Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches

By Shane Storks, Qiaozi Gao, Joyce Yue Chai. 2019

80 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yejin Choi
2. Xiang Ren
3. Mohit Bansal
4. Percy Liang
5. Pei Zhou
6. Yoshua Bengio
7. Ninareh Mehrabi
8. Zhe Gan

Citations in the corpus

• Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021, background): “Injecting commonsense knowledge into different NLP tasks is gaining significant attention recently (Storks et al., 2019).”
• Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules (2021, background): “Refer to Storks et al. (2019) and Arabshahi et al. (2021) for a comprehensive list of commonsense reasoning benchmarks.”
• Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering (2020, background): “A corpus, however, might not be an ideal source of commonsense knowledge, as such knowledge is seldom stated explicitly in text (Storks et al., 2019).”
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity (2023, background): “Commonsense Reasoning It is understanding and reasoning about everyday concepts and knowledge that most people are familiar with, to make judgments and predictions about new situations (Storks et al., 2019).”
• Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods (2019, background): “, 2015), provided comprehensive overview of only natural language processing tasks such as natural language generation (NLG) (Gatt & Krahmer, 2018) and commonsense reasoning (Storks et al., 2019).”
• Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension (2019, background): “First, we follow previous work (Sun et al., 2019b) for English question answering tasks.”
• Reasoning with Language Model Prompting: A Survey (2022, background): “Commonsense knowledge and commonsense reasoning are some of the major issues in machine intelligence (Storks et al., 2019; Bhargava and Ng, 2022).”