavior in neural networks . they propose to softly combine input/output sequences from the training set . |
| Outcome: | The proposed approach yields 1.0 BLEU improvement on translation datasets over baselines. |
Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
Copied to clipboard
| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
| Approach: | They propose a method to recombinate examples from a model called Compositional Structure Learner and add them to a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed model is even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. |
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
Copied to clipboard
| Challenge: | Compositional generalization is the ability of a system to correctly predict the meaning of complex sentences when trained on simpler sentences. |
| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
| Outcome: | The proposed method outperforms existing methods that sampled from the training distribution and outperformed existing methods. |
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
Copied to clipboard
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Learning to Substitute Spans towards Improving Compositional Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | despite the rising prevalence of neural sequence models, there is a deficiency in compositional generalization. |
| Approach: | They propose a compositional augmentation strategy that enables multi-grained composition of substructures in the whole training set. |
| Outcome: | The proposed strategy outperforms existing strategies on three compositional generalization benchmarks. |
Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation. |
| Approach: | They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
| Outcome: | The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent datasets expose the lack of systematic generalization ability in standard sequence-to-sequence models. |
| Approach: | They propose two techniques to address the lack of systematic generalization ability in standard sequence-to-sequence models by mutual exclusivity training and prim2primX data augmentation. |
| Outcome: | The proposed methods improve on two widely-used compositionality datasets. |
Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)
Copied to clipboard
| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |