Papers Text Simplification
“Text Simplification” 태그가 달린 논문 505편 · 필터 해제
Automatic Sentence Simplification in Low Resource Settings for Urdu
To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification system…
SentenceText SimplificationFlesch-Kincaid is Not a Text Simplification Evaluation Metric
Sentence-level text simplification is currently evaluated using both automated metrics and human evaluation. For automatic evaluation, a combination of metrics is usually employed to evaluate different aspects of the sim…
SentenceText SimplificationInvestigating Text Simplification Evaluation
Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models. However, existing studies show that parallel TS corpora contain inaccurate simplifications and in…
Text SimplificationKeep it Simple: Unsupervised Simplification of Multi-Paragraph Text
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algo…
Reading ComprehensionText SimplificationWhat Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification
Advancements within the field of text simplification (TS) have primarily been within syntactic or lexical simplification. However, conceptual simplification has previously been identified as another field of TS that has …
Binary ClassificationLexical SimplificationReading ComprehensionText SimplificationNeural semi-Markov CRF for Monolingual Word Alignment
Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neut…
Paraphrase GenerationSentenceSentence-Pair ClassificationText Generation+2Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification
Text simplification is a growing field with many potential useful applications. Training text simplification algorithms generally requires a lot of annotated data, however there are not many corpora suitable for this tas…
Text SimplificationSimplification automatique de textes biomédicaux en français: lorsque des données précises de petite taille aident (French Biomedical Text Simplification : When Small and Precise Helps )
Nous présentons un résumé en français et un résumé en anglais de l’article (Cardon & Grabar, 2020), publié dans les actes de la conférence 28th International Conference on Computational Linguistics (COLING 2020).
Text SimplificationImproving Human Text Simplification with Sentence Fusion
The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of huma…
RerankingSentenceSentence FusionText SimplificationContext-Preserving Text Simplification
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principle…
SentenceText SimplificationPredicting Text Readability from Scrolling Interactions
Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study…
Question AnsweringReading ComprehensionText SimplificationFinding Spoiler Bias in Tweets by Zero-shot Learning and Knowledge Distilling from Neural Text Simplification
Automatic detection of critical plot information in reviews of media items poses unique challenges to both social computing and computational linguistics. In this paper we propose to cast the problem of discovering spoil…
Split and RephraseText SimplificationTranslationZero-Shot LearningCreating an Aligned Russian Text Simplification Dataset from Language Learner Data
Parallel language corpora where regular texts are aligned with their simplified versions can be used in both natural language processing and theoretical linguistic studies. They are essential for the task of automatic te…
Text SimplificationText Simplification by Tagging
Edit-based approaches have recently shown promising results on multiple monolingual sequence transduction tasks. In contrast to conventional sequence-to-sequence (Seq2Seq) models, which learn to generate text from scratc…
Text SimplificationPredicting Lexical Complexity in English Texts: The Complex 2.0 Dataset
Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This …
Complex Word IdentificationLexical Complexity PredictionText SimplificationThe GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evalu…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationData-to-Text GenerationExtreme Summarization+4Simple-QE: Better Automatic Quality Estimation for Text Simplification
Text simplification systems generate versions of texts that are easier to understand for a broader audience. The quality of simplified texts is generally estimated using metrics that compare to human references, which ca…
Text SimplificationCUT: Controllable Unsupervised Text Simplification
In this paper, we focus on the challenge of learning controllable text simplifications in unsupervised settings. While this problem has been previously discussed for supervised learning algorithms, the literature on the …
Text SimplificationTranslationSemantic Structural Decomposition for Neural Machine Translation
Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer mode…
Machine TranslationSemantic ParsingSentenceText Simplification+1Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity
We optimize rewards of reinforcement learning in text simplification using metrics that are highly correlated with human-perspectives. To address problems of exposure bias and loss-evaluation mismatch, text-to-text gener…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Text Generation+1