Papers with Paraphrasing
AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts (2020.aacl-demo)
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| Challenge: | OECD, 2016) report that a significant proportion of citizens still have general reading difficulties. |
| Approach: | They propose to use a readability formula and natural language processing tools to analyze texts and highlight linguistic phenomena considered difficult to read. |
| Outcome: | The AMesure platform analyzes administrative texts and offers advice from plain language guides. |
Catchphrase: Automatic Detection of Cultural References (2021.acl-short)
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| Challenge: | a snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. |
| Approach: | They propose to use pop-culture quotes to train algorithms to detect cultural references in text. |
| Outcome: | The proposed algorithm can detect cultural references in pop-culture quotes and train on them. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
| Outcome: | The proposed architecture achieves remarkably good performance on the utterance rewriting task. |
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances (2020.coling-industry)
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| Challenge: | In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. |
| Approach: | They propose a method to leverage this feedback by creating annotated training examples from it. |
| Outcome: | The proposed method can be used in a commercial dialog system across various domains and three languages. |
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (2023.eacl-main)
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| Challenge: | Curriculum Data Augmentation (CDA) presents synthetic data with increasing difficulties to neural models. |
| Approach: | They propose a curriculum-aware paraphrase generation module with bottom-k sampling and cyclic learning strategy that passes through the curriculums multiple times. |
| Outcome: | The proposed framework surpasses competitive baselines on few-shot text classification and dialogue generation. |
Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (D19-55)
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| Challenge: | Recent approaches for paraphrasing generate unpredictable results . |
| Approach: | They propose a question paraphrasing pipeline model based on templates that identifies template and retrieves candidate templates and fills them with original topic words. |
| Outcome: | The proposed model outperforms the seq2seq model on two datasets and is more promising when the training sample is small. |
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. |
Personalizing Lexical Simplification (C18-1)
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| Challenge: | Experimental results show that even a simple personalized CWI model can help the system avoid some unnecessary simplifications and produce more readable output. |
| Approach: | They evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels and measure the benefits of using complex word identification models to personalize the system. |
| Outcome: | The proposed system produces a more readable output for learners with special needs and those with language disabilities. |
Pointwise Paraphrase Appraisal is Potentially Problematic (2020.acl-srw)
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| Challenge: | prevailing methods for paraphrase identification models are binary classification problems . current methods do not provide consistent and robust performance on unseen samples and real world problems. |
| Approach: | They propose to use binary classification to evaluate paraphrase identification models . they propose to improve methods for fine-tuning BERT models by pairing two sentences as one sequence . |
| Outcome: | The proposed methods may fail on simple tasks like identifying pairs with two identical sentences. |
Lexi: A tool for adaptive, personalized text simplification (C18-1)
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| Challenge: | Existing research on text simplification has aimed to develop generic solutions . instead, we need to develop customized simplification systems for individual users . |
| Approach: | They propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source tool for personalized text simplification. |
| Outcome: | The proposed framework is based on a free open-source tool for adaptive, personalized text simplification. |
Arabic Word-level Readability Visualization for Assisted Text Simplification (2022.emnlp-demos)
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| Challenge: | a Google Docs add-on for automatic Arabic word-level readability visualization is available for free. |
| Approach: | They propose a Google Docs add-on for automatic Arabic word-level readability visualization. |
| Outcome: | The proposed add-on can be used to assess the reading difficulty of a text and identify difficult words as part of manual text simplification. |
A Study of Incorrect Paraphrases in Crowdsourced User Utterances (N19-1)
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| Challenge: | Developing bots requires high quality training samples, especially for unqualified crowd workers. |
| Approach: | They propose an annotated dataset for detecting quality issues in crowdsourced paraphrasing . they propose to use existing tools and services to provide baselines for identifying issues . |
| Outcome: | The proposed dataset provides a baseline for detecting unqualified paraphrases. |
Improved Evaluation Framework for Complex Plagiarism Detection (P18-2)
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| Challenge: | Plagiarism is a major issue in science and education. |
| Approach: | They propose to use plagdet to normalize single case scores to prevent misuse of paraphrased datasets. |
| Outcome: | The proposed evaluation framework is made publicly available as a Github repository. |
Gauging the Gap Between Human and Machine Text Simplification Through Analytical Evaluation of Simplification Strategies and Errors (2023.findings-eacl)
