Challenge: Winograd schemas are well-established tools for evaluating coreference resolution and commonsense reasoning capabilities of computational models.
Approach: They present a dataset of German, French, and Russian schemas aligned with their English counterparts.
Outcome: The proposed model improves in English and German, while the model improve in other languages.

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A Surprisingly Robust Trick for the Winograd Schema Challenge (P19-1)

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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.
Approach: They propose to fine-tune language models on the Winograd Schema Challenge dataset WSC273 and its inference counterpart WNLI to achieve accuracies of 72.5% and 74.7%, respectively.
Outcome: The proposed language models achieve 72.5% and 74.7% accuracy on the WSC273 and WNLI datasets, respectively.
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.
Approach: They propose a method that uses twin sentences for evaluation and two new baselines that account for artifacts in WS benchmarks.
Outcome: The proposed evaluation method is suboptimal for the Winograd Schema . it uses twin sentences to account for commonsense reasoning abilities .
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge (2021.emnlp-main)

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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.
Approach: They propose a logic-based framework that focuses on high-quality commonsense knowledge.
Outcome: The proposed framework focuses on high-quality commonsense knowledge.
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries (2024.lrec-main)

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Challenge: Large Language Models excel at the Winograd Schema Challenge, but struggle with instances that feature minor alterations or rewording.
Approach: They propose an open-source platform that harnesses a human-in-the-loop approach to create a dynamic dataset tailored to such altered WSC instances.
Outcome: The proposed model outperforms existing models in the Winograd Schema Challenge (WSC) a human-in-the-loop approach allows for a dynamic dataset tailored to such altered instances.
Precise Task Formalization Matters in Winograd Schema Evaluations (2020.emnlp-main)

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Challenge: Recent results report a surge in performance to nearhuman levels on the Winograd Schema Challenge (WSC) however, variations in task formulation across papers and evaluations makes it hard to understand the true degree of recent progress.
Approach: They propose to use a model with multiple choice to frame the task as multiple choice and reuse a pretrained language modeling head to mitigate the model's extreme sensitivity to hyperparameters.
Outcome: The proposed frameworks improve the model's reasoning ability by framing the task as multiple choice and reuse of a pretrained language modeling head.
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.
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge (2020.acl-main)

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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 .
Approach: They propose a task that categorizes essential commonsense knowledge for the Winograd Schema Challenge . they leverage the collected reasons to identify plausible reasons from wrong ones .
Outcome: The proposed task aims to shed light on commonsense knowledge needed for better reasoning . it is based on the collected reasons and a new task called WinoWhy .
Investigating Multilingual Coreference Resolution by Universal Annotations (2023.findings-emnlp)

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Challenge: Existing systems for multilingual coreference resolution have been challenging due to linguistic diversity and complexity of different languages.
Approach: They propose a multilingual coreference dataset with universal morphosyntactic and coreference annotations.
Outcome: The proposed dataset improves the baseline system by 0.9% . the proposed dataset is based on the framework of Universal Dependencies 2 .
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations (2020.acl-main)

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Challenge: Large-scale pre-trained language models are driving recent improvements in perfromance on the Winograd Schema Challenge . a diagnostic dataset shows that these models are sensitive to linguistic perturbations that minimally affect human understanding .
Approach: They propose to use a dataset to test pre-trained language models for the Winograd Schema Challenge . they show that these models are sensitive to linguistic perturbations that minimally affect human understanding .
Outcome: The proposed models are sensitive to linguistic perturbations that minimally affect human understanding.
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models (2021.emnlp-main)

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Challenge: Existing language models can be refined for zero-shot commonsense reasoning . however, commons sense reasoning is still an unsolved problem .
Approach: They propose a self-supervised learning approach that refines a pre-trained language model to boost conceptualization.
Outcome: The proposed approach boosts conceptualization by utilizing loss landscape refinement.

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