47,311 research outputs found
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
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
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Rich and dense human labeled datasets are among the main enabling factors for
the recent advance on vision-language understanding. Many seemingly distant
annotations (e.g., semantic segmentation and visual question answering (VQA))
are inherently connected in that they reveal different levels and perspectives
of human understandings about the same visual scenes --- and even the same set
of images (e.g., of COCO). The popularity of COCO correlates those annotations
and tasks. Explicitly linking them up may significantly benefit both individual
tasks and the unified vision and language modeling. We present the preliminary
work of linking the instance segmentations provided by COCO to the questions
and answers (QAs) in the VQA dataset, and name the collected links visual
questions and segmentation answers (VQS). They transfer human supervision
between the previously separate tasks, offer more effective leverage to
existing problems, and also open the door for new research problems and models.
We study two applications of the VQS data in this paper: supervised attention
for VQA and a novel question-focused semantic segmentation task. For the
former, we obtain state-of-the-art results on the VQA real multiple-choice task
by simply augmenting the multilayer perceptrons with some attention features
that are learned using the segmentation-QA links as explicit supervision. To
put the latter in perspective, we study two plausible methods and compare them
to an oracle method assuming that the instance segmentations are given at the
test stage.Comment: To appear on ICCV 201
Adversarial Domain Adaptation for Duplicate Question Detection
We address the problem of detecting duplicate questions in forums, which is
an important step towards automating the process of answering new questions. As
finding and annotating such potential duplicates manually is very tedious and
costly, automatic methods based on machine learning are a viable alternative.
However, many forums do not have annotated data, i.e., questions labeled by
experts as duplicates, and thus a promising solution is to use domain
adaptation from another forum that has such annotations. Here we focus on
adversarial domain adaptation, deriving important findings about when it
performs well and what properties of the domains are important in this regard.
Our experiments with StackExchange data show an average improvement of 5.6%
over the best baseline across multiple pairs of domains.Comment: EMNLP 2018 short paper - camera ready. 8 page
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
We propose a weakly-supervised approach that takes image-sentence pairs as
input and learns to visually ground (i.e., localize) arbitrary linguistic
phrases, in the form of spatial attention masks. Specifically, the model is
trained with images and their associated image-level captions, without any
explicit region-to-phrase correspondence annotations. To this end, we introduce
an end-to-end model which learns visual groundings of phrases with two types of
carefully designed loss functions. In addition to the standard discriminative
loss, which enforces that attended image regions and phrases are consistently
encoded, we propose a novel structural loss which makes use of the parse tree
structures induced by the sentences. In particular, we ensure complementarity
among the attention masks that correspond to sibling noun phrases, and
compositionality of attention masks among the children and parent phrases, as
defined by the sentence parse tree. We validate the effectiveness of our
approach on the Microsoft COCO and Visual Genome datasets.Comment: CVPR 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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