Code of paper Exploring Task Difficulty for Few-Shot Relation Extraction. https://arxiv.org/abs/2109.05473

Repository for "Uncertainty-Aware Machine Translation Evaluation", accepted to Findings of EMNLP 2021.

Unsupervised Contextualized Document Representation, to appear in SustaiNLP 2021 EMNLP 2021

Code for the article "Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning", Outstanding Paper at EMNLP2021

Code for paper Document-Level Paraphrase Generation with Sentence Rewriting and Reordering by Zhe Lin, Yitao Cai and Xiaojun Wan. This paper is accepted by Findings of EMNLP'21.

Research code for EMNLP 2021 Paper "Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models"

This repository contains the dataset and codes for the task of Morality Frames prediction in political tweets using Relational Learning. This work is published as a paper - "Identifying Morality Frames in Political Tweets using Relational Learning" (EMNLP'2021).

Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP 2021)

Repository for "Uncovering the Limits of Text-based Emotion Detection", — Findings of EMNLP, 2021

Matching The Statements: A Simple and Accurate Model for Key Point Analysis (ArgMining | EMNLP 2021)

EMNLP2021 - DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer

A monolingual and cross-lingual meta-embedding generation and evaluation framework

source code of our RaNet in EMNLP 2021

[EMNLP'21] Mirror-BERT: Converting Pretrained Language Models to universal text encoders without labels.

Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)

This repository is for the paper Counterfactual Adversarial Learning with Representation Interpolation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4809–4820, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Research code and scripts used in the Silburt et al. (2021) EMNLP 2021 paper 'FANATIC: FAst Noise-Aware TopIc Clustering'

Uncertainty-Aware Encoder (Findings of EMNLP 2021)

Repository supporting an accepted paper at EMNLP 2021 on contrastive refinement.