BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples (2022.findings-emnlp)
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| Challenge: | Biomedical research has progressed at a tremendous pace, with PubMed2 indexing well over 1M publications per year in the past eight years. |
| Approach: | They propose a semi-supervised procedure that bootstraps biomedical NLI datasets from positive entailment examples present in biomedically published texts. |
| Outcome: | The proposed procedure bootstraps biomedical NLI datasets from positive entailment examples from biomedically challenging texts. |
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Lessons from Natural Language Inference in the Clinical Domain (D18-1)
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| Challenge: | State of the art models with deep neural networks lack generalization capabilities in specialized domains where training data is limited. |
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| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
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MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing (2025.findings-naacl)
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Generating Natural Language Adversarial Examples (D18-1)
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| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
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BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification (2021.naacl-main)
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| Challenge: | Recent efforts to generate adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever-increasing biomedical literature poses unique challenges. |
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BioReddit: Word Embeddings for User-Generated Biomedical NLP (D19-62)
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| Challenge: | a corpus of medical-themed posts was scrapped from Reddit to train word embeddings on downstream tasks. |
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Hy-NLI: a Hybrid system for Natural Language Inference (2020.coling-main)
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| Challenge: | Recent work has revealed the generalization difficulties of deep models . however, such models fail to perform well on adversarial datasets with challenging linguistic phenomena. |
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AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (P18-1)
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| Challenge: | Recent deep learning entailment systems have achieved close to human level performance on large datasets, but the problem is far from solved. |
| Approach: | They propose a knowledge-guided adversarial example generator for incorporating large lexical resources into entailment models via only a handful of rule templates and a natural language example generator that iteratively adjusts to the discriminator’s weaknesses. |
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