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| Challenge: | Recent studies on text simplification have focused on lexical and syntactic simplification, but few studies have attempted to assess what kind of editing operations are performed by the systems in concrete terms. |
| Approach: | They develop an analytical evaluation framework for neural text simplification systems that includes fine-grained taxonomies of simplification strategies and errors. |
| Outcome: | The framework was used to evaluate TS models produced by human editors and multiple neural TS systems and found that human editors perform deletions and local substitutions while excessively omitting important information. |
Par4Sim – Adaptive Paraphrasing for Text Simplification (C18-1)
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| Challenge: | a new challenge is learning from a real-world data stream and continuously updating the model without explicit supervision. |
| Approach: | They develop an adaptive learning system for text simplification which improves the underlying ranking model from usage data. |
| Outcome: | The proposed system improves the learning-to-rank model from usage data over time. |
Iterative Paraphrastic Augmentation with Discriminative Span Alignment (2021.tacl-1)
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| Challenge: | Existing datasets can be expanded or created using a small, manually produced seed corpus. |
| Approach: | They propose a paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrases and discriminative span alignment. |
| Outcome: | The proposed approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
Two-stage Incomplete Utterance Rewriting on Editing Operation (2025.coling-main)
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| Challenge: | Existing methods to generate rewritten utterances based on dialogue context ignore coreference and ellipsis in dialogues. |
| Approach: | They propose a framework where the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations. |
| Outcome: | The proposed framework outperforms the existing models on three IUR datasets. |
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification (C18-1)
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| Challenge: | Sentence simplification is the task of improving readability and understandability of an input text. |
| Approach: | They propose a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model and a novel ‘multi-level’ soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the model. |
| Outcome: | The proposed model outperforms competing simplification systems in SARI and FKGL automatic metrics, and human evaluation. |
CGF: Constrained Generation Framework for Query Rewriting in Conversational AI (2022.emnlp-industry)
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Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo, Gokhan Tur
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri and Google Assistant help millions of users to perform a lot of tasks. |
| Approach: | They propose a Constrained Generation Framework for query rewriting at global and personalized levels. |
| Outcome: | The proposed framework significantly boosts the query rewriting performance. |
Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge (2020.coling-main)
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| Challenge: | Current dialog models require large quantities of training data due to privacy issues . current methods require large amounts of training and are even scarcer in the health domain . |
| Approach: | They propose a method for creating dialog corpora from physician-patient interaction data . they combine expert knowledge provided by physicians with automatic paraphrase extraction techniques . |
| Outcome: | The proposed method outperforms existing models in the health domain . it provides a natural way of guiding learning and improves quality of human-machine interactions . |
Unified Contextual Query Rewriting (2023.acl-industry)
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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life. |
| Approach: | They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance . |
| Outcome: | The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks. |
Semantic Structural Evaluation for Text Simplification (N18-1)
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| Challenge: | Current measures for evaluating text simplification systems focus on lexical aspects, neglecting its structural aspects. |
| Approach: | They propose to use a reference-less automatic evaluation procedure to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. |
| Outcome: | The proposed measure has a significant correlation with human judgments and is highly comparable with existing measures. |
Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language (2022.aacl-main)
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| Challenge: | Automatic Readability Assessment (ARA) aims at assigning a complexity level to a text, which could help improve the accessibility to information in specific domains, such as the administrative one. |
| Approach: | They propose to use a Neural Pairwise Ranking Model (NPRM) to assess Italian administrative texts by rewriting sentences using three different rewrite strategies. |
| Outcome: | The proposed model is effective in zero-shot scenarios (0.78) and the best results are obtained by adding in-domain data (achieving perfect performance for such sentence pairs). |
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. |
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |
Complex Word Identification as a Sequence Labelling Task (P19-1)
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| Challenge: | Complex Word Identification (CWI) is a crucial first step in a simplification pipeline. |
| Approach: | They propose a system that performs CWI in context without extensive feature engineering and outperforms state-of-the-art systems on this task. |
