Seminars home
Adapting Transformer models for document-level natural language tasks
at
Seminars at NAVER LABS Europe are open to the public. Please register
Due to the COVID-19 situation, this seminar will not be held at NAVER LABS Europe but be online, conducted on Zoom.
Date: 25th June 2020, 4:00 PM-5:00 PM
Title: Adapting Transformer models for document-level natural language tasks
Speaker: Arman Cohan is a research scientist at the Allen Institute for AI. His research primarily focuses on developing NLP models with practical applications, especially in the scientific domain.
Prior to AI2, he obtained his PhD at Georgetown University in May 2018. His research has been recognized with best paper award at EMNLP 2017, honorable mention at COLING 2018, and Harold N. Glassman Distinguished Doctoral Dissertation award in 2019.
Abstract:
Representation learning forms the foundation of today’s natural language processing system; Transformer models have been extremely effective at producing word- and sentence-level contextualized representations, achieving state-of-the-art results in many NLP tasks. However, applying these models to produce contextualized representations of the entire documents faces challenges. These challenges include lack of inter-document relatedness information, decreased performance in low-resource settings, and computational inefficiency when scaling to long documents.In this talk, I will describe 3 recent works on developing Transformer-based models that target document-level natural language tasks.
I will first introduce SPECTER, a method for producing scientific document representations using a Transformer model that incorporates document-level relatedness signals and achieves state-of-the-art results in multiple document-level tasks. Second, I will describe TLDR, the task of extreme summarization for scientific papers. I will introduce SciTLDR, our new multi-target summarization dataset of scientific papers as well as a simple yet effective training strategy for generating summaries in low-resource setting. Finally, I will discuss the practical challenges of applying existing Transformer models to long documents, and our proposed solution, Longformer. Longformer, introduces a new sparse self-attention pattern that scales linearly with the input length while capturing both local and global context in the document, and achieves state-of-the-art results in both character-level language modeling and document NLP tasks.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
Cookie settings
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.