| Challenge: | a taxonomy is often used to look up concepts in text documents. |
| Approach: | They compare two state-of-the-art paraphrase models with a paraphrase dataset . they find that paraphrasing is a viable method to augment taxonomies with more terms . |
| Outcome: | The proposed model outperforms the previous model on the risk domain. |
Similar Papers
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Task-Oriented Paraphrase Analytics (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on paraphrasing have applied different criteria to the task . authors have previously unmasked related tasks as paraphrases . |
| Approach: | They propose a taxonomy to organize 25 identified paraphrasing tasks . authors propose to use classifiers to identify tasks that a given paraphrased instance fits . |
| Outcome: | The proposed taxonomy identifies 25 paraphrasing tasks that fit the proposed task. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
Copied to clipboard
| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Having high quality annotated data is crucial for training supervised machine learning models. |
| Approach: | They propose automated methods to improve NLP datasets by viewing them as graphs with expected semantic properties. |
| Outcome: | The proposed methods improve paraphrase models on pre-trained datasets. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models (2025.coling-main)
Copied to clipboard
| Challenge: | prevailing notion of paraphrase is simplistic, offering only limited view of vast spectrum of paraphrasing phenomena. |
| Approach: | They propose a benchmarking tool for paraphrase detection that provides a fine-grained evaluation lens. |
| Outcome: | The proposed benchmark enables rapid calibration of models to specific strictness levels. |
Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)
Copied to clipboard
| Challenge: | a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining . |
| Approach: | They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages. |
| Outcome: | The proposed model outperforms the existing model on human evaluation in five of the six languages. |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
Copied to clipboard
| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
Copied to clipboard
| 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 . |
| Approach: | They propose an adversarial method for paraphrase identification that uses word overlap and syntax to identify paraphrases. |
| Outcome: | The proposed method improves paraphrase detection accuracy and speed of generation of datasets. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
Copied to clipboard
| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |