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Smart Word Suggestions for Writing Assistance

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arxiv 2305.09975 v1 pith:RWQMUDRX submitted 2023-05-17 cs.CL

Chenshuo Wang , Shaoguang Mao , Tao Ge , Wenshan Wu , Xun Wang , Yan Xia , Jonathan Tien , Dongyan Zhao This is my paper · ORCID
classification cs.CL
keywords suggestionsassistancedatasettaskwordwritingbenchmarkdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces "Smart Word Suggestions" (SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end evaluation and presents a more realistic writing assistance scenario. This task involves identifying words or phrases that require improvement and providing substitution suggestions. The benchmark includes human-labeled data for testing, a large distantly supervised dataset for training, and the framework for evaluation. The test data includes 1,000 sentences written by English learners, accompanied by over 16,000 substitution suggestions annotated by 10 native speakers. The training dataset comprises over 3.7 million sentences and 12.7 million suggestions generated through rules. Our experiments with seven baselines demonstrate that SWS is a challenging task. Based on experimental analysis, we suggest potential directions for future research on SWS. The dataset and related codes is available at https://github.com/microsoft/SmartWordSuggestions.

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  1. Learning to Substitute Words with Model-based Score Ranking

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A BERT model fine-tuned with ranking losses against BARTScore substitutes words to improve that score, outperforming supervised and LLM baselines on BARTScore-based metrics without human labels.

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