141 research outputs found
OSU Multimodal Machine Translation System Report
This paper describes Oregon State University's submissions to the shared
WMT'17 task "multimodal translation task I". In this task, all the sentence
pairs are image captions in different languages. The key difference between
this task and conventional machine translation is that we have corresponding
images as additional information for each sentence pair. In this paper, we
introduce a simple but effective system which takes an image shared between
different languages, feeding it into the both encoding and decoding side. We
report our system's performance for English-French and English-German with
Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves
the best performance in TER for English-German for MSCOCO dataset.Comment: 5, WMT 201
Group Sparse CNNs for Question Classification with Answer Sets
Question classification is an important task with wide applications. However,
traditional techniques treat questions as general sentences, ignoring the
corresponding answer data. In order to consider answer information into
question modeling, we first introduce novel group sparse autoencoders which
refine question representation by utilizing group information in the answer
set. We then propose novel group sparse CNNs which naturally learn question
representation with respect to their answers by implanting group sparse
autoencoders into traditional CNNs. The proposed model significantly outperform
strong baselines on four datasets.Comment: 6, ACL 201
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.Comment: this paper has been accepted by ACL 201
- …