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
Most implemented papers
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
However, due to limited data resources from downstream tasks and the extremely large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model.
Scaling Instruction-Finetuned Language Models
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
RealFormer: Transformer Likes Residual Attention
google-research/google-research
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Findings (ACL) 2021
Transformer is the backbone of modern NLP models.
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
facebookresearch/SentEval
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ICLR 2018
In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.
A Deep Relevance Matching Model for Ad-hoc Retrieval
sebastian-hofstaetter/neural-ranking-drmm
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Specifically, our model employs a joint deep architecture at the query term level for relevance matching.
Simple and Effective Text Matching with Richer Alignment Features
hitvoice/RE2
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ACL 2019
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
Most existing work on adversarial data generation focuses on English.
Entailment as Few-Shot Learner
Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners.
Natural Language Inference over Interaction Space
YichenGong/Densely-Interactive-Inference-Network
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ICLR 2018
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
PAWS: Paraphrase Adversaries from Word Scrambling
Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases.
