CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (N19-1)
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| Challenge: | Recent work on question answering relies on factoid questions with little general knowledge. |
| Approach: | They propose a dataset to capture commonsense question answering with prior knowledge . they extract multiple-choice questions that discriminate between the source and target concepts . |
| Outcome: | The proposed dataset captures commonsense reasoning beyond associations . it obtains 56% accuracy, well below human performance, which is 89% . |
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| Challenge: | Existing commonsense reasoning datasets target different knowledge types, modalities, and formats, but how to help machines acquire and infer over commonsensical knowledge is still unclear. |
| Approach: | They propose a commonsense reasoning benchmark to motivate commonsensing progress from two perspectives: (1) Evaluating whether models can distinguish knowledge quality by predicting if the knowledge is enough to answer the question or not. |
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Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources (2020.coling-main)
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| Challenge: | Existing methods to facilitate natural language understanding rarely involve commonsense or background knowledge. |
| Approach: | They propose a question-answering method that integrates multiple knowledge sources to boost performance. |
| Outcome: | The proposed method outperforms other competing methods on the CommonsenseQA dataset and achieves the new state-of-the-art. |
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (2020.emnlp-main)
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| Challenge: | Existing question answering datasets for common sense reasoning are lacking for prototypical situations. |
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Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
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Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)
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| Challenge: | Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts. |
| Approach: | They propose to use external entity descriptions to provide contextual information for commonsense question answering models. |
| Outcome: | The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field. |
Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)
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| Challenge: | Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching. |
| Approach: | They propose a task of commonsense question generation that aims to yield deep-level questions from the text. |
| Outcome: | The proposed model can yield deep-level and to-the-point questions from the text. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
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Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)
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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
| Challenge: | a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA) |
| Approach: | They propose to retrieve and generate explanations for a given question, correct answer choice, incorrect answer choices tuple from a dataset called CommonsenseQA. |
| Outcome: | The proposed model beats baseline model by 100% in F1 score and similarity score of 61.9 . |
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (D19-60)
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| Challenge: | Recent approaches on non-extractive commonsense QA show increased performance . attention-based injection seems to be preferable for knowledge integration . |
| Approach: | They propose to use attention-based injection to integrate knowledge into commonsense QA models. |
| Outcome: | The proposed methods show that attention-based injection is preferable for knowledge integration, and that the degree of domain overlap plays a crucial role in determining model success. |
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). |