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adapt_vs_finetune icon adapt_vs_finetune

This repository contains code for paper "To Adapt or to Fine-tune: A Case Study on Abstractive Summarization" which appears in CCL 2022.

allennlp icon allennlp

An open-source NLP research library, built on PyTorch.

cnn-dailymail icon cnn-dailymail

Code to obtain the CNN / Daily Mail dataset (non-anonymized) for summarization (Python3)

fastai icon fastai

The fastai deep learning library, plus lessons and tutorials

herman icon herman

This repository contains code for the EMNLP 2020 Findings paper "Reducing Quantity Hallucinations in Abstractive Summarization".

iaml2017 icon iaml2017

Introductory Applied Machine Learning (INFR09029)

joint_multilingual_analysis icon joint_multilingual_analysis

This repository contains code for the paper "A Joint Matrix Factorization Analysis of Multilingual Representations" which appears in EMNLP2023 Findings

multilingual-typology-probing icon multilingual-typology-probing

Code and data for the NAACL 2022 paper "Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models".

opennmt-py icon opennmt-py

Open Source Neural Machine Translation in PyTorch

pointer-generator icon pointer-generator

Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks" (Python3)

presumm icon presumm

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

qdap icon qdap

Quantitative Discourse Analysis Package: Bridging the gap between qualitative data and quantitative analysis

revision icon revision

Study materials created by myself for revision for University of Edinburgh's School of Informatics' exams.

structural-correspondence-learning-scl icon structural-correspondence-learning-scl

The code base for the SCL implementation used in "Neural Structural Correspondence Learning for Domain Adaptation", CoNLL 2017 and in "Pivot Based Language Modeling for Improved Neural Domain Adaptation", NAACL 2018

subpopulation_analysis icon subpopulation_analysis

This repository contains code for the paper "Understanding Domain Learning in Language Models Through Subpopulation Analysis" which appears in BlackBox 2022

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