| Outcome: | The proposed system outperforms state-of-the-art systems on complex word identification. |
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (P18-1)
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| Challenge: | Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces complexity and fragility. |
| Approach: | They propose a novel sequence-to-sequence (seq2sequ) model which tracks dialogue believes and a two stage copynet instantiation which emonstrates good scalability. |
| Outcome: | The proposed framework outperforms state-of-the-art pipeline-based methods on large datasets and retains satisfactory entity match rate on out-of vocabulary (OOV) cases where pipeline-designed competitors totally fail. |
How Well Apply Simple MLP to Incomplete Utterance Rewriting? (2023.acl-short)
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| Challenge: | Incomplete utterance rewriting (IUR) aims to restore incomplete utterant with sufficient context information for comprehension. |
| Approach: | They propose a method to restore incomplete utterances with sufficient context information . they employ only one-layer MLP architecture to mine latent semantic information based on joint utterations . |
| Outcome: | The proposed method is superior to existing methods in quality and speed. |
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search (2024.emnlp-main)
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| Challenge: | Recent advances in task-solving capabilities of Large Language Models (LLMs) have motivated researchers to integrate these models into existing conversational search systems. |
| Approach: | They propose a method that leverages the capabilities of large language models to resolve ambiguities in conversation history before query rewriting. |
| Outcome: | The proposed method leads to state-of-the-art results across most settings compared with closed-source LLMs. |
LCQMC:A Large-scale Chinese Question Matching Corpus (C18-1)
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| Challenge: | Existing methods for question answering system lack large-scale question matching corpora . lack of large-sized question matching results in problem solving . |
| Approach: | They propose a large-scale Chinese question matching corpus which is released to the public . they use a search engine to collect large-sized question pairs related to high-frequency words . |
| Outcome: | The proposed corpus is more general than paraphrase corpus as it focuses on intent matching rather than paraphrasing. |
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting (2023.findings-emnlp)
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| Challenge: | Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, introducing words from irrelevant utterances. |
| Approach: | They propose a framework to capture the multi-granularity of semantic information and fetch the relevant utterance. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two benchmark datasets . it can capture the source of important words and fetch the relevant utterance . |
Incomplete Utterance Rewriting by A Two-Phase Locate-and-Fill Regime (2023.findings-acl)
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| Challenge: | Existing models with incomplete utterances have too large search space, resulting in poor quality of rewriting results. |
| Approach: | They propose a 2-phase rewriting framework which predicts empty slots in the utterance that need to be completed and generates the part to be filled into each position. |
| Outcome: | The proposed framework achieves state-of-the-art results on several public rewriting datasets. |
Learning Scalar Adjective Intensity from Paraphrases (D18-1)
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| Challenge: | Existing lexical resources do not include the relative intensities of adjectives. |
| Approach: | They propose a method to automatically learn relative intensity relation between scalar adjectives . they use a paraphrase-based method that assumes that a pair of adjectives is "really hot" a similar method is used to infer the polarity of indirect answers to "yes/no" questions . |
| Outcome: | The proposed method improves the quality of systems for ordering sets of scalar adjectives and inferring the polarity of indirect answers to "yes/no" questions. |
SPADE: Evaluation Dataset for Monolingual Phrase Alignment (L18-1)
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| Challenge: | Existing studies on sentential paraphrase detection focus on finer grained paraphrases, i.e., phrasal paraphrase. |
| Approach: | They propose to use the SPADE to evaluate syntactic phrase alignment in paraphrasal sentences. |
| Outcome: | The proposed method is compared with humans and provides benchmarks to show its performance. |
ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation (L18-1)
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| Challenge: | Extended Paraphrase Typology addresses limitations of existing typologies . extended typology provides better means for evaluation and error analysis . |
| Approach: | a new typology copes with non-paraphrase pairs in paraphrase identification corpora, a paper proposes . a large corpus annotated with atomic paraphrase types is the largest to date . |
| Outcome: | The Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC) address practical limitations of existing paraphrase typologies. |
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification (2024.acl-long)
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Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. |
| Approach: | They propose a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
| Outcome: | The proposed framework characterizes and recovers simplification-induced information loss in form of question-and-answer (QA) pairs. |
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection (C18-1)
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Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis, Srinivasa Satya Sameer Kumar Chivukula
| Challenge: | Existing methods for document-level novelty detection are limited and do not require manual feature engineering. |
