76,986 research outputs found
Structural Embedding of Syntactic Trees for Machine Comprehension
Deep neural networks for machine comprehension typically utilizes only word
or character embeddings without explicitly taking advantage of structured
linguistic information such as constituency trees and dependency trees. In this
paper, we propose structural embedding of syntactic trees (SEST), an algorithm
framework to utilize structured information and encode them into vector
representations that can boost the performance of algorithms for the machine
comprehension. We evaluate our approach using a state-of-the-art neural
attention model on the SQuAD dataset. Experimental results demonstrate that our
model can accurately identify the syntactic boundaries of the sentences and
extract answers that are syntactically coherent over the baseline methods
Interpretation of Natural Language Rules in Conversational Machine Reading
Most work in machine reading focuses on question answering problems where the
answer is directly expressed in the text to read. However, many real-world
question answering problems require the reading of text not because it contains
the literal answer, but because it contains a recipe to derive an answer
together with the reader's background knowledge. One example is the task of
interpreting regulations to answer "Can I...?" or "Do I have to...?" questions
such as "I am working in Canada. Do I have to carry on paying UK National
Insurance?" after reading a UK government website about this topic. This task
requires both the interpretation of rules and the application of background
knowledge. It is further complicated due to the fact that, in practice, most
questions are underspecified, and a human assistant will regularly have to ask
clarification questions such as "How long have you been working abroad?" when
the answer cannot be directly derived from the question and text. In this
paper, we formalise this task and develop a crowd-sourcing strategy to collect
32k task instances based on real-world rules and crowd-generated questions and
scenarios. We analyse the challenges of this task and assess its difficulty by
evaluating the performance of rule-based and machine-learning baselines. We
observe promising results when no background knowledge is necessary, and
substantial room for improvement whenever background knowledge is needed.Comment: EMNLP 201
Supervised Transfer Learning for Product Information Question Answering
Popular e-commerce websites such as Amazon offer community question answering
systems for users to pose product related questions and experienced customers
may provide answers voluntarily. In this paper, we show that the large volume
of existing community question answering data can be beneficial when building a
system for answering questions related to product facts and specifications. Our
experimental results demonstrate that the performance of a model for answering
questions related to products listed in the Home Depot website can be improved
by a large margin via a simple transfer learning technique from an existing
large-scale Amazon community question answering dataset. Transfer learning can
result in an increase of about 10% in accuracy in the experimental setting
where we restrict the size of the data of the target task used for training. As
an application of this work, we integrate the best performing model trained in
this work into a mobile-based shopping assistant and show its usefulness.Comment: 2018 17th IEEE International Conference on Machine Learning and
Application
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