Section: Partnerships and Cooperations

International Initiatives

Inria International Labs

Inria@SiliconValley

Associate Team involved in the International Lab:

LEGO

• Title: LEarning GOod representations for natural language processing

• International Partner (Institution - Laboratory - Researcher):

  • USC (United States), Prof. Fei Sha.

• Start year: 2016

• See also: https://team.inria.fr/lego/

• LEGO lies in the intersection of Machine Learning and Natural Language Processing (NLP). Its goal is to address the following challenges: what are the right representations for structured data and how to learn them automatically, and how to apply such representations to complex and structured prediction tasks in NLP? In recent years, continuous vectorial embeddings learned from massive unannotated corpora have been increasingly popular, but they remain far too limited to capture the complexity of text data as they are task-agnostic and fall short of modeling complex structures in languages. LEGO strongly relies on the complementary expertise of the two partners in areas such as representation/similarity learning, structured prediction, graph-based learning, and statistical NLP to offer a novel alternative to existing techniques. Specifically, we will investigate the following three research directions: (a) optimize the embeddings based on annotations so as to minimize structured prediction errors, (b) generate embeddings from rich language contexts represented as graphs, and (c) automatically adapt the context graph to the task/dataset of interest by learning a similarity between nodes to appropriately weigh the edges of the graph. By exploring these complementary research strands, we intend to push the state-of-the-art in several core NLP problems, such as dependency parsing, coreference resolution and discourse parsing.

Inria Associate Teams Not Involved in an Inria International Labs

• North-European Associate Team PAD-ML: Privacy-Aware Distributed Machine Learning.

• International Partner: the PPDA team at the Alan Turing Institute.

• Start year: 2018

• In the context of increasing legislation on data protection (e.g., the recent GDPR), an important challenge is to develop privacy-preserving algorithms to learn from datasets distributed across multiple data owners who do not want to share their data. The goal of this joint team is to devise novel privacy-preserving, distributed machine learning algorithms and to assess their performance and guarantees in both theoretical and practical terms.