Code and data for the EMNLP 2020 paper "Intrinsic Probing through Dimension Selection".
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Updated
Nov 26, 2020 - Python
Code and data for the EMNLP 2020 paper "Intrinsic Probing through Dimension Selection".
Source Code for "Teaching Machine Comprehension with Compositional Explanations" (Findings of EMNLP 2020)
[EMNLP'20][Findings] Official Repository for the paper "Why and when should you pool? Analyzing Pooling in Recurrent Architectures."
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing
Code for the paper "META: Metadata-Empowered Weak Supervision for Text Classification"
This repository is for the paper Recurrent Inference in Text Editing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1758–1769, Online. Association for Computational Linguistics.
Finding the Optimal Vocabulary for NMT
Entity and relation recognition over wet-lab protocols
Repository containing the experimental code for the publication 'Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks' (Emelin, Denis, Ivan Titov, and Rico Sennrich, EMNLP 2020).
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (EMNLP2020)
This repo contains datasets and code for Assessing Phrasal Representation and Composition in Transformers, by Lang Yu and Allyson Ettinger.
Codebase for "Decoding language spatial relations to 2D spatial arrangements" (Findings of EMNLP 2020).
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.
PyTorch code of “Out-of-Sample Representation Learning for Multi-Relational Graphs” (EMNLP 2020)
Code for GenAug: Data Augmentation for Finetuning Text Generators.
A PyTorch implementation of SSCR
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.
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