139,735 research outputs found
Deep Joint Entity Disambiguation with Local Neural Attention
We propose a novel deep learning model for joint document-level entity
disambiguation, which leverages learned neural representations. Key components
are entity embeddings, a neural attention mechanism over local context windows,
and a differentiable joint inference stage for disambiguation. Our approach
thereby combines benefits of deep learning with more traditional approaches
such as graphical models and probabilistic mention-entity maps. Extensive
experiments show that we are able to obtain competitive or state-of-the-art
accuracy at moderate computational costs.Comment: Conference on Empirical Methods in Natural Language Processing
(EMNLP) 2017 long pape
Improving Retrieval-Based Question Answering with Deep Inference Models
Question answering is one of the most important and difficult applications at
the border of information retrieval and natural language processing, especially
when we talk about complex science questions which require some form of
inference to determine the correct answer. In this paper, we present a two-step
method that combines information retrieval techniques optimized for question
answering with deep learning models for natural language inference in order to
tackle the multi-choice question answering in the science domain. For each
question-answer pair, we use standard retrieval-based models to find relevant
candidate contexts and decompose the main problem into two different
sub-problems. First, assign correctness scores for each candidate answer based
on the context using retrieval models from Lucene. Second, we use deep learning
architectures to compute if a candidate answer can be inferred from some
well-chosen context consisting of sentences retrieved from the knowledge base.
In the end, all these solvers are combined using a simple neural network to
predict the correct answer. This proposed two-step model outperforms the best
retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Deep learning models have achieved remarkable success in natural language
inference (NLI) tasks. While these models are widely explored, they are hard to
interpret and it is often unclear how and why they actually work. In this
paper, we take a step toward explaining such deep learning based models through
a case study on a popular neural model for NLI. In particular, we propose to
interpret the intermediate layers of NLI models by visualizing the saliency of
attention and LSTM gating signals. We present several examples for which our
methods are able to reveal interesting insights and identify the critical
information contributing to the model decisions.Comment: 11 pages, 11 figures, accepted as a short paper at EMNLP 201
Depth estimation from monocular images
This work will focus on studying different deep learning architectures for obtaining depth information from monocular RGB images.During this project, state-of-the-art deep learning models have been used to estimate depth
maps from a monocular RGB image applying a teacher-student learning approach.
This paradigm has been used in order to distillate the knowledge of high capacity deep neural
networks into shallower ones to make inference faster for real-time applications.
Some successful applications of this technique can be found both at natural language and
computer vision applications
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