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Joint semantic and distributional word representations with multi-graph embeddings
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| Pierre Daix-Moreux, Matthias Gallé |
| TextGraphs-2019, 13th Workshop on Graph-Based Natural Language Processing, EMNLP, Hong Kong, China, 4 November, 2019 |
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@inproceedings{daix2019joint,
title={Joint Semantic and Distributional Word Representations with Multi-Graph Embeddings},
author={Daix-Moreux, Pierre and Gall{\'e}, Matthias},
booktitle={Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)},
pages={118--123},
year={2019}
}Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases with the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping all information into a multigraph and modifying existing node embeddings techniques to compute word representation. Our experiments show improved results than vanilla word embeddings and retrofitting techniques using the same information, on a variety of data-sets of word similarities.
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.
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