17,011 research outputs found
Supervised and Unsupervised Transfer Learning for Question Answering
Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target dataset using two QA models. The performance of both models on a
TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson
et al., 2013) is significantly improved via a simple transfer learning
technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models
achieves the state-of-the-art on all target datasets; for the TOEFL listening
comprehension test, it outperforms the previous best model by 7%. Finally, we
show that transfer learning is helpful even in unsupervised scenarios when
correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper
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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