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
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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
Predicate-Argument Based Bi-Encoder for Paraphrase Identification
Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings.
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data.
How much pretraining data do language models need to learn syntax?
This calls for a study of the impact of pretraining data size on the knowledge of the models.
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification
Question Paraphrase Identification (QPI) is a critical task for large-scale Question-Answering forums.
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models
Consistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding model.
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP
Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks.
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching
We then employ a novel gated graph attention network to encode the constructed graph for sentence matching.
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification
Question paraphrase identification is a key task in Community Question Answering (CQA) to determine if an incoming question has been previously asked.
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset
The dataset that we use is provided by Quora.
