130,433 research outputs found
Visual Question Answering with Memory-Augmented Networks
In this paper, we exploit a memory-augmented neural network to predict
accurate answers to visual questions, even when those answers occur rarely in
the training set. The memory network incorporates both internal and external
memory blocks and selectively pays attention to each training exemplar. We show
that memory-augmented neural networks are able to maintain a relatively
long-term memory of scarce training exemplars, which is important for visual
question answering due to the heavy-tailed distribution of answers in a general
VQA setting. Experimental results on two large-scale benchmark datasets show
the favorable performance of the proposed algorithm with a comparison to state
of the art.Comment: CVPR 201
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve
equivalent questions that result in the same answer as the original question.
Such a system can be used to understand and answer rare and noisy
reformulations of common questions by mapping them to a set of canonical forms.
This has large-scale applications for community Question Answering (cQA) and
open-domain spoken language question answering systems. In this paper we
describe a new QPR system implemented as a Neural Information Retrieval (NIR)
system consisting of a neural network sentence encoder and an approximate
k-Nearest Neighbour index for efficient vector retrieval. We also describe our
mechanism to generate an annotated dataset for question paraphrase retrieval
experiments automatically from question-answer logs via distant supervision. We
show that the standard loss function in NIR, triplet loss, does not perform
well with noisy labels. We propose smoothed deep metric loss (SDML) and with
our experiments on two QPR datasets we show that it significantly outperforms
triplet loss in the noisy label setting
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.Comment: Appears in: Advances in Neural Information Processing Systems 28
(NIPS 2015). 14 pages, 13 figure
Crowdsourcing Multiple Choice Science Questions
We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-specific text and a small set of existing questions. It
produces model suggestions for document selection and answer distractor choice
which aid the human question generation process. With this method we have
assembled SciQ, a dataset of 13.7K multiple choice science exam questions
(Dataset available at http://allenai.org/data.html). We demonstrate that the
method produces in-domain questions by providing an analysis of this new
dataset and by showing that humans cannot distinguish the crowdsourced
questions from original questions. When using SciQ as additional training data
to existing questions, we observe accuracy improvements on real science exams.Comment: accepted for the Workshop on Noisy User-generated Text (W-NUT) 201
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