Statistics and accepted paper list of NLP conferences with arXiv link

This is official Pytorch code and datasets of the paper "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News", EMNLP 2020.

The implementation of the papers on dual learning of natural language understanding and generation. (ACL2019,2020; Findings of EMNLP 2020)

GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

EMNLP2020 findings paper: Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction

PyTorch code for EMNLP 2020 paper "X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers"

Data and code for our paper "Exploring and Predicting Transferability across NLP Tasks", to appear at EMNLP 2020.

Code accompanying EMNLP 2020 paper "Cold-start Active Learning through Self-supervised Language Modeling".

Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (EMNLP 2020)

[EMNLP 2020] Collective HumAn OpinionS on Natural Language Inference Data

Code for GenAug: Data Augmentation for Finetuning Text Generators.

A PyTorch implementation of SSCR

Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (EMNLP2020)

This repository contains the code accompanying the paper "Learning Informative Representations of Biomedical Relations with Latent Variable Models", Harshil Shah and Julien Fauqueur, EMNLP SustaiNLP 2020.

Code for "Data-to-text Generation with Style Imitation." [Findings of EMNLP 2020]

Research code and scripts used in the paper Semantic Role Labeling as Syntactic Dependency Parsing.

code for our EMNLP2020 paper: Multilevel Text Alignment with Cross-Document Attention by Xuhui Zhou, Nikolaos Pappas, and Noah A. Smith

Source Code for "Teaching Machine Comprehension with Compositional Explanations" (Findings of EMNLP 2020)

This repo contains datasets and code for Assessing Phrasal Representation and Composition in Transformers, by Lang Yu and Allyson Ettinger.