Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension (C18-1)
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| Challenge: | Recent studies have shown that cloze-style reading comprehension is a popular task for measuring the progress of natural language understanding. |
| Approach: | They propose a multi-perspective framework which can be seen as joint training of heterogeneous experts and aggregate context information from different perspectives. |
| Outcome: | The proposed framework achieves new state-of-the-art over previous strong baselines on a recently released cloze-test dataset. |
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| Challenge: | a new model for reading comprehension integrates external commonsense knowledge . cloze-style reading comprehension is a language understanding task similar to question answering . |
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
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| Challenge: | Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue . |
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Cloze-driven Pretraining of Self-attention Networks (D19-1)
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| Challenge: | Existing work on pretraining language models has used unidirectional (left-to-right) or bi-directional (both left-to right and right-to left) LMs with loss function. |
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ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
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| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
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PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)
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| Challenge: | Experimental results show that the main challenge lies in long context and perspective extraction. |
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Pedagogically Aligned Objectives Create Reliable Automatic Cloze Tests (2024.naacl-long)
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| Challenge: | a recent study has focused on the cloze training objective of Masked Language Models . distractors must be distinct and incorrect, and can be biased if the test creator is testing two versions of a text . |
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Multi-choice Relational Reasoning for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | cloze-style reading comprehension is a task that requires much semantic understanding and reasoning using various clues from texts. |
| Approach: | They propose a multi-choice relational reasoning model that emulates human reading comprehension by combining fusion representations of document, query and candidates. |
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