4,841 research outputs found
Lessons learned in multilingual grounded language learning
Recent work has shown how to learn better visual-semantic embeddings by
leveraging image descriptions in more than one language. Here, we investigate
in detail which conditions affect the performance of this type of grounded
language learning model. We show that multilingual training improves over
bilingual training, and that low-resource languages benefit from training with
higher-resource languages. We demonstrate that a multilingual model can be
trained equally well on either translations or comparable sentence pairs, and
that annotating the same set of images in multiple language enables further
improvements via an additional caption-caption ranking objective.Comment: CoNLL 201
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the amount of boilerplate code to be written. nmtpy has been used for
LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News
Translation tasks in 2016 and 2017.Comment: 10 pages, 3 figure
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Recently there has been a lot of interest in learning common representations
for multiple views of data. Typically, such common representations are learned
using a parallel corpus between the two views (say, 1M images and their English
captions). In this work, we address a real-world scenario where no direct
parallel data is available between two views of interest (say, and )
but parallel data is available between each of these views and a pivot view
(). We propose a model for learning a common representation for ,
and using only the parallel data available between and
. The proposed model is generic and even works when there are views
of interest and only one pivot view which acts as a bridge between them. There
are two specific downstream applications that we focus on (i) transfer learning
between languages ,,..., using a pivot language and (ii)
cross modal access between images and a language using a pivot language
. Our model achieves state-of-the-art performance in multilingual document
classification on the publicly available multilingual TED corpus and promising
results in multilingual multimodal retrieval on a new dataset created and
released as a part of this work.Comment: Published at NAACL-HLT 201
- …