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André Martins

VP of Artificial Intelligence Research

André Martins is the VP of AI Research at Unbabel, an invited professor at Instituto Superior Técnico, and a member of the Instituto de Telecomunicações. He received his dual-degree Ph.D. in Language Technologies in 2012 from Carnegie Mellon University and the University of Lisbon. His research interests include natural language processing, machine translation, machine learning, and deep learning. He has published 30+ papers with over 1700 citations and h-index of 21 in top-tier conferences and journals (such as Computational Linguistics, JMLR, PAMI, ACL, ICML, and EMNLP), including a best paper award at the Annual Meeting of the Association for Computational Linguistics (ACL) for his work in natural language syntax. His Ph.D. dissertation has been awarded an SCS Honorable Mention at CMU. He won the Portuguese IBM Scientific Prize in 2012. André is one of the co-founders and organizers of the Lisbon Machine Learning Summer School (LxMLS).

Papers published

2020

Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings

Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT) 2020

2019

Adaptively Sparse Transformers

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019

Translator2Vec: Understanding and Representing Human Post-Editors

Proceedings of Machine Translation Summit XVII Volume 1: Research Track 2019

Unbabel's Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) 2019

Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) 2019

Sparse Sequence-to-Sequence Models

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019

Scheduled Sampling for Transformers

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop 2019

OpenKiwi: An Open Source Framework for Quality Estimation

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations 2019

Latent Structure Models for Natural Language Processing

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts 2019

Joint Learning of Named Entity Recognition and Entity Linking

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop 2019

A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019

Selective attention for context-aware neural machine translation.

Conference of the North American Chapter of the Association for Computational Linguistics (NAACL'19) 2019

Learning classifiers with Fenchel-Young losses: Generalized entropies, margins, and algorithms

International Conference on Artificial Intelligence and Statistics (AISTATS'19) 2019

2018

Interpretable Structure Induction via Sparse Attention

Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP 2018

Towards Dynamic Computation Graphs via Sparse Latent Structure

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018

Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations

Proceedings of the Third Conference on Machine Translation: Research Papers 2018

SparseMAP: Differentiable Sparse Structured Inference.

International Conference on Machine Learning (ICML'18) 2018

Sparse and Constrained Attention for Neural Machine Translation.

Annual Meeting of the Association for Computational Linguistics (ACL'18) 2018

Marian: Fast Neural Machine Translation in C++

  • Marcin Junczys-Dowmunt
  • Roman Grundkiewicz
  • Tomasz Dwojak
  • Hieu Hoang
  • Kenneth Heafield
  • Tom Neckermann
  • Frank Seide
  • Ulrich Germann
  • Alham Aji
  • Nikolay Bogoychev
  • André Martins
  • Alexandra Birch

Proceedings of ACL 2018, System Demonstrations 2018

2017

The Interact Project and Crisis MT.

XVI Machine Translation Summit 2017

Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task

Proceedings of the Second Conference on Machine Translation 2017

Learning What's Easy: Fully Differentiable Neural Easy-First Taggers

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 2017

Pushing the Limits of Translation Quality Estimation

Transactions of the Association for Computational Linguistics 2017

2016

Semi-Supervised Learning of Sequence Models with Method of Moments

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 2016

Unbabel's Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers 2016

Jointly Learning to Embed and Predict with Multiple Languages

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2016

Projects

  • DeepSPIN

    Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.

  • Unbabel4EU

    We’re working on advancing European language engines for borderless business communication. Create Europe’s Translation Layer, specifically, by enabling seamless human-quality translation between any pairing of the 24 official languages of the EU in different content types such as Email, Chat and Listings (P2020 Co-Funded Project).

  • User-Focused Marian

    Improve the pre-existing neural machine translation toolkit “Marian” to address the needs of CEF eTranslation and to broaden its user base (H2020 Co-Funded Project). Terminology, on-the-fly domain adaptation, better documentation and GPU optimization are the focus areas in this Marian iteneration.