generalized_paraphrase_identification

Implementation of the paper 'GAPX: Generalized Autoregressive Paraphrase-identification X'

NeurIPS 2022

An ensemble model for paraphrase identification robust to distribution shift.

Requirements

• GPU
• requirements.txt

Dataset

Please download the following paraphrase identification datasets:

• Quora Question Pair : https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs
• Paraphrase and Semantic Similarity on Twitter: https://github.com/cocoxu/SemEval-PIT2015
• Paraphrase Adversarials from Word Scrambling (en): https://www.statmt.org/wmt17/metrics-task.html
• WMT 17: https://www.statmt.org/wmt17/metrics-task.html

Usage

To train and evaluate a paraphrase identification model, run:

python run.py --source_dataset [QQP, PIT, PAWS] --option [naive, robust]

Here we implemented a simplified version from the paper, where for the discriminative model, we use BART instead of RoBERTa

Results

You should expect to see something similar to this (f1/acc/auc):

Command	QQP->QQP	QQP->WMT	QQP->PAWS	QQP->PIT
python run.py --source_dataset QQP --option naive	83.4/83.5/91.2	66.7/66.8/74.2	44.7/49.8/57.1	63.6/66.5/82.0
python run.py --source_dataset QQP --option robust	83.1/83.2/88.4	74.4/74.7/79.3	56.6/56.9/59.5	62.3/63.6/73.5