10,438 research outputs found
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning word representations from character and morpheme
level word decompositions. We incorporate these representations in a novel
machine translation model which jointly learns word alignments and translations
via a hard attention mechanism. Evaluating on translating from several
morphologically rich languages into English, we show consistent improvements
over strong baseline methods, of between 1 and 1.5 BLEU points
MAT: A Multimodal Attentive Translator for Image Captioning
In this work we formulate the problem of image captioning as a multimodal
translation task. Analogous to machine translation, we present a
sequence-to-sequence recurrent neural networks (RNN) model for image caption
generation. Different from most existing work where the whole image is
represented by convolutional neural network (CNN) feature, we propose to
represent the input image as a sequence of detected objects which feeds as the
source sequence of the RNN model. In this way, the sequential representation of
an image can be naturally translated to a sequence of words, as the target
sequence of the RNN model. To represent the image in a sequential way, we
extract the objects features in the image and arrange them in a order using
convolutional neural networks. To further leverage the visual information from
the encoded objects, a sequential attention layer is introduced to selectively
attend to the objects that are related to generate corresponding words in the
sentences. Extensive experiments are conducted to validate the proposed
approach on popular benchmark dataset, i.e., MS COCO, and the proposed model
surpasses the state-of-the-art methods in all metrics following the dataset
splits of previous work. The proposed approach is also evaluated by the
evaluation server of MS COCO captioning challenge, and achieves very
competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40)
Character-level Transformer-based Neural Machine Translation
Neural machine translation (NMT) is nowadays commonly applied at the subword
level, using byte-pair encoding. A promising alternative approach focuses on
character-level translation, which simplifies processing pipelines in NMT
considerably. This approach, however, must consider relatively longer
sequences, rendering the training process prohibitively expensive. In this
paper, we discuss a novel, Transformer-based approach, that we compare, both in
speed and in quality to the Transformer at subword and character levels, as
well as previously developed character-level models. We evaluate our models on
4 language pairs from WMT'15: DE-EN, CS-EN, FI-EN and RU-EN. The proposed novel
architecture can be trained on a single GPU and is 34% percent faster than the
character-level Transformer; still, the obtained results are at least on par
with it. In addition, our proposed model outperforms the subword-level model in
FI-EN and shows close results in CS-EN. To stimulate further research in this
area and close the gap with subword-level NMT, we make all our code and models
publicly available
- …