| Challenge: | Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness. |
| Approach: | They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data. |
| Outcome: | The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods. |
Similar Papers
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)
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| Challenge: | Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness. |
| Approach: | They propose a retrieval-augmented framework for creating diverse counterfactual perturbations for CDA. |
| Outcome: | Experiments on natural language inference and sentiment analysis show that the proposed framework can be used to encourage diversity in manually authored perturbations. |
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation (2022.findings-emnlp)
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| Challenge: | Existing approaches to produce counterfactuals rely on small perturbations via minimal edits, resulting in simplistic changes. |
| Approach: | They propose a novel approach to produce counterfactuals that allow for larger edits and linguistic diversity while still bearing similarity to the original document. |
| Outcome: | The proposed approach outperforms existing methods for generalizing natural language models under select settings. |
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing approaches to improve performance of deep neural models are limited by the nature of spurious patterns in the data. |
| Approach: | They propose to use augmented data to generate spurious patterns in NLP models . they propose to generate counterfactual data for data augmentation and explanation . |
| Outcome: | The proposed approach improves performance on augmented data and on human-generated data. |
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)
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| Challenge: | Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health. |
| Approach: | They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference. |
| Outcome: | The proposed method outperforms baselines on multiple downstream tasks. |
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration (2024.eacl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable generalization capabilities, performing well on various tasks such as question answering (QA), complex reasoning, and code generation. |
| Approach: | They propose to augment training data of smaller language models with automatically generated counterfactuals (CF) instances to improve out-of-domain (OOD) performance of SLMs in extractive question answering setup. |
| Outcome: | The proposed approach improves out-of-domain (OOD) performance of small language models in extractive question answering setup. |
A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models (2021.acl-long)
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| Challenge: | Existing counterfactual generation methods rely on manual labor to create very few counterf actuals or only instantiate limited types of perturbations such as paraphrases or word substitutions. |
| Approach: | They propose a general-purpose counterfactual generator that allows for control over perturbation types and locations. |
| Outcome: | The proposed generator produces diverse sets of realistic counterfactuals that are useful in various applications. |
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (2022.emnlp-demos)
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
| Approach: | They propose a pipeline for validating and training QA data and an interface for human annotation. |
| Outcome: | The proposed pipeline improves QA performance on domain-specific datasets while preserving the accuracy of the model. |
Counterfactual Off-Policy Training for Neural Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing models for open-domain dialogue generation suffer from data insufficiency . a potential response inferred in hindsight is called a counterfactual reasoning . |
| Approach: | They propose to explore potential responses by counterfactual reasoning . given an observed response, the model automatically infers the outcome of an alternative policy that could have been taken . |
| Outcome: | The proposed model outperforms the HRED model and conventional learning frameworks on the DailyDialog dataset. |
Leveraging QA Datasets to Improve Generative Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent advances in NLP have substantially improved the capability of pretrained language models to generate high-quality text. |
| Approach: | They propose to reformulate data generation as context generation for a given question-answer (QA) pair and leverage QA datasets for training context generators. |
| Outcome: | The proposed approach improves performance for few-shot and zero-shot classification datasets on multiple classification dataset. |