Implementation of the ESIM model for natural language inference with PyTorch
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Updated
Aug 29, 2021 - Python
Implementation of the ESIM model for natural language inference with PyTorch
Source code of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".
Unofficial implementation algorithms of attention models on SNLI dataset
Repository for NLI models (EMNLP 2018)
Keras implementation (tensorflow backend) of natural language inference
Text pair classification
Models for Nature Language Inference (Tensorflow Version), including 'A Decomposable Attention Model for Natural Language Inference', ..., to be continued.
Implementation of the NLI model in our ACL 2019 paper: Augmenting Neural Networks with First-order Logic.
pytorch implementation of various models for snli and mnli task
PyTorch Implementation of "Learning Natural Language Inference with LSTM", 2016, S. Wang et al. (https://arxiv.org/pdf/1512.08849.pdf)
Implementation of the Character-level Intra Attention Network (CIAN) for Natural Language Inference (NLI) upon SNLI and MultiNLI corpus
Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models
Pytorch implementations of several text semantic matching models. The repository currently contains ESIM, CAFE, RE2
In this repository, we deal with the task of implementing Natural Language Inferencing (NLI) using the SNLI dataset. Different methods such as SumEmbeddings, BiLSTM, BiGRU, Transformers, and Logistic Regression are experimented.
Implementation of the InferSent paper by Conneau et al., EMNLP2017
From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks
End-to-end Siamese CNNs as feature selection for a CNN-based Entailment Modelling
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