988,907 research outputs found
Better Answers to Real Questions
We consider existential problems over the reals. Extended quantifier
elimination generalizes the concept of regular quantifier elimination by
providing in addition answers, which are descriptions of possible assignments
for the quantified variables. Implementations of extended quantifier
elimination via virtual substitution have been successfully applied to various
problems in science and engineering. So far, the answers produced by these
implementations included infinitesimal and infinite numbers, which are hard to
interpret in practice. We introduce here a post-processing procedure to
convert, for fixed parameters, all answers into standard real numbers. The
relevance of our procedure is demonstrated by application of our implementation
to various examples from the literature, where it significantly improves the
quality of the results
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for
evaluating the abilities and shortcomings of current systems for image
understanding. Many of the recently proposed VQA systems include attention or
memory mechanisms designed to support "reasoning". For multiple-choice VQA,
nearly all of these systems train a multi-class classifier on image and
question features to predict an answer. This paper questions the value of these
common practices and develops a simple alternative model based on binary
classification. Instead of treating answers as competing choices, our model
receives the answer as input and predicts whether or not an
image-question-answer triplet is correct. We evaluate our model on the Visual7W
Telling and the VQA Real Multiple Choice tasks, and find that even simple
versions of our model perform competitively. Our best model achieves
state-of-the-art performance on the Visual7W Telling task and compares
surprisingly well with the most complex systems proposed for the VQA Real
Multiple Choice task. We explore variants of the model and study its
transferability between both datasets. We also present an error analysis of our
model that suggests a key problem of current VQA systems lies in the lack of
visual grounding of concepts that occur in the questions and answers. Overall,
our results suggest that the performance of current VQA systems is not
significantly better than that of systems designed to exploit dataset biases.Comment: European Conference on Computer Visio
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
Interactive Recommendation Engine for Conversational Recommendations
The task of a traditional recommendation engine generally ends when it returns a list of possible answers to a user’s query, even if the user finds the answers unhelpful. Such a paradigm is incompatible with modern virtual assistants, where a user might request to filter, control, and modify results in an interactive conversational flow that is based on user context. This disclosure describes a conversational recommendation engine that provides an explanation of the recommendations provided to the user, proactively offers the user options to find better recommendations, asks personalized follow-up questions to ease the interaction, and enables the user to provide real-time feedback to update the recommendation
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
Generative question answering (QA) models generate answers to questions
either solely based on the parameters of the model (the closed-book setting) or
additionally retrieving relevant evidence (the open-book setting). Generative
QA models can answer some relatively complex questions, but the mechanism
through which they do so is still poorly understood. We perform several studies
aimed at better understanding the multi-hop reasoning capabilities of
generative QA models. First, we decompose multi-hop questions into multiple
corresponding single-hop questions, and find marked inconsistency in QA models'
answers on these pairs of ostensibly identical question chains. Second, we find
that models lack zero-shot multi-hop reasoning ability: when trained only on
single-hop questions, models generalize poorly to multi-hop questions. Finally,
we demonstrate that it is possible to improve models' zero-shot multi-hop
reasoning capacity through two methods that approximate real multi-hop natural
language (NL) questions by training on either concatenation of single-hop
questions or logical forms (SPARQL). In sum, these results demonstrate that
multi-hop reasoning does not emerge naturally in generative QA models, but can
be encouraged by advances in training or modeling techniques.Comment: COLING 202
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