| Approach: | They propose a deep Convolutional Neural Networks based model to classify a document as novel or redundant on the basis of documents already seen by the system. |
| Outcome: | The proposed model outperforms the state-of-the-art on a document-level novelty detection dataset by a margin of 5% in terms of accuracy. |
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)
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| Challenge: | Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating. |
| Approach: | They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages. |
| Outcome: | The proposed benchmark outperforms pre-trained models in Russian in zero-shot cross-lingual transfer to low-resource languages. |
Evaluating Multilingual Sentence Representation Models in a Real Case Scenario (2022.lrec-1)
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| Challenge: | a recent study has shown that the infamous Protocols are actually plagiarized . a convoluted task with no standard benchmarks for paraphrase detection and sentence similarity is a problem . |
| Approach: | They evaluate sentence representation models on the paraphrase detection task . they use a forged text from the so-called "Protocols of the Elders of Zion" scholars have demonstrated that the first text plagiarizes from the second . |
| Outcome: | The proposed model is based on the forged “Protocols of the Elders of Zion” . the model is similar to the standard model but has some problems . |
Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting (2022.naacl-main)
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| Challenge: | Existing studies do not consider semantic information between incomplete utterance and rewritten utterant or model the semantic structure implicitly and insufficiently. |
| Approach: | They propose a query-Enhanced network to bring semantic structural knowledge between incomplete utterance and rewritten utteras . they adopt a fast and effective edit operation scoring network to model the relation between two tokens based on extra information and the well-designed network . |
| Outcome: | The proposed query template explicitly brings semantic structural knowledge between the incomplete utterance and the rewritten utterant making model perceive where to refer back to or recover omitted tokens. |
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts (2024.emnlp-main)
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| Challenge: | Simplifying the entire text makes it understandable but sometimes removes important details. |
| Approach: | They propose a simplification task for rewriting text to help readers comprehend text containing unfamiliar concepts and introduce a dataset of 22k definitions from 13 academic domains paired with a difficult concept within each definition. |
| Outcome: | The proposed model outperforms open-source and commercial models on the task and human judges prefer explanations over simplifications of the difficult concept. |
Towards the Inference of Semantic Relations in Complex Nominals: a Pilot Study (L18-1)
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| Challenge: | Complex nominals (CNs) show similar external forms but encode different semantic relations because of noun packing. |
| Approach: | They propose to use paraphrases to convey conceptual content of english two-term CNs in the domain of environmental science to disambiguate the semantic relation between constituents of CN. |
| Outcome: | The proposed method disambiguates the semantic relation between constituents of the CN and infers the semantic relations in these multi-word terms. |
The Lower The Simpler: Simplifying Hierarchical Recurrent Models (N19-1)
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| Challenge: | Using a simplified version of GRU, we replace the GRUs at the middle layers of hierarchical recurrent models with Fixed-size Ordinally-Forgetting Encoding (FOFE). |
| Approach: | They propose to make the lower layers simpler than the upper ones to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. |
| Outcome: | The proposed models contain less trainable parameters, consume less training time, and achieve slightly better performance than baseline models. |
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation (2024.emnlp-main)
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| Challenge: | Existing generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, but they often include irrelevant and redundant tokens in rewritten utteras . |
| Approach: | They propose a multi-task learning framework that uses editing operation labels to guide generation model to focus on critical tokens in dialogue context. |
| Outcome: | The proposed model outperforms state-of-the-art models on open-domain and task-oriented dialogues on three datasets. |
Facilitating the Communication of Politeness through Fine-Grained Paraphrasing (2020.emnlp-main)
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| Challenge: | Using technology, people are increasingly able to communicate across geographical, cultural and language barriers, but they also face new challenges, as they need to adapt their communication approaches to increasingly diverse circumstances. |
| Approach: | They propose a method for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance and evaluate it in two realistic communication scenarios. |
| Outcome: | The proposed method reduces misalignment between the speaker’s intentions and listener’s perceptions in two realistic communication scenarios and is able to communicate with people from different backgrounds in diverse contexts. |
Edit-Constrained Decoding for Sentence Simplification (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that lexically constrained decoding is effective for sentence simplification, but their constraints can be loose and may lead to sub-optimal generation. |
| Approach: | They propose an edit operation based on lexically constrained decoding for sentence simplification using a dictionary of technical terms as constraints. |
