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
Memory-efficient Stochastic methods for Memory-based Transformers
vishwajit-vishnu/memory-efficient-stochastic-methods-for-memory-based-transformers
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Training Memory-based transformers can require a large amount of memory and can be quite inefficient.
SplitEE: Early Exit in Deep Neural Networks with Split Computing
Div290/SplitEE
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To overcome the issue, various approaches are considered, like offloading part of the computation to the cloud for final inference (split computing) or performing the inference at an intermediary layer without passing through all layers (early exits).
Do Multilingual Language Models Think Better in English?
In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models.
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding
boleima/profit
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We conduct comprehensive experiments on diverse cross-lingual language understanding tasks (sentiment classification, paraphrase identification, and natural language inference) and empirically analyze the variation trends of prompt-based finetuning performance in cross-lingual transfer across different few-shot and full-data settings.
Assessing Word Importance Using Models Trained for Semantic Tasks
Many NLP tasks require to automatically identify the most significant words in a text.
Cross-functional Analysis of Generalisation in Behavioural Learning
peluz/beluga
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In behavioural testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs.
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge
platanus-hy/sememes_codriven_text_matching
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Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs.
Improving word mover's distance by leveraging self-attention matrix
The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences.
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).
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models
hannahxchen/balanced-adversarial-training
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Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction.
