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".

Implementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.

Re-implementation of BIMPM (Bilateral Multi-Perspective Matching for Natural Language Sentences, Zhiguo Wang et al.) on Pytorch.

Repository for NLI models (EMNLP 2018)

Re-implementation of Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling (T. Shen et al., ICLR 2018) on Pytorch.

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

Unofficial implementation algorithms of attention models on SNLI dataset

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

Keras implementation (tensorflow backend) of natural language inference

PyTorch Implementation of "Learning Natural Language Inference with LSTM", 2016, S. Wang et al. (https://arxiv.org/pdf/1512.08849.pdf)

Models for Nature Language Inference (Tensorflow Version), including 'A Decomposable Attention Model for Natural Language Inference', ..., to be continued.

Implementation of the Character-level Intra Attention Network (CIAN) for Natural Language Inference (NLI) upon SNLI and MultiNLI corpus

Text pair classification

From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks

Pytorch implementations of several text semantic matching models. The repository currently contains ESIM, CAFE, RE2

Code developed for the RepEval 2017 Shared Task by the the team Rivercorners

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.