Papers with sentence splitting
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora (2022.acl-demo)
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| Challenge: | Currently, high-quality corpora of this type are rare and often of comparably small size. |
| Approach: | They propose an open-source web application for automatic text simplification. |
| Outcome: | TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs, w.r.t. simplification transformations, and iv) manual simplification of complex documents. |
BLEU is Not Suitable for the Evaluation of Text Simplification (D18-1)
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| Challenge: | BLEU is widely considered to be an informative metric for text-to-text generation . Xu et al. (2016) found that BLUE is not suitable for evaluation of sentence splitting . |
| Approach: | They propose to use BLEU to evaluate sentence splitting as a metric for machine translation . they propose to compare BLUE with a corpus containing multiple structural paraphrases . |
| Outcome: | The proposed BLEU is not suitable for evaluation of sentence splitting . a correlation analysis with human judgments shows low correlation with BLUE . |
Learning Simplifications for Specific Target Audiences (P18-2)
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| Challenge: | Text simplification is a monolingual text-to-text transformation task . data from TS data can contain multiple simplifications of the same original text . |
| Approach: | They propose to use sequence-to-sequence neural models to build models tailored for specific grade levels. |
| Outcome: | The proposed model outperforms state-of-the-art approaches for a monolingual text-to-text transformation task. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
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| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Seq2Edits: Sequence Transduction Using Span-level Edit Operations (2020.emnlp-main)
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| Challenge: | Seq2Edits is an open-vocabulary approach to sequence editing for natural language processing tasks with a high degree of overlap between input and output texts. |
| Approach: | They propose an open-vocabulary approach to sequence editing for NLP tasks with a high degree of overlap between input and output texts. |
| Outcome: | The proposed approach speeds up inference by up to 5.2x compared to full sequence models . it improves explainability by associating each edit operation with a human-readable tag. |
Encode, Tag, Realize: High-Precision Text Editing (D19-1)
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| Challenge: | Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts. |
| Approach: | They propose a sequence tagging approach that casts text generation as a text editing task. |
| Outcome: | The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance. |