64,155 research outputs found
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
An important goal of computer vision is to build systems that learn visual
representations over time that can be applied to many tasks. In this paper, we
investigate a vision-language embedding as a core representation and show that
it leads to better cross-task transfer than standard multi-task learning. In
particular, the task of visual recognition is aligned to the task of visual
question answering by forcing each to use the same word-region embeddings. We
show this leads to greater inductive transfer from recognition to VQA than
standard multitask learning. Visual recognition also improves, especially for
categories that have relatively few recognition training labels but appear
often in the VQA setting. Thus, our paper takes a small step towards creating
more general vision systems by showing the benefit of interpretable, flexible,
and trainable core representations.Comment: Accepted in ICCV 2017. The arxiv version has an extra analysis on
correlation with human attentio
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
Open-Retrieval Conversational Question Answering
Conversational search is one of the ultimate goals of information retrieval.
Recent research approaches conversational search by simplified settings of
response ranking and conversational question answering, where an answer is
either selected from a given candidate set or extracted from a given passage.
These simplifications neglect the fundamental role of retrieval in
conversational search. To address this limitation, we introduce an
open-retrieval conversational question answering (ORConvQA) setting, where we
learn to retrieve evidence from a large collection before extracting answers,
as a further step towards building functional conversational search systems. We
create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an
end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader
that are all based on Transformers. Our extensive experiments on OR-QuAC
demonstrate that a learnable retriever is crucial for ORConvQA. We further show
that our system can make a substantial improvement when we enable history
modeling in all system components. Moreover, we show that the reranker
component contributes to the model performance by providing a regularization
effect. Finally, further in-depth analyses are performed to provide new
insights into ORConvQA.Comment: Accepted to SIGIR'2
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