Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge (P19-1)
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| Challenge: | Existing methods to solve Winograd Schema Challenge use only knowledge embedded in text . this limits the performance of such models on the WSC problems. |
| Approach: | They propose to augment existing language models with a commonsense knowledge hunting module and an explicit reasoning module to extract the needed knowledge from text. |
| Outcome: | The proposed system improves on the language model based methods by 5.53% and 7.7% on the dataset. |
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| Challenge: | a new system that performs well on common-sense reasoning tasks is developed . the Winograd Schema Challenge (WSC) is a popular alternative to the Turing test . |
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| Challenge: | a new task is developed to categorize essential commonsense knowledge for the Winograd Schema Challenge . a previous work focused on the classification of commonsensical knowledge by pre-trained models . |
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| Challenge: | a new system that uses common sense to solve a common sense problem is developed . a winograd schema challenge and a choice of plausible alternatives are popular tests . |
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| Challenge: | Large Language Models excel at the Winograd Schema Challenge, but struggle with instances that feature minor alterations or rewording. |
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| Challenge: | Recent success of neural language models on the Winograd Schema Challenge has called for further investigation of commonsense reasoning ability of these models. |
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| Challenge: | The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. |
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| Challenge: | Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases. |
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| Challenge: | a large number of test instances overlap considerably with pretraining corpora, a study finds . for a number of years, models struggled to exceed chance-level performance . |
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Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema (2021.emnlp-main)
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| Challenge: | Pre-trained language models have boosted performance on some WS benchmarks, but the source of improvement is not clear. |
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