Papers with Winograd Schema Challenge
A Hybrid Neural Network Model for Commonsense Reasoning (D19-60)
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| Challenge: | a hybrid neural network (HNN) model for commonsense reasoning is proposed . it combines language models and semantic similarity models to achieve new state-of-the-art results . |
| Approach: | They propose a hybrid neural network model for commonsense reasoning . it combines a masked language model and a semantic similarity model . |
| Outcome: | The proposed model outperforms the WNLI, WSC and PDP60 benchmarks on three commonsense reasoning tasks. |
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge (N18-4)
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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 . |
| Approach: | They propose an automatic system that performs well on two common-sense reasoning tasks. |
| Outcome: | The proposed system improves performance on the Winograd Schema Challenge and COPA by 0.16 over the previous best. |
InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
| Approach: | They present an interactive visualization tool for interpreting Transformer-based models. |
| Outcome: | The tool can track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeds, and identify task-related functions of attention heads using new metrics. |
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction (2025.naacl-short)
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| Challenge: | Recent research has revealed that Large Language Models (LLMs) often experience issues with hallucinations and unreliable reasoning due to semantic associations and superficial logical chains. |
| Approach: | They propose a concept-reversed Winograd Schema Challenge dataset to evaluate the robustness of Large Language Models (LLMs) they propose Abstraction-of-Thought (AoT) method for recovering adversarial cases to normal cases using conceptual abstraction to improve LLMs’ robustness and consistency in reasoning. |
| Outcome: | The proposed method improves LLMs’ robustness and consistency in reasoning under adversarial and long-tail scenarios. |
Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge (2024.acl-long)
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| Challenge: | Large Language Models have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. |
| Approach: | They propose a framework to isolate models' ability in pronoun disambiguation from other visual processing challenges. |
| Outcome: | The proposed framework isolates the models’ ability in pronoun disambiguation from other visual processing challenges. |
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition (P19-1)
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| Challenge: | Selectional Preference (SP) is a common phenomenon in human language and has been shown to be useful in many natural language processing tasks. |
| Approach: | They propose a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
| Outcome: | The proposed evaluation sets provide human ratings for plausibility of 10,000 SP pairs over five SP relations covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts (2024.eacl-long)
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| Challenge: | Large Language Models (LLMs) excel at answering WSC questions, but their ability to generate such questions remains less explored. |
| Approach: | They propose a tree-of-experts prompting method which enhances the generation of WSC instances by incorporating new 'ambiguous' and 'offensive' categories. |
| Outcome: | The proposed method enhances the generation of WSC instances (50% valid cases vs. 10% in recent methods) it extends the framework by incorporating new ‘ambiguous’ and ‘offensive’ categories, providing a deeper insight into model overconfidence and bias. |
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations (2022.emnlp-main)
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| Challenge: | Recent work on explainable NLP has shown that few-shot prompting can enable large pre-trained language models (LLMs) to generate grammatical and factual explanations for data labels. |
| Approach: | They propose to use few-shot prompting to generate grammatical and factual explanations for data labels by varying the hardness of the test samples and in-context samples to investigate the link between explainability and sample hardness. |
| Outcome: | The proposed model can generate grammatical and factual explanations for data labels with few-shot prompting. |
Attention-based Contrastive Learning for Winograd Schemas (2021.findings-emnlp)
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| Challenge: | Existing approaches to learn discriminative features using contrastive objective are lacking. |
| Approach: | They propose a self-supervised framework that leverages a contrastive loss directly at the level of self-attention. |
| Outcome: | The proposed framework outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones. |
A Knowledge Hunting Framework for Common Sense Reasoning (D18-1)
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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 . |
| Approach: | They propose an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge . they use a knowledge hunting module to gather web text for problem resolutions . |
| Outcome: | The proposed system achieves state-of-the-art on the Winograd Schema Challenge . it improves F1 performance on the full WSC by 0.21 over the previous best . |
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. |
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG (D19-1)
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| Challenge: | a recent study has improved the state-of-the-art on common-sense reasoning benchmarks . a san francisco-based approach to common-ense reasoning is challenging . |
| Approach: | They propose to use common-sense reasoning benchmarks to test machine learning's common-sentence inference task SWAG to test common-mind systems. |
| Outcome: | a new study shows that improved performance on common-sense reasoning benchmarks is genuine . the proposed task is more difficult than the current one, but it is more efficient than the previous one. |
Attention Is (not) All You Need for Commonsense Reasoning (P19-1)
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| Challenge: | Recent language models such as word2vec have produced impressive results on various tasks such as question-answering and natural language inference. |
| Approach: | They propose a simple re-implementation of BERT for commonsense reasoning . they propose to use attention-guided reasoning to solve the Pronoun Disambiguation Problem . |
| Outcome: | The proposed model outperforms the state-of-the-art on several language understanding benchmarks while outperforming the existing models by a margin. |
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. |
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 . |
An Analysis of Dataset Overlap on Winograd-Style Tasks (2020.coling-main)
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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 . |
| Approach: | They analyze the effects of varying degrees of overlaps that occur between pretraining corpora and test instances in WSC-style tasks. |
| Outcome: | The WSC-Web dataset is the largest to date and has lower overlaps with current pretraining corpora. |
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. |
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. |
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. |
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. |
Causal interventions expose implicit situation models for commonsense language understanding (2023.findings-acl)
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| Challenge: | Classical psycholinguistic accounts have suggested that world knowledge enters into language understanding through structured schemas called situation models. |
| Approach: | They apply causal intervention techniques to transformer models to analyze performance on the Winograd Schema Challenge . |
| Outcome: | The proposed model performs well on the Winograd Schema Challenge . |