Segmenting Natural Language Sentences via Lexical Unit Analysis (2021.findings-emnlp)
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| Challenge: | Recent work on sequence segmentation models suffer from invalid predictions and a lack of consistency. |
| Approach: | They propose a unified span-based model that embeds every span and computes a score for each segmentation candidate. |
| Outcome: | The proposed model achieves state-of-the-art on 6 of the 3 tasks tested. |
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| Challenge: | Despite the cutting-edge representation learning, most language models focus on specific levels of linguistic units. |
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Span Fine-tuning for Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process. |
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A unified approach to sentence segmentation of punctuated text in many languages (2021.acl-long)
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| Challenge: | Existing tools for segmenting punctuated text in many languages are limited in their language coverage and evaluation is ad hoc. |
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Lexically Grounded Subword Segmentation (2024.emnlp-main)
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Improving Text Readability through Segmentation into Rheses (2024.lrec-main)
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| Challenge: | a new study examines the segmentation of sentences into rheses to improve readability for dyslexics . short lines of text can be beneficial for dyslexia sufferers as it limits attention span . however, random line splits can be confusing than helpful . |
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Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
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