Emnlp

Highlighted work from our institute appearing at this year’s EMNLP conference — Empirical Methods in Natural Language Processing (EMNLP) is a leading conference in the area of natural language processing and artificial intelligence. Every year, AI2 is proud to have many of our researchers represent their work at this important event. Feeling overwhelmed by EMNLP 2022 proceedings? Looking for a faster way…

In the end 2021 there was a conference https://2021.emnlp.org/. EMNLP is one the main conferences for the one, who does NLP. Nevertheless, I couldn’t participate it offline in Dominican Republic, I could participate in online format. As moderators say, candidates all over the globe send their papers to have a…

This is a quick review of the conference EMNLP focusing on Summarization. EMNLP-2021-Paper-Digests (847 papers) Various Scenarios for Summarization News Reviews Chat Logs Meetings Mail Threads Scientific Articles Datasets

The latest EMNLP (’21) conference featured a tutorial titled Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection that was motivated by the fact that crowdsourcing annotations, so far, has been mostly guided by common practises and personal experiences, as there is very limited research on these practises to lean…

This article is a gist of the work (here) accepted at EMNLP Findings 2021. The link to the project website can be found here. This work primarily lies in the areas of Information Retrieval and Natural Language Processing, and has been jointly done by me, Soumya and Shubham from CNeRG…

Have a look at the newest paper on EMNLP 2020, introducing each paper on problem/solution/result perspective. — Machine Learning for NLP Seq2Edits: Sequence Transduction Using Span-level Edit Operations Paper: https://www.aclweb.org/anthology/2020.emnlp-main.418 Problem: speed up seq2seq generation via editing. Solution: highly overlap input target can be done by replace/delete/copy.

In this article, I am listing out most of the papers related to the discovery and mitigation of bias in NLP models, which were accepted in EMNLP 2020. I have divided these papers into three major categories alongside a separate category of all the new datasets released in these papers…

This post explains our paper on style transfer, “Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer” (presented at EMNLP 2019) in a more accessible manner. The paper can be found here. Our Github repo contains code and instructions for replicability. The What Our paper proposes a method to…