University of Amsterdam: SLPLL

Gideon Maillette de Buy Wenniger

Gideon Maillette de Buy Wenniger

M.Sc. in Artificial Intelligence, Intelligent Systems
PhD. in computer science from the University of Amsterdam
Postdoctoral researcher in the ADAPT center at Dublin City University
Marie Skłodowska-Curie EDGE Fellow in the ADAPT center at Dublin City University
ADAPT Centre at DCU
Supervised by Prof. Andy Way

Visiting Address: 

   Dublin City University
   ADAPT Centre
   McNulty Building (L), second floor
   Dublin City University, Glasnevin
   Dublin, 9, Ireland

Postal Address: 

   Dublin City University
   ADAPT Centre
   McNulty Building (L), Room L2.01B
   Dublin City University, Glasnevin
   Dublin, 9, Ireland

Email: gemdbw _at_

Office: Room L2.01B

About me: 

I received my Master diploma (Cum Laude) from the University of Amsterdam in November 2008, after completing a innovative Master Thesis project at the EPFL Lausanne. During my Master thesis research in Lausanne I worked on gesture recognition with Hidden Markov Models. Back in Amsterdam I completed another extensive research project on the topic of obstacle and free space recognition for robot navigation. I published this research together with Dr. Arnoud Visser and Tijn Schmits and presented it in September 2009 at the ECMR conference in Croatia.Starting from may 2009 I worked in Germany at the Rheinische Friedrich-Wilhelms-Universität Bonn. We participated in the Robocup at Home competition of the International Robocup, and in our team I was responsible for our visual methods for people detection and people recognition. After the Robocup I worked on visual methods for object recognition using visual features (SIFT) and support vector machines. On 1 June 2010 I started my work on Statistical Machine Translation at the Institute for Logic, Language and Computation with Dr. Khalil Sima'an , in his project "Machine Translation When Exact Pattern Match Fails" funded by NWO Exact Sciences Free Competition . Following my PhD, I worked as a postdoctoral researcher in ILLC until October 2016. In June 2016 I defended my PhD titled "Aligning the Foundations of Hierarchical Statistical Machine Translation". In November 2016 I started a postdoc with Prof. Andy Way in ADAPT, at Dublin City University, working on hierarchical statistical machine translation and neural machine translation. In 2017, I obtained a Marie Skłodowska-Curie EDGE Grant for my project BAIT: Bilingual Association in Neural Machine Translation EDGE project ] , and in May 2017 I started working on this project. Over the last year I invested to become a deep learning expert and expert in pytorch programming, which allows me to implement deep learning models, when necessary from the ground up. This investment is currently starting to pay off, opening up new opportunities for multi-modal deep learning for neural machine translation and handwritten text recognition.

Research Interests and Current Work: 

My research interests include machine translation (including syntax, morphology and semantics), handwritten text recognition, deep learning, computer vision and general machine learning. My current work focuses on developing new models and techniques for neural machine translation and (neural) handwritten text recognition. I have a special interest in applying multi-modal techniques to take models in both fields to the next level. More information about my current EDGE project can be found at [ EDGE project ]

My Master thesis project consisted of two parts. In the first part I automatically analyzed the structure of Hidden Markov Models (HMMs), and used it to automatically segment gesture sequences into the underlying primitive gestures. In the second part of my project I developed a technique to automatically merge or compress gesture models (HMMs).


PhD thesis:  

Diploma thesis:  



Selected older reports from Bachelor and Master:  

Recent Developments: 


Our new paper "No Padding Please: Efficient Neural Handwriting Recognition", which proposes new methods for efficient neural handwriting recognition with multi-dimenisional long short-term memories (MDLSTMs) is now on arXiv. This work also involves an efficient reimplementation of MDLSTMs from scratch in PyTorch, and a large number of experiments and comparisons against literature results on the popular multi-writer IAM (handwriting) database.


Our paper "Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods" got accepted at CICLing 2019.


Presented the continued work on handwriting recognition with minimal padding in an invited talk for the research team lead by Prof. Dr. Ing. Rozenn DAHYOT, at Trinity College Dublin.


Presented our work on handwriting recognition with minimal padding at CLIN 2019 in Groningen. Bladie
CLIN 2019 website ]


Two of our papers got accepted at EAMT 2018 [ EAMT 2018 ].


Made a fix for loading of the optimizer state for Adam in opennmt_py. For the opennmt neural machine translation project [ OpenNMT main website ]
Issue and fix in the opennmt_py open neural machine translation repository ].


Presented new paper "Elastic-substitution decoding for Hierarchical SMT: efficiency, richer search and double labels" at MT Summit, in Nagoya, Japan. [ MT Summit 2017 ].

17-7-2017 -- 21-7-2017

Attended the International Summer School on Deep Learning 2017 in Bilbao, Spain [ DeepLearn 2017 ]. Bladie

6-8-2010 - Added support for m-n alignments to the tool

M-n alignments


Based on software developed by Federico Sangati and in close collaboration with him, I developed an extension to his Tree visualization tool to allow the simultaneous visualization of source parse trees and the associated word alignments for SMT. Alignment Tree Viewer

Tree Alignment Violations 

Recently a feature was added that allows visualizing the alignment constraint violations, assuming a reordering model that allows children of every node only to be permuted. Once a given source node n and its descending terminals "claim" a certain range in the target sentence, any source word outside the subtree rooted at n that tries to align within the same range, causes a crossing of alignments and an alignment violation. Alignment violations are indicated by pink, the offending words are drawn in pink and aligned by striped alignment lines for clearity. Furthermore, the words that cause the alignment violations with a certain subtree are indicated behind the root node of this subtree. We are still thinking how to optimize the visualization for clarity and avoiding overlap with parts of the tree.
The original alignment:
Alignment Tree Viewer
The alignment with viualization of constraint violations turned on:
Alignment Tree Viewer

23-2-2010 As a next step I implemented a method to perform and visualize the reordering of the tree by means of child node permutations, bringing the source words as far as possible in the order of the aligned target words under the constraint that only tree child node permutations may be performed. First I restricted the permutations to only non-violated nodes (those whose "claimed" alignment span is not aligned to by other source words outside the subtree rooted at that node), however, as can be seen from the example below, any reordering that is allowed under the child node permutation constraints and improves the order should probably just be done. Reordering visualization

28-7-2010 In addition to adding colors to better emphasize the reordered nodes, I added the functionality of batch processing of the sentences for reordering. Furthermore I added a configfile to the system, which makes it possible to automatically load everything without cumbersome manual file selection (which really gets nerving after some time...). The result is shown below. Reordering visualization
- Put tool on google code project ,

Download Alignment Visualizer Package ]

University of Amsterdam, Science Faculty Institute for Logic Language and Computation