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Naver Labs Europe’s systems for the document-level generation and translation task at WNGT 2019
at
| Fahimeh Saleh, Alexandre Berard, Ioan Calapodescu, Laurent Besacier |
| Workshop on Neural Generation and Translation (WNGT), EMNLP, Hong Kong, China, 4 November, 2019 |
@article{saleh2019naver,
title={Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019},
author={Saleh, Fahimeh and B{\'e}rard, Alexandre and Calapodescu, Ioan and Besacier, Laurent},
journal={arXiv preprint arXiv:1910.14539},
year={2019}
}Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation (NLG) tasks.
However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with access to metadata. First, we focus on training a document-based MT system with the DGT parallel data. Then, we augment this MT model to obtain a “Data + Text to Text” MT model. Finally, we remove the text part of the input to obtain a pure NLG system, able to translate metadata to full documents. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG dataset.
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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