Probing handling of verbal probabilities in NLP models

✨ Build AI interfaces that spark joy

A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo

XNLIeu: a dataset for cross-lingual NLI in Basque

An AI assistant for a Learning Management System (LMS)

BERT and RoBERTa models fine-tuned on the MNLI dataset, optimized for binary entailment/non-entailment classification. Additionally, their performance is explored in handling figurative language.

PhoBERT: Pre-trained language models for Vietnamese (EMNLP-2020 Findings)

A Natural Language Inference (NLI) model based on Transformers (BERT and ALBERT)

Natural Language Understanding and Response engine. Semantic parser and execution engine. With DBPedia and SHRDLU demo's.

Testing Theory of Mind (ToM) in language models with epistemic logic

[EMNLP2022] BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples

Prove Ducth NLI problems of SICK-NL with LangPro

Tableau-based Theorem Prover for Natural Logic and Language

Project on a Natural Language Processing (NLP) task known as Natural Language Inference (NLI) for the Bahasa Indonesia language.

The dataset and code for ACL 2022 paper "SciNLI: A Corpus for Natural Language Inference on Scientific Text" are released here.

Welcome to the Neural Language Interface (NLI) Explain project! This repository is dedicated to exploring and explaining the decision-making process of BERT models in the context of Natural Language Inference (NLI) tasks. We employ Feature Interaction methods to shed light on why BERT makes specific predictions in NLI.

"Understanding Dataset Difficulty with V-Usable Information" (ICML 2022, outstanding paper) - AIC FEE CTU NLP group's customized version for NLI with XLM-RoBERTa Large in Czech language.

unsupervised creation of contradictory, entailing sentences from a given premise for SNLI tasks.

Assignments and projects from the interpretable natural language processing course offered at the University of Tehran.

Natural Language Inference problems specialized for spatial semantics & reasoning