1,359 research outputs found
Combining Residual Networks with LSTMs for Lipreading
We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size target-words consisting of 1.28sec video excerpts from BBC
TV broadcasts. The proposed network attains word accuracy equal to 83.0,
yielding 6.8 absolute improvement over the current state-of-the-art, without
using information about word boundaries during training or testing.Comment: Submitted to Interspeech 201
On Tree-Based Neural Sentence Modeling
Neural networks with tree-based sentence encoders have shown better results
on many downstream tasks. Most of existing tree-based encoders adopt syntactic
parsing trees as the explicit structure prior. To study the effectiveness of
different tree structures, we replace the parsing trees with trivial trees
(i.e., binary balanced tree, left-branching tree and right-branching tree) in
the encoders. Though trivial trees contain no syntactic information, those
encoders get competitive or even better results on all of the ten downstream
tasks we investigated. This surprising result indicates that explicit syntax
guidance may not be the main contributor to the superior performances of
tree-based neural sentence modeling. Further analysis show that tree modeling
gives better results when crucial words are closer to the final representation.
Additional experiments give more clues on how to design an effective tree-based
encoder. Our code is open-source and available at
https://github.com/ExplorerFreda/TreeEnc.Comment: To Appear at EMNLP 201
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Traditional text summarization approaches often require a
large amount of preprocessing costs and do not capture the important issue of
conversion rate in E-commerce. This paper proposes a novel multi-task learning
approach for improving product title compression with user search log data. In
particular, a pointer network-based sequence-to-sequence approach is utilized
for title compression with an attentive mechanism as an extractive method and
an attentive encoder-decoder approach is utilized for generating user search
queries. The encoding parameters (i.e., semantic embedding of original titles)
are shared among the two tasks and the attention distributions are jointly
optimized. An extensive set of experiments with both human annotated data and
online deployment demonstrate the advantage of the proposed research for both
compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201
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