11,932 research outputs found
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
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