| Challenge: | Story Cloze Test (SOTA) models can achieve over 90% accuracy on predicting the last sentence, but high accuracy can be achieved by merely using surface-level features. |
| Approach: | They constructed a human-labeled and human-verified commonsense knowledge inference dataset using data from 1871 stories and three human workers labeled each story. |
| Outcome: | The proposed models can achieve 90% accuracy on predicting the last sentence, but they don't perform well on new and more challenging tasks. |
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| Challenge: | Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. |
| Approach: | They propose to use a crowdsourcing scheme to create a new SCT dataset to overcome some of the biases discovered in the original SCT. |
| Outcome: | The proposed model performs better than the baselines on the SCT dataset, despite human-authorship biases. |
SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations (2020.acl-main)
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| Challenge: | Experimental results show that there is a significant performance gap between advanced models (72%) and humans (87%) Cloze datasets are convenient either to be generated automatically or by annotators. |
| Approach: | They propose to use a dataset to evaluate the performance of computational models through sentence prediction. |
| Outcome: | The proposed model fills up multiple blanks in a passage from a shared candidate set with distractors designed by English teachers. |
A Simple and Effective Approach to the Story Cloze Test (N18-2)
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| Challenge: | Existing approaches to the Cloze Test that use feature engineering to achieve high accuracy are ignoring the training set and training a model on the validation set. |
| Approach: | They propose a fully-neural approach to the Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance without any feature engineering. |
| Outcome: | The proposed approach achieves close to state-of-the-art performance on the Cloze Test without any feature engineering. |
Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)
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| Challenge: | CR is the ability to understand and navigate the world using basic knowledge and understanding shared by most people. |
| Approach: | They propose to incorporate pretrained knowledge into NMT models and use them as robust testbeds for investigating CR in NMT. |
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A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
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Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge (P18-1)
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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 . |
| Approach: | They propose a reading comprehension model that integrates external commonsense knowledge in a cloze-style setting. |
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MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (L18-1)
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| Challenge: | Various approaches for script knowledge extraction and processing have been proposed in recent years. |
| Approach: | They propose a dataset to evaluate natural language understanding approaches based on commonsense knowledge. |
| Outcome: | The proposed dataset provides test cases for the broader natural language understanding community. |
Large-scale Cloze Test Dataset Created by Teachers (D18-1)
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| Challenge: | Existing cloze tests are used to evaluate language proficiency in language exams, but they are not yet available. |
| Approach: | They propose to create a large-scale human-created cloze test dataset CLOTH, which contains questions used in middle-school and high-school language exams. |
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KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
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| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
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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 . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |