2,744 research outputs found
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition 2016.
Fixed titl
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks,
such as question answering and ad-hoc document ranking. However, their
computational expenses deem them cost-prohibitive in practice. Our proposed
approach, called PreTTR (Precomputing Transformer Term Representations),
considerably reduces the query-time latency of deep transformer networks (up to
a 42x speedup on web document ranking) making these networks more practical to
use in a real-time ranking scenario. Specifically, we precompute part of the
document term representations at indexing time (without a query), and merge
them with the query representation at query time to compute the final ranking
score. Due to the large size of the token representations, we also propose an
effective approach to reduce the storage requirement by training a compression
layer to match attention scores. Our compression technique reduces the storage
required up to 95% and it can be applied without a substantial degradation in
ranking performance.Comment: Accepted at SIGIR 2020 (long
Split and Rephrase
We propose a new sentence simplification task (Split-and-Rephrase) where the
aim is to split a complex sentence into a meaning preserving sequence of
shorter sentences. Like sentence simplification, splitting-and-rephrasing has
the potential of benefiting both natural language processing and societal
applications. Because shorter sentences are generally better processed by NLP
systems, it could be used as a preprocessing step which facilitates and
improves the performance of parsers, semantic role labellers and machine
translation systems. It should also be of use for people with reading
disabilities because it allows the conversion of longer sentences into shorter
ones. This paper makes two contributions towards this new task. First, we
create and make available a benchmark consisting of 1,066,115 tuples mapping a
single complex sentence to a sequence of sentences expressing the same meaning.
Second, we propose five models (vanilla sequence-to-sequence to
semantically-motivated models) to understand the difficulty of the proposed
task.Comment: 11 pages, EMNLP 201
- …