2,626 research outputs found
Reading Wikipedia to Answer Open-Domain Questions
This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine comprehension of text (identifying the answer spans from those
articles). Our approach combines a search component based on bigram hashing and
TF-IDF matching with a multi-layer recurrent neural network model trained to
detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
This study considers the task of machine reading at scale (MRS) wherein,
given a question, a system first performs the information retrieval (IR) task
of finding relevant passages in a knowledge source and then carries out the
reading comprehension (RC) task of extracting an answer span from the passages.
Previous MRS studies, in which the IR component was trained without considering
answer spans, struggled to accurately find a small number of relevant passages
from a large set of passages. In this paper, we propose a simple and effective
approach that incorporates the IR and RC tasks by using supervised multi-task
learning in order that the IR component can be trained by considering answer
spans. Experimental results on the standard benchmark, answering SQuAD
questions using the full Wikipedia as the knowledge source, showed that our
model achieved state-of-the-art performance. Moreover, we thoroughly evaluated
the individual contributions of our model components with our new Japanese
dataset and SQuAD. The results showed significant improvements in the IR task
and provided a new perspective on IR for RC: it is effective to teach which
part of the passage answers the question rather than to give only a relevance
score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201
Question Dependent Recurrent Entity Network for Question Answering
Question Answering is a task which requires building models capable of
providing answers to questions expressed in human language. Full question
answering involves some form of reasoning ability. We introduce a neural
network architecture for this task, which is a form of , that
recognizes entities and their relations to answers through a focus attention
mechanism. Our model is named
and extends by exploiting aspects of the question
during the memorization process. We validate the model on both synthetic and
real datasets: the question answering dataset and the $CNN\ \&\ Daily\
Newsreading\ comprehension$ dataset. In our experiments, the models achieved
a State-of-The-Art in the former and competitive results in the latter.Comment: 14 page
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