SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (D18-1)
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| Challenge: | a new dataset presents a task of grounded commonsense inference, unifying natural language inference and commonsensical reasoning. |
| Approach: | They propose a procedure that constructs a de-biased dataset by iteratively training stylistic classifiers and using them to filter the data. |
| Outcome: | The proposed procedure oversamples a de-biased dataset using state-of-the-art language models . human models struggle on the proposed procedure, indicating significant opportunities for future research. |
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Annotation Artifacts in Natural Language Inference Data (N18-2)
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| Challenge: | Large-scale datasets for natural language inference are created by crowdsourcing annotations . authors show that success of natural language models to date has been overestimated . |
| Approach: | They propose a method for crowdsourcing annotations to generate 3 new sentences based on a sentence (premise) they show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI and 53% of MultiNLI . |
| Outcome: | The proposed model can classify the hypothesis alone in 67% of SNLI and 53% of MultiNLI datasets. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
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| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
| Approach: | They propose a large-scale NLI benchmark dataset that is iteratively compared with a human-and-model-in-the-loop procedure. |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
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| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
| Outcome: | The first workshop on crowdsourcing for NLP received 16 submissions and accepted 7 . the workshop will focus on ambiguous, subjective or ambiguity analysis of crowdsourced data . |
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
| Approach: | They examine linguistic background to craft plausible adversarial examples that expose biases in popular NLP models. |
| Outcome: | The proposed model improves robustness without sacrificing performance on clean data. |
HellaSwag: Can a Machine Really Finish Your Sentence? (P19-1)
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| Challenge: | Existing commonsense models struggle to perform inferences that are trivial for humans, but are often misclassified by state-of-the-art models. |
| Approach: | They propose a dataset that is adversarial to state-of-the-art commonsense reasoning and use it to build a model that is surprisingly robust. |
| Outcome: | The proposed dataset is compared with existing models and scaled up towards a critical 'Goldilocks zone' wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. |
Natural Language Annotations for Reasoning about Program Semantics (2023.findings-emnlp)
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| Challenge: | Xu et al., 2022) and Tafjord eet . al. 2021) have shown that programming assistants can explain their work by grounding natural language inference in code. |
| Approach: | They propose a dataset and protocol for annotating programs with natural language predicates at a finer granularity than code comments without relying on internal compiler representations. |
| Outcome: | The proposed method can be used to ground natural language inference in code without static analysis and without internal compiler representations. |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |