Papers by Thomas Wolf
Transfer Learning in Natural Language Processing (N19-5)
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| Challenge: | supervised machine learning is based on learning in isolation, a single predictive model for a task using a dataset. |
| Approach: | They present an overview of modern transfer learning methods in natural language processing . they review examples and case studies on how models can be integrated and adapted . |
| Outcome: | The proposed methods improve upon the state-of-the-art on a wide range of NLP tasks. |
Datasets: A Community Library for Natural Language Processing (2021.emnlp-demo)
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Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, Thomas Wolf
| Challenge: | Contemporary NLP systems use many different datasets at significantly varying scale and level of annotation. |
| Approach: | a community library for contemporary NLP is available at https://github.com/datasets . the library includes more than 650 unique datasets and has more than 250 contributors a year after its initial development . |
| Outcome: | the library includes more than 650 unique datasets and has more than 250 contributors . it supports a variety of cross-dataset research projects and shared tasks . |
Continuous Learning in a Hierarchical Multiscale Neural Network (P18-2)
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| Challenge: | Language models are a major class of natural language processing (NLP) models whose development has led to major progress in many areas like translation, speech recognition or summarization. |
| Approach: | They propose a hierarchical multi-scale language model where short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time- scale dependencies can be encoded into the dynamic of the lower- level network. |
| Outcome: | The proposed model uses a meta-learner to update the weights of the lower-level neural network in an online meta-learning fashion to prevent catastrophic forgetting in the continuous learning framework. |
Transformers: State-of-the-Art Natural Language Processing (2020.emnlp-demos)
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush
| Challenge: | Transformers is an open-source library that aims to open up advances in natural language processing to the wider machine learning community. |
| Approach: | they propose an open-source library that aims to open up advances in machine learning to the wider community. |
| Outcome: | Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. |
Large-Scale Transfer Learning for Natural Language Generation (P19-1)
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Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf
| Challenge: | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. |
| Approach: | They propose to apply large-scale pretrained language models to natural language generation tasks by comparing architectural and training schemes. |
| Outcome: | The proposed architectures perform well on open-domain dialog as a typical high entropy generation task. |
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
| Approach: | They will discuss how and why NLG models succeed/fail at generating coherent text. |
| Outcome: | This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications. |
FinGPT: Large Generative Models for a Small Language (2023.emnlp-main)
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Risto Luukkonen, Ville Komulainen, Jouni Luoma, Anni Eskelinen, Jenna Kanerva, Hanna-Mari Kupari, Filip Ginter, Veronika Laippala, Niklas Muennighoff, Aleksandra Piktus, Thomas Wang, Nouamane Tazi, Teven Scao, Thomas Wolf, Osma Suominen, Samuli Sairanen, Mikko Merioksa, Jyrki Heinonen, Aija Vahtola, Samuel Antao, Sampo Pyysalo
| Challenge: | Neural language models excel in many tasks in NLP but are limited to smaller languages. |
| Approach: | They propose two approaches to pretrain large language models for Finnish . they train seven monolingual models from scratch and use Finnish as pretraining data . |
| Outcome: | The proposed model is based on a dataset of Finnish web crawls, news, social media and eBooks. |