Papers with simplification
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization (2024.eacl-short)
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| Challenge: | Text summarization and simplification are among the most widely used applications of NLP, but they are prone to hallucination due to training on unaligned data. |
| Approach: | They propose a loss-truncation approach to modify the standard log loss to adaptively remove noisy examples during training to improve model performance. |
| Outcome: | The proposed approach yields a considerable number of hallucinated entities on various datasets. |
Simplification Using Paraphrases and Context-Based Lexical Substitution (N18-1)
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| Challenge: | Lexical simplification involves identifying complex words or phrases that need to be simplified and suggesting simpler meaning-preserving substitutes. |
| Approach: | They propose a complex word identification model that exploits both lexical and contextual features and a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. |
| Outcome: | The proposed model detects complex words with higher accuracy than other models and proposes good substitutes in context. |
Crowdsourced Corpus of Sentence Simplification with Core Vocabulary (L18-1)
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| Challenge: | a crowdsourced corpus of simplified sentences is used to generate complex sentences from more complex ones. |
| Approach: | They propose to use crowdsourced data set of simplified sentences from Japanese textbooks and reference books to generate simplified sentences. |
| Outcome: | The proposed set of simplified sentences is a good quality data set for machine learning. |
Investigating Text Simplification Evaluation (2021.findings-acl)
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| Challenge: | Existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. |
| Approach: | They propose to improve the distribution of parallel text simplification corpora to build more robust TS models. |
| Outcome: | The proposed models can be improved by improving the distribution of TS datasets. |
On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)
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| Challenge: | Text simplification is a hot issue in the field of natural language generation (NLG). |
| Approach: | They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences. |
| Outcome: | The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%). |
Alector: A Parallel Corpus of Simplified French Texts with Alignments of Misreadings by Poor and Dyslexic Readers (2020.lrec-1)
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| Challenge: | Typical readers tend to progress quickly in reading because of the automatic process, which increases word identification and vice-versa. |
| Approach: | They propose a parallel corpus for reading tests and for the development of automatic text simplification tools for children with reading difficulties. |
| Outcome: | The proposed corpus is available for consultation through a web interface and available on demand for research purposes. |
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)
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| Challenge: | Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias. |
| Approach: | They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. |
| Outcome: | The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results. |
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification (N19-1)
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Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
| Challenge: | Recent research has applied sequence-to-sequence (Seq2Sequen) models to text simplification . generic models tend to copy directly from the original sentence, resulting in outputs that are long and complex. |
| Approach: | They propose to incorporate word complexities into the loss function during training and generate a large set of diverse candidate simplifications at test time. |
| Outcome: | The proposed model can perform competitively with state-of-the-art systems while generating simpler sentences. |
Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task (2024.lrec-main)
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| Challenge: | Existing methods to assess lexical complexity are used to evaluate the difficulty of vocabulary for language learners. |
| Approach: | They propose to use pre-trained language models to assess the complexity of a word based on its context. |
| Outcome: | The proposed method outperforms the best systems in SemEval-2021. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases (2024.lrec-main)
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| Challenge: | Automated Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. |
| Approach: | They propose to use a dataset of Swedish paraphrases to train ATS models utilizing prefix-tuning with control prefixes to provide more control over the simplification. |
| Outcome: | The proposed model improves on the baseline model and compares with previous models. |
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)
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| Challenge: | Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. |
| Approach: | They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification. |
| Outcome: | The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show . |
Paragraph-level Simplification of Medical Texts (2021.naacl-main)
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| Challenge: | Existing methods for simplification of medical texts are limited due to jargon and technical content. |
| Approach: | They propose to automate the simplification of medical texts by penalizing decoders for producing "jargon" terms. |
| Outcome: | The proposed method improves on existing heuristics by penalizing the decoder for producing "jargon" terms. |
COSTRA 1.0: A Dataset of Complex Sentence Transformations (2020.lrec-1)
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| Challenge: | Several studies have shown word vector representations capture syntactic and semantic regularities. |
| Approach: | They present a dataset of sentence-level embeddings in Czech and plan to use it in other languages. |
| Outcome: | The proposed dataset is limited to Czech but will be used in other languages. |
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
| Outcome: | The proposed method achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. |
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)
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| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
| Approach: | They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification . |
| Outcome: | The proposed system can improve grammaticality of generated text and improve formal style tasks. |
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss (2024.findings-acl)
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| Challenge: | Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in natural language processing (NLP). |
| Approach: | They propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning by using adaptive contrastive learning and resampling miners. |
| Outcome: | The proposed model outperforms previous models significantly on a SemEval challenge and outperformed previous alternatives in many metrics. |
JEBS: A Fine-grained Biomedical Lexical Simplification Task (2025.findings-acl)
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| Challenge: | Existing systems for simplification of complex medical terms are limited in the scope of their topics and require massive cost and effort to keep up with the latest research. |
| Approach: | They propose a fine-grained lexical simplification task and dataset to enable more targeted development and evaluation of systems for replacing or explaining complex biomedical terms. |
| Outcome: | The proposed task and dataset pave the way for development and evaluation of systems for replacing or explaining complex biomedical terms. |
DEFT-UCS: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection for Text-Editing (2024.emnlp-main)
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| Challenge: | Recent advances in language modelling have led to the availability of many pre-trained language models (PLMs); however, how much data is needed to fine-tune PLMs for downstream tasks? |
| Approach: | They propose a data-efficient fine-tuning framework that leverages unsupervised core-set selection to identify a smaller, representative dataset to fine- tune PLMs for text-editing tasks. |
| Outcome: | The proposed framework is as accurate as CoEDIT across eight different datasets consisting of six different editing tasks, while finetuning on 70% less data. |