RALS: Resources and Baselines for Romanian Automatic Lexical Simplification
- URL: http://arxiv.org/abs/2607.20078v1
- Date: Wed, 22 Jul 2026 12:31:43 GMT
- Title: RALS: Resources and Baselines for Romanian Automatic Lexical Simplification
- Abstract summary: This dataset jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) approaches for Romanian.<n>We propose a methodology for ordering simplification suggestions using a pairwise ranking approximation method.<n>We provide human lexical complexity annotations for 3,921 word samples in context.
- Score: 0.758305251912708
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches. We propose a methodology for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments. In addition, we provide human lexical complexity annotations for 3,921 word samples in context. Finally, we explore several novel pipelines for complexity prediction and simplification and present the first text simplification system for Romanian.
Related papers
- New Evaluation Paradigm for Lexical Simplification [15.890439726439276]
Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking.<n>We found large language models (LLMs) can simplify sentences directly with a single prompt, bypassing the traditional pipeline.<n>We propose a new annotation method for constructing an all-in-one LS dataset through human-machine collaboration.
arXiv Detail & Related papers (2025-01-25T16:31:37Z) - H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables [56.73919743039263]
This paper introduces a novel algorithm that integrates both symbolic and semantic (textual) approaches in a two-stage process to address limitations.<n>Our experiments demonstrate that H-STAR significantly outperforms state-of-the-art methods across three question-answering (QA) and fact-verification datasets.
arXiv Detail & Related papers (2024-06-29T21:24:19Z) - Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese [1.1650821883155187]
We propose Readability-guided-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques with lexcial simplification.
RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2)aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions.
arXiv Detail & Related papers (2024-06-05T06:15:48Z) - A New Dataset and Empirical Study for Sentence Simplification in Chinese [50.0624778757462]
This paper introduces CSS, a new dataset for assessing sentence simplification in Chinese.
We collect manual simplifications from human annotators and perform data analysis to show the difference between English and Chinese sentence simplifications.
In the end, we explore whether Large Language Models can serve as high-quality Chinese sentence simplification systems by evaluating them on CSS.
arXiv Detail & Related papers (2023-06-07T06:47:34Z) - Exploiting Summarization Data to Help Text Simplification [50.0624778757462]
We analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify.
We named these pairs Sum4Simp (S4S) and conducted human evaluations to show that S4S is high-quality.
arXiv Detail & Related papers (2023-02-14T15:32:04Z) - NapSS: Paragraph-level Medical Text Simplification via Narrative
Prompting and Sentence-matching Summarization [46.772517928718216]
We propose a summarize-then-simplify two-stage strategy, which we call NapSS.
NapSS identifies the relevant content to simplify while ensuring that the original narrative flow is preserved.
Our model achieves significantly better than the seq2seq baseline on an English medical corpus.
arXiv Detail & Related papers (2023-02-11T02:20:25Z) - Controllable Text Simplification with Explicit Paraphrasing [88.02804405275785]
Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting.
Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously.
We propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles.
arXiv Detail & Related papers (2020-10-21T13:44:40Z) - Elaborative Simplification: Content Addition and Explanation Generation
in Text Simplification [33.08519864889526]
We present the first data-driven study of content addition in text simplification.
We analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity.
Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
arXiv Detail & Related papers (2020-10-20T05:06:23Z) - Chinese Lexical Simplification [29.464388721085548]
There is no research work for Chinese lexical simplification ( CLS) task.
To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS.
We present five different types of methods as baselines to generate substitute candidates for the complex word.
arXiv Detail & Related papers (2020-10-14T12:55:36Z) - ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification
Models with Multiple Rewriting Transformations [97.27005783856285]
This paper introduces ASSET, a new dataset for assessing sentence simplification in English.
We show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task.
arXiv Detail & Related papers (2020-05-01T16:44:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.