Papers with Text Simplification
Semantic Specialization of Distributional Word Vectors (D19-2)
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| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
| Outcome: | This paper provides an overview of specialization methods for distributional word vectors . the most recent developments include a new method for asymmetric relations in Euclidean . |
mEdIT: Multilingual Text Editing via Instruction Tuning (2024.naacl-long)
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| Challenge: | mEdIT is a multi-lingual extension to CoEdit for writing assistance. |
| Approach: | They propose to train multi-lingual large language models (LLMs) by fine-tuning them via instruction tuning. |
| Outcome: | The proposed model performs well on multilingual text editing benchmarks and generalizes well to new languages. |
A Japanese News Simplification Corpus with Faithfulness (2024.lrec-main)
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| Challenge: | Existing simplified corpora lack faithfulness to original text, resulting in errors in translation. |
| Approach: | They propose to simplify Japanese newspaper articles to prioritize faithfulness over automated models. |
| Outcome: | The proposed corpus preserves the original text, surpassing existing corpora. |
Inherent Biases in Reference-based Evaluation for Grammatical Error Correction (P18-1)
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| Challenge: | Existing evaluation systems obtain comparable or superior performance compared to humans by making few but targeted changes to the input. |
| Approach: | They propose to re-scale M 2 by the inter-annotator agreement and increase the number of references in any feasible range to overcome low coverage bias in GEC evaluation. |
| Outcome: | The proposed measure overcomes low coverage bias in GEC evaluation by re-scaling or increasing the number of references in any feasible range. |
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 . |
FELIX: Flexible Text Editing Through Tagging and Insertion (2020.findings-emnlp)
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| Challenge: | FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. |
| Approach: | They propose a flexible text-editing approach that decomposes a text-generating task into two sub-tasks: tagging and insertion. |
| Outcome: | The proposed model is efficient in low-resource settings and fast at inference time while being capable of modeling flexible input-output transformations. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
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| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |
Klexikon: A German Dataset for Joint Summarization and Simplification (2022.lrec-1)
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| Challenge: | Traditionally, Text Simplification is a monolingual translation task where individual sentences are "translated" into a simplified version. |
| Approach: | They propose to use a dataset to jointly simplify long source documents by combining sentences from a source and their simplified counterparts. |
| Outcome: | The proposed system can summarize and simplify long source documents using almost 2,900 documents. |
Text Simplification from Professionally Produced Corpora (L18-1)
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| Challenge: | Existing approaches to Text Simplification rely on the Wikipedia-Simple Wikipedia parallel corpus, which is used for many tasks. |
| Approach: | They propose to use the Newsela corpus to extract 550, 644 complex-simple sentence pairs from the corpus and introduce a lexical simplifier that uses the corpu to generate candidate simplifications. |
| Outcome: | The proposed model outperforms state-of-the-art approaches and generates candidate simplifications from the newsela corpus. |
LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)
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| Challenge: | Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach. |
| Approach: | They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner . |
| Outcome: | The proposed model correlates much better with human judgment than existing metrics. |