| Outcome: | The proposed method outperforms previous studies on English simplification corpora and is based on lexical paraphrasing. |
MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025.findings-acl)
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| Challenge: | Medical texts contain technical terms, and non-experts often cannot use information effectively. |
| Approach: | They propose a method for training medical text simplification models to actively paraphrase medical terms. |
| Outcome: | The proposed method improves the performance of medical text simplification in nine languages. |
Recursive Context-Aware Lexical Simplification (D19-1)
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| Challenge: | REC-LS is a system that can be used to perform a number of simplifications at once, but the results are sometimes ungrammatical and meaning can be changed, making the original text less clear and more complex. |
| Approach: | They propose a recursive context-aware lexical simplification architecture that takes previous simplification steps into account and makes use of the wider context when detecting the words in need of simplification. |
| Outcome: | The proposed system outperforms the current state-of-the-art systems in lexical simplification. |
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (2020.emnlp-main)
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| Challenge: | Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. |
| Approach: | They propose to use semantic role labeling to provide additional guidance for multi-turn dialogue rewriting models. |
| Outcome: | The proposed model outperforms existing models on multi-turn dialogue rewriting tasks. |
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection (2022.lrec-1)
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| Challenge: | In this paper, we present a dataset that is collected from users’ input in a plagiarism detection system. |
| Approach: | They propose to use a Persian paraphrase dataset that is collected from users’ input in a plagiarism detection system to improve the quality of the data. |
| Outcome: | The proposed dataset contains 2446 instances of paraphrasing. |
Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus (2022.coling-1)
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| Challenge: | a dataset of 112 admissions instructions is used to simplify the language used by higher education institutions to communicate with prospective students. |
| Approach: | They propose to simplify admissions instructions by professionally simplifying them and comparing them to a dataset of 112 admissions documents. |
| Outcome: | The proposed dataset includes 112 admissions instructions from higher education institutions across the US. |
VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing (2023.findings-emnlp)
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| Challenge: | During the Covid-19 pandemic, the number of people living with anxiety and depression rose more than four times . counselor training is difficult to speed up due to several factors, such as the need for expert supervision and the laborious and time-extensive process needed to provide evaluative feedback. |
| Approach: | They propose a template-based rewriting system that transforms non-reflective statements into reflective responses using paraphrase-augmented training and adaptive template updating. |
| Outcome: | The proposed model transforms non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off. |
Don’t Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting (2023.emnlp-main)
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| Challenge: | Existing stylistic text rewriting methods ignore the context of the text, causing generic, incoherent, and generic outputs. |
| Approach: | They propose a contextual evaluation metric that integrates preceding context into stylistic text rewriting. |
| Outcome: | The proposed metric integrates the preceding textual context into rewriting and evaluation stages . human preferences are better reflected by the proposed criterio and other metrics . |
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives (2021.emnlp-main)
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| Challenge: | Recent years have seen an increasing need for gender-neutral and inclusive language. |
| Approach: | They propose a rule-based and a neural approach to gender-neutral rewriting for English . they use manually curated synthetic and natural data to train a rewriter . |
| Outcome: | The proposed approach improves on the rule-based approach with word error rates below 0.18% on synthetic, in-domain and out-domain test sets. |
SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone (2025.findings-emnlp)
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| Challenge: | Modern language models are evaluated on large benchmarks, which are difficult to make sense of. |
| Approach: | They propose a framework to Simplify Benchmark Analysis using model-centric evaluation numbers. |
| Outcome: | The proposed framework can be applied to HELM, MMLU, and BigBenchLite benchmarks. |
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands (2022.lrec-1)
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| Challenge: | Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner. |
| Approach: | They propose a corpus construction scheme that considers intent and formality of directives in Korean language. |
| Outcome: | The proposed method is validated by a corpus construction scheme on Korean topics. |
CycleKQR: Unsupervised Bidirectional Keyword-Question Rewriting (2022.emnlp-main)
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| Challenge: | Existing approaches to query paraphrases are based on encoderdecoder architectures, but they do not support the two important functionalities beyond questions. |
| Approach: | They propose a keyword-question rewriting task to improve query understanding capabilities of NLU systems for all surface forms. |
| Outcome: | Empirically, we show that CycleKQR significantly improves QA performance by rewriting queries into the appropriate form while retaining the original semantic meaning of input queries. |
Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)
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| Challenge: | Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process . |
| Approach: | They propose a query rewriting framework that generates process-level supervision signals for intermediate steps. |
| Outcome: | a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks. |
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset (2020.lrec-1)
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| Challenge: | a dataset of similar sentences and paraphrases is a challenging task, but it requires high resources. |
| Approach: | They propose a discourse component-based paraphrase generation scheme for directive utterances . they use Korean language, which has diverse paraphrasing due to frequent subject drop . |
| Outcome: | The proposed scheme is efficient in terms of human-aided construction and content preservation. |
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)
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| Challenge: | Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. |
| Approach: | They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering. |
| Outcome: | The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing. |
Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing (2024.emnlp-main)
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| Challenge: | a recent study examines the ability of large language models (LLMs) to paraphrase slang within climate-related tweets . slanted tweets from non-anglocentric countries may contain cultural references and idioms based on sociocultural identities . |
| Approach: | They investigate the ability of large language models to paraphrase slang within climate-related tweets from Nigeria and the UK. |
| Outcome: | The proposed model can paraphrase slang within climate-related tweets from Nigeria and the UK . the model can only parse sexist and sex-related slurs, the study shows . |
Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions (2024.lrec-main)
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| Challenge: | Existing methods for text similarity measurement focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean. |
| Approach: | They propose a Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. |
| Outcome: | The proposed method outperforms existing methods in Korean and other languages . it identifies which methods preserve semantics and tone while preserving similarity . |
EAIR: Entity-aware Inference-Time Knowledge Routing for Multi-Hop Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing in-context knowledge editing methods suffer from paraphrase sensitivity . Existing methods interfere with the model's original knowledge and can degrade its inherent capabilities. |
| Approach: | They propose a novel entity-aware inference-time knowledge routing method to address paraphrase sensitivity. |
| Outcome: | The proposed method achieves highest strict case accuracy in 11 of 12 settings, significantly reducing paraphrase sensitivity. |
Meta-Evaluation of Sentence Simplification Metrics (2024.lrec-main)
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| Challenge: | Automatic Text Simplification (ATS) is a major natural language processing task that aims to help people understand complex text. |
| Approach: | They propose to use a human-annotated dataset to study automatic text simplification models to determine which metrics to use when evaluating new models. |
| Outcome: | The proposed models reconstruct the text into a simpler format by deletion, substitution, addition or splitting, while preserving the original meaning and correct grammar. |
Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification (2026.findings-acl)
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| Challenge: | Existing studies have focused on lexical- and sentence-level simplification, leaving long text simplification comparatively unexplored . |
| Approach: | They propose a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios. |
| Outcome: | The proposed framework outperforms advanced and proprietary LLMs in in-domain and out-of-domain simplification tasks and matches or outperformed existing LLM frameworks. |
Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. |
| Approach: | They propose a lightweight IUR model for query rewriting to complete key information in dialogue to enhance retrieval. |
| Outcome: | The proposed model improves retrieval and generation ability of RAG system in multi-round dialogue scenarios. |
Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance (2026.findings-acl)
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| Challenge: | Large language models (LLMs) perform well on well-posed factual queries, yet standard question-answering (QA) benchmarks remain far from solved. |
| Approach: | They propose an LLM-based classifier to identify underspecified questions and apply it to several widely used QA datasets. |
| Outcome: | The proposed classifier detects underspecified questions in QA datasets and significantly improves on them. |
Q2EI: Query-to-Entity Inference for Semantic Condensation in Domain-Specific Retrieval (2026.findings-acl)
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| Challenge: | Existing generative expansions introduce redundancy or hallucinations that cause semantic drift. |
| Approach: | They propose a query rewriting strategy that reframes rewrite as semantic condensation rather than expansion. |
| Outcome: | The proposed method outperforms baselines on medical and legal benchmarks while reducing token consumption. |
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)
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| Challenge: | Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited. |
| Approach: | They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. |
| Outcome: | The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM. |