Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022)(2022)
Abstract
This paper presents the first multi-objective transformer model for constructing open cloze tests that exploits generation and discrimination capabilities to improve performance. Our model is further enhanced by tweaking its loss function and applying a post-processing reranking algorithm that improves overall test structure. Experiments using automatic and human evaluation show that our approach can achieve up to 82% accuracy according to experts, outperforming previous work and baselines. We also release a collection of highquality open cloze tests along with sample system output and human annotations that can serve as a future benchmark.
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Key words
open cloze tests
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