Challenge: Existing research on sentence-level paraphrase detection in Pashto has focused on English, but no work has been done on low-resource Pashtone.
Approach: They propose to annotate sentences in Pashto to detect paraphrases . they will publicize a subset of 1,800 instances from their corpus, free from licensing issues.
Outcome: The proposed corpus contains 6,727 sentences, encompassing 3,687 paraphrased and 3,040 non-paraphrased sentences.

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Challenge: Opusparcus is a new corpus of paraphrases for six European languages . it is based on movie and TV subtitles, which are colloquial and informal .
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PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models (2025.coling-main)

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Challenge: prevailing notion of paraphrase is simplistic, offering only limited view of vast spectrum of paraphrasing phenomena.
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Paraphrastic Representations at Scale (2022.emnlp-demos)

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Challenge: a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages.
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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.
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Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)

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Challenge: a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs .
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Paraphrase Types for Generation and Detection (2023.emnlp-main)

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Challenge: Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language.
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A Large Resource of Patterns for Verbal Paraphrases (L18-1)

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Challenge: Xu et al., 2015: paraphrases play an important role in natural language understanding . he says it is difficult to propose a paraphrasing relation for natural language processing systems .
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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)

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Challenge: Paraphrase generation is a long-standing task in natural language processing (NLP).
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Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (2024.findings-acl)

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Challenge: Existing work focuses on detecting (partially) AI-generated texts, but paraphrasing is commonly employed in various application scenarios for text refinement and diversity.
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Essentia: Mining Domain-specific Paraphrases with Word-Alignment Graphs (D19-53)

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Challenge: Existing methods for mining general-purpose paraphrases are often based on statistical methods, but domain-specific corpora are too small to fit statistical methods.
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