Paraphrase Identification
72 papers with code • 10 benchmarks • 17 datasets
The goal of Paraphrase Identification is to determine whether a pair of sentences have the same meaning.
Source: Adversarial Examples with Difficult Common Words for Paraphrase Identification
Image source: On Paraphrase Identification Corpora
Benchmarks
These leaderboards are used to track progress in Paraphrase Identification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Paraphrase Identification models and implementations
IMDb Movie Reviews
PAWS-X
PAWS
Yelp
WikiHop
Quora Question Pairs
TURL
Latest papers with no code
Pointwise Paraphrase Appraisal is Potentially Problematic
The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases.
Cross-Lingual Adaptation Using Universal Dependencies
In this paper, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages.
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding
Prior to the transformer era, bidirectional Long Short-Term Memory (BLSTM) has been the dominant modeling architecture for neural machine translation and question answering.
Matching Text with Deep Mutual Information Estimation
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem.
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching
Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target.
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query.
Robustness to Modification with Shared Words in Paraphrase Identification
Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations.
A Qualitative Evaluation Framework for Paraphrase Identification
In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems.
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.
