145,986 research outputs found
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach employs features derived from networks trained for
specialized tasks of scene classification, person activity prediction, and
person and object attribute prediction. We also present a method for selecting
sub-regions of an image that are relevant for evaluating the appropriateness of
a putative answer. Visual features are computed both from the whole image and
from local regions, while sentences are mapped to a common space using a simple
normalized canonical correlation analysis (CCA) model. Our results show a
significant improvement over the previous state of the art, and indicate that
answering different question types benefits from examining a variety of image
cues and carefully choosing informative image sub-regions
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long
Question Type Guided Attention in Visual Question Answering
Visual Question Answering (VQA) requires integration of feature maps with
drastically different structures and focus of the correct regions. Image
descriptors have structures at multiple spatial scales, while lexical inputs
inherently follow a temporal sequence and naturally cluster into semantically
different question types. A lot of previous works use complex models to extract
feature representations but neglect to use high-level information summary such
as question types in learning. In this work, we propose Question Type-guided
Attention (QTA). It utilizes the information of question type to dynamically
balance between bottom-up and top-down visual features, respectively extracted
from ResNet and Faster R-CNN networks. We experiment with multiple VQA
architectures with extensive input ablation studies over the TDIUC dataset and
show that QTA systematically improves the performance by more than 5% across
multiple question type categories such as "Activity Recognition", "Utility" and
"Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we
achieve 3% improvement for overall accuracy. Finally, we propose a multi-task
extension to predict question types which generalizes QTA to applications that
lack of question type, with minimal performance loss
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
We describe a new deep learning architecture for learning to rank question
answer pairs. Our approach extends the long short-term memory (LSTM) network
with holographic composition to model the relationship between question and
answer representations. As opposed to the neural tensor layer that has been
adopted recently, the holographic composition provides the benefits of scalable
and rich representational learning approach without incurring huge parameter
costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified
architecture for both deep sentence modeling and semantic matching