Paraphrase Identification

The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data. Source: http://nlpprogress.com/english/sentiment_analysis.html Image Source: Maas et al

PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki. Source: Google Research Image Source: https://arxiv.org/pdf/1908.11828v1.pdf

Paraphrase Adversaries from Word Scrambling (PAWS) is a dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. Source: PAWS

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

WikiHop is a multi-hop question-answering dataset. The query of WikiHop is constructed with entities and relations from WikiData, while supporting documents are from WikiReading. A bipartite graph connecting entities and documents is first built and the answer for each query is located by traversal on this graph. Candidates that are type-consistent with the answer and share the same relation in query with the answer are included, resulting in a set of candidates. Thus, WikiHop is a multi-choice style reading comprehension data set. There are totally about 43K samples in training set, 5K samples in development set and 2.5K samples in test set. The test set is not provided. The task is to predict the correct answer given a query and multiple supporting documents. The dataset includes a masked variant, where all candidates and their mentions in the supporting documents are replaced by random but consistent placeholder tokens. Source: Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs Image Source: http://qangaroo.cs.ucl.ac.uk/

Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs, and each question pair is annotated with a binary value indicating whether the two questions are paraphrase of each other. Source: Bilateral Multi-Perspective Matching for Natural Language Sentences

Paraphrase and Semantic Similarity in Twitter (PIT) presents a constructed Twitter Paraphrase Corpus that contains 18,762 sentence pairs. Source: SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)

Paralex learns from a collection of 18 million question-paraphrase pairs scraped from WikiAnswers.

FLUE is a French Language Understanding Evaluation benchmark. It consists of 5 tasks: Text Classification, Paraphrasing, Natural Language Inference, Constituency Parsing and Part-of-Speech Tagging, and Word Sense Disambiguation.

Twitter News URL Corpus is a human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. Source: A Continuously Growing Dataset of Sentential Paraphrases Image Source: https://arxiv.org/pdf/1708.00391v1.pdf

Finnish Paraphrase Corpus is a fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headings. Out of all paraphrase pairs in the corpus 98% are manually classified to be paraphrases at least in their given context, if not in all contexts.

SV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Sentences are labeled with variables that are mentioned either explicitly or implicitly. The dataset supports the following tasks: - Variable Detection: identifying whether a sentence contains a variable mention or not. - Variable Disambiguation: identifying which variable from a given vocabulary is mentioned in a sentence.

PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge. Source: PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

This is a paraphrasing dataset created using the adversarial paradigm. A task was designed called the Adversarial Paraphrasing Task (APT) whose objective was to write sentences that mean the same as a given sentence but have as different syntactical and lexical properties as possible. As shown in the paper, this dataset can be used to measure the performance of paraphrase identifier models and train them. This dataset and the task associated with it (APT) can also be used to challenge neural networks to generate better adversarial paraphrases (the work has done this for T5-base), which will in turn help create better paraphrase identifiers.

This is a benchmark for neural paraphrase detection, to differentiate between original and machine-generated content. ####Training: 1,474,230 aligned paragraphs (98,282 original, 1,375,948 paraphrased with 3 models and 5 hyperparameter configurations each 98,282) extracted from 4,012 (English) Wikipedia articles. ####Testing: BERT-large (cased): arXiv - Original - 20,966; Paraphrased - 20,966; Theses - Original - 5,226; Paraphrased - 5,226; Wikipedia - Original - 39,241; Paraphrased - 39,241; RoBERTa-large (cased): arXiv - Original - 20,966; Paraphrased - 20,966; Theses - Original - 5,226; Paraphrased - 5,226; Wikipedia - Original - 39,241; Paraphrased - 39,241; Longformer-large (uncased): arXiv - Original - 20,966; Paraphrased - 20,966; Theses - Original - 5,226; Paraphrased - 5,226; Wikipedia - Original - 39,241; Paraphrased - 39,241;

For more details see https://huggingface.co/datasets/jpwahle/autoregressive-paraphrase-dataset

This dataset is used to train and evaluate models for the detection of machine-paraphrased text. The training set consists of 200,767 paragraphs (98,282 original, 102,485 paraphrased) extracted from 8,024 Wikipedia (English) articles (4,012 original, 4,012 paraphrased using the SpinBot API). The test set is divided into 3 subsets: one created from preprints of research papers on arXiv, one from graduation theses, and one from Wikipedia articles. Additionally, different marchine-paraphrasing methods were used. Test sets: SpinBot: arXiv - Original - 20,966; Spun - 20,867 Theses - Original - 5,226; Spun - 3,463 Wikipedia - Original - 39,241; Spun - 40,729 SpinnerChief-4W: arXiv - Original - 20,966; Spun - 21,671 Theses - Original - 2,379; Spun - 2,941 Wikipedia - Original - 39,241; Spun - 39,618 SpinnerChief-2W: arXiv - Original - 20,966; Spun - 21,719 Theses - Original - 2,379; Spun - 2,941 Wikipedia - Original - 39,241; Spun - 39,697

Translated SNLI Dataset in Marathi A translated version of the SNLI dataset in Marathi, designed for Semantic Textual Similarity (STS) tasks. The translations were generated using the model aryaumesh/english-to-marathi. #### Translation Quality Metrics: - BERT Precision: 88.57% - BERT Recall: 88.60% - BERT F1: 88.58% - TransQuest Score: 0.72