Summary of Recent Advances in Multi-choice Machine Reading Comprehension: a Survey on Methods and Datasets, by Shima Foolad et al.
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets
by Shima Foolad, Kourosh Kiani, Razieh Rastgoo
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper provides an exhaustive examination of recent advancements in the field of multi-choice Machine Reading Comprehension (MRC). The study focuses on benchmark datasets, methodologies, challenges, and future directions to offer researchers a comprehensive overview of the current landscape. The analysis categorizes 30 existing cloze-style and multiple-choice MRC benchmark datasets based on attributes like corpus style, domain, complexity, context style, question style, and answer style. This refined classification method enhances understanding of each dataset’s diverse attributes. Furthermore, the paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods. Fine-tuned methods adapt pre-trained language models to a specific task through retraining on domain-specific datasets, while prompt-tuned methods use prompts to guide response generation, presenting potential applications in zero-shot or few-shot learning scenarios. The study aims to contribute to ongoing discussions, inspire future research directions, and foster innovations, ultimately propelling multi-choice MRC towards new frontiers of achievement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers are getting better at reading and understanding text. It focuses on a specific type of question that asks you to choose the correct answer from several options. The researchers looked at 30 different datasets that test this ability and categorized them based on things like what kind of texts they use, what topics they cover, and how hard the questions are. They also talked about two main ways that computers can get better at answering these questions: fine-tuning their language models or using prompts to help them understand what’s being asked. |
Keywords
» Artificial intelligence » Classification » Few shot » Fine tuning » Prompt » Zero shot
