6 research outputs found
Multilingual Language Processing From Bytes
We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads
text as bytes and outputs span annotations of the form [start, length, label]
where start positions, lengths, and labels are separate entries in our
vocabulary. Because we operate directly on unicode bytes rather than
language-specific words or characters, we can analyze text in many languages
with a single model. Due to the small vocabulary size, these multilingual
models are very compact, but produce results similar to or better than the
state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that
use only the provided training datasets (no external data sources). Our models
are learning "from scratch" in that they do not rely on any elements of the
standard pipeline in Natural Language Processing (including tokenization), and
thus can run in standalone fashion on raw text
COMIC: Towards A Compact Image Captioning Model with Attention
Recent works in image captioning have shown very promising raw performance.
However, we realize that most of these encoder-decoder style networks with
attention do not scale naturally to large vocabulary size, making them
difficult to be deployed on embedded system with limited hardware resources.
This is because the size of word and output embedding matrices grow
proportionally with the size of vocabulary, adversely affecting the compactness
of these networks. To address this limitation, this paper introduces a brand
new idea in the domain of image captioning. That is, we tackle the problem of
compactness of image captioning models which is hitherto unexplored. We showed
that, our proposed model, named COMIC for COMpact Image Captioning, achieves
comparable results in five common evaluation metrics with state-of-the-art
approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an
embedding vocabulary size that is 39x - 99x smaller. The source code and models
are available at:
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table