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Paraphrase Identification

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평가표: 11
보존된 평가 기록: 44

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt

공식 코드

Improving Paraphrase Detection with the Adversarial Paraphrasing Task

https://github.com/HazyResearch/metal

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Training Complex Models with Multi-Task Weak Supervision

https://github.com/Maluuba/gensen

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Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

https://github.com/McGill-NLP/latent-translation

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Modelling Latent Translations for Cross-Lingual Transfer

https://github.com/ShannonAI/Self_Explaining_Structures_Improve_NLP_Models

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Self-Explaining Structures Improve NLP Models

https://github.com/ShannonAI/dice_loss_for_NLP

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Dice Loss for Data-imbalanced NLP Tasks

https://github.com/UVa-NLP/GMASK

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Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

https://github.com/YichenGong/Densely-Interactive-Inference-Network

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Natural Language Inference over Interaction Space

https://github.com/aditya-choudhary599/Marathi-Plagiarism-Detection

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Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing

https://github.com/amzn/trans-encoder

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Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

https://github.com/boleima/profit

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Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding

https://github.com/cluebbers/dpo-rlhf-paraphrase-types

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Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data

https://github.com/csong27/collision-bert

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Adversarial Semantic Collisions

https://github.com/dinghanshen/SWEM

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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

https://github.com/facebookresearch/SentEval

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Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

https://github.com/facebookresearch/SpanBERT

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SpanBERT: Improving Pre-training by Representing and Predicting Spans

https://github.com/gingasan/adversarialsa

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Adversarial Self-Attention for Language Understanding

https://github.com/google-research-datasets/paws

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PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification

https://github.com/google-research/bert

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/google-research/google-research

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RealFormer: Transformer Likes Residual Attention

https://github.com/hannahxchen/balanced-adversarial-training

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Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models

https://github.com/heyunh2015/PARADE_dataset

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PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

https://github.com/hitvoice/RE2

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Simple and Effective Text Matching with Richer Alignment Features

https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT

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TinyBERT: Distilling BERT for Natural Language Understanding

https://github.com/j4vorsky/word-importance

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Assessing Word Importance Using Models Trained for Semantic Tasks

https://github.com/jpcorb20/bet-backtranslation-paraphrase-experiment

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BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context

https://github.com/juletx/self-translate

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Do Multilingual Language Models Think Better in English?

https://github.com/kwang2049/pytorch-bertflow

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TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/kwang2049/useb

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TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/lanwuwei/SPM_toolkit

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Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

https://github.com/lifu-tu/Study-NLP-Robustness

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An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

https://github.com/mklimasz/transferable-modularity

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Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation

https://github.com/muhaochen/bilingual_dictionaries

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Learning to Represent Bilingual Dictionaries

https://github.com/namisan/mt-dnn

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Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/peluz/beluga

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Cross-functional Analysis of Generalisation in Behavioural Learning

https://github.com/platanus-hy/sememes_codriven_text_matching

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Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.