Papers Text Simplification
“Text Simplification” 태그가 달린 논문 505편 · 필터 해제
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models to natural language remains challenging due t…
Natural Language UnderstandingParaphrase GenerationQuestion GenerationQuestion-Generation+2Context-Aware Document Simplification
To date, most work on text simplification has focused on sentence-level inputs. Early attempts at document simplification merely applied these approaches iteratively over the sentences of a document. However, this fails …
SentenceText SimplificationBenchmarking large language models for biomedical natural language processing applications and recommendations
The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown…
BenchmarkingDocument ClassificationMedical Question Answeringnamed-entity-recognition+7ChatGPT as a Text Simplification Tool to Remove Bias
The presence of specific linguistic signals particular to a certain sub-group of people can be picked up by language models during training. If the model begins to associate specific language with a distinct group, any d…
AttributeText SimplificationSyntactic Complexity Identification, Measurement, and Reduction Through Controlled Syntactic Simplification
Text simplification is one of the domains in Natural Language Processing (NLP) that offers an opportunity to understand the text in a simplified manner for exploration. However, it is always hard to understand and retrie…
Knowledge GraphsSentenceSplit and RephraseText SimplificationSimpLex: a lexical text simplification architecture
Text simplification (TS) is the process of generating easy-to-understand sentences from a given sentence or piece of text. The aim of TS is to reduce both the lexical (which refers to vocabulary complexity and meaning) a…
SentenceText SimplificationWord EmbeddingsRISC: Generating Realistic Synthetic Bilingual Insurance Contract
This paper presents RISC, an open-source Python package data generator (https://github.com/GRAAL-Research/risc). RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in F…
Machine TranslationNERQuestion AnsweringText SimplificationLexical Complexity Prediction: An Overview
The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts …
Lexical Complexity PredictionPredictionReading ComprehensionText SimplificationkNN-BOX: A Unified Framework for Nearest Neighbor Generation
Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BO…
Machine TranslationParaphrase GenerationQuestion GenerationQuestion-Generation+2Prompt-based Learning for Text Readability Assessment
We propose the novel adaptation of a pre-trained seq2seq model for readability assessment. We prove that a seq2seq model - T5 or BART - can be adapted to discern which text is more difficult from two given texts (pairwis…
Domain GeneralizationText SimplificationMed-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts
Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requir…
PositionText SimplificationExploiting Summarization Data to Help Text Simplification
One of the major problems with text simplification is the lack of high-quality data. The sources of simplification datasets are limited to Wikipedia and Newsela, restricting further development of this field. In this pap…
SentenceText SimplificationText SummarizationNapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this …
Semantic SimilaritySemantic Textual SimilaritySentenceText SimplificationFindings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification
We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 20…
Lexical SimplificationText SimplificationMitigating Data Scarcity for Large Language Models
In recent years, pretrained neural language models (PNLMs) have taken the field of natural language processing by storm, achieving new benchmarks and state-of-the-art performances. These models often rely heavily on anno…
Data AugmentationEnsemble LearningText SimplificationLexical Simplification using multi level and modular approach
Text Simplification is an ongoing problem in Natural Language Processing, solution to which has varied implications. In conjunction with the TSAR-2022 Workshop @EMNLP2022 Lexical Simplification is the process of reducing…
Lexical SimplificationRe-RankingTAGText SimplificationThe Fewer Splits are Better: Deconstructing Readability in Sentence Splitting
In this work, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in t…
SentenceText SimplificationTraditional Readability Formulas Compared for English
Traditional English readability formulas, or equations, were largely developed in the 20th century. Nonetheless, many researchers still rely on them for various NLP applications. This phenomenon is presumably due to the …
NeRFText SimplificationTextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ com…
Abstractive Text SummarizationData-to-Text GenerationDialogueMachine Translation+7Automatic Text Simplification of News Articles in the Context of Public Broadcasting
This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/R…
ArticlesText Simplification