| 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. |
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Good-Enough Compositional Data Augmentation (2020.acl-main)
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| 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. |
Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
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| 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. |
Neural Data-to-Text Generation with LM-based Text Augmentation (2021.eacl-main)
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| Challenge: | Neural data-to-text generation is a difficult task for many new applications because of a lack of training data. |
| Approach: | They propose a few-shot approach that augments the data available for training by generating new text samples based on replacing specific values by alternative ones from the same category and pairing the new text with data samples. |
| Outcome: | The proposed approach outperforms fully supervised sequence-to-sequence models with less than 10% of the training set on both datasets. |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)
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| Challenge: | Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations. |
| Approach: | They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration. |
| Outcome: | The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores. |
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| 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. |
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources (2021.eacl-main)
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| Challenge: | a hybrid approach to lemmatization enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Approach: | They propose a hybrid approach that enhances a seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Outcome: | The proposed model achieves statistically significant improvements on 23 UD languages, compared to baseline models not utilizing additional lemma information. |
Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)
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| Challenge: | Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging. |
| Approach: | They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization. |
| Outcome: | The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations. |
Learning to Substitute Spans towards Improving Compositional Generalization (2023.acl-long)
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| 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. |
SUBS: Subtree Substitution for Compositional Semantic Parsing (2022.naacl-main)
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| Challenge: | Semantic parsing models fail at compositional generalization due to lack of reasoning ability. |
| Approach: | They propose to use subtree substitution for compositional data augmentation to increase the number of subtreas with similar semantic functions as exchangeable. |
| Outcome: | The proposed method improves performance on Scan and GeoQuery, and new SOTA on compositional split of GeoQuery. |
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks (2021.emnlp-main)
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| Challenge: | Existing neural models lack systematic compositionality in learning symbolic structures . existing models lack this ability in learning symbols, despite being able to understand complex structures. |
| Approach: | They propose to use auxiliary sequence prediction tasks to train a Transformer model to understand compositional symbolic structures of input data. |
| Outcome: | The proposed model improves on the SCAN compositionality challenge, with only 418 (5%) training instances, and achieves 97.8% accuracy on the MCD1 split. |