Jan 29 2018

Focus on a joint research project: LEGO

LEGO (Since 2016)

 LEarning GOod representations for natural language processing  

Principal Investigators: 

  • Aurélien Bellet, MAGNET project-team, Inria Lille
  • Fei Sha, University of Southern California, Dpt of Computer Science (formerly with TEDS,UCLA)

Research objectives:

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? LEGO strongly relies on the complementary expertise of the two partners in areas such as representation learning, structured prediction, graph-based learning, and statistical NLP to offer a novel alternative to existing techniques. The team intends to push the state-of-the-art in several core NLP problems, such as dependency parsing, coreference resolution and discourse parsing.

Scientific achievements:

The contributions of LEGO span several research directions. The team has proposed methods to learn/adapt word representations for specific tasks (implicit discourse relation identification, dependency parsing, text classification), sometimes exploiting richer language contexts than simple word co-occurrences. LEGO has also developed methods to transfer word representations between related tasks. Finally, the team is developing approaches to learn word embeddings from multi-modal inputs (such as representation of visual objects extracted from images, in addition to the text corpora).

Publications and Awards:

  • 5 conference papers
  • First joint article in preparation

Selected Publication:

  • Mathieu Dehouck and Pascal Denis. Delexicalized Word Embeddings for Cross-lingual Dependency Parsing. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2017.
  •  Soravit Changpinyo, Wei-Lun Chao and Fei Sha. Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.