There are 0 repository under acl2021 topic.
Collection of papers and resources for data augmentation for NLP.
[ACL-IJCNLP 2021] Automated Concatenation of Embeddings for Structured Prediction
[NAACL'21 & ACL'21] SapBERT: Self-alignment pretraining for BERT & XL-BEL: Cross-Lingual Biomedical Entity Linking.
CogIE: An Information Extraction Toolkit for Bridging Text and CogNet. ACL 2021
Codebase, data and models for the Keep it Simple paper at ACL2021
[ACL-IJCNLP 2021] Self-Supervised Multimodal Opinion Summarization
Code for the NLP4Prog workshop paper "Reading StackOverflow Encourages Cheating: Adding Question TextImproves Extractive Code Generation"
code for the paper "UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction"
The official repository for "Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP" published in ACL-IJNLP 2021.
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization (ACL 2021)
Code for Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution (ACL2021)
[ACL 2021] Self-Attention Networks Can Process Bounded Hierarchical Languages
[ACL 2021, Findings] Cognate Prediction Per Machine Translation
Official implementation of "Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path" (ACL'21)
This repo contains datasets and code for On the Interplay Between Fine-tuning and Composition in Transformers, by Lang Yu and Allyson Ettinger.
Official repository for the paper titled "From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text" accepted at ACL 2021
Implementation of the paper "How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements"
Evaluating the Efficacy of Summarization Evaluation across Languages. In Findings of ACL 2021.
Multi-Scale Progressive Attention Network for Video Question Answering
Python scripts and Jupyter Notebook analysis of ACL Festival 2021 StubHub ticket data.
Make a bar graph of points each team got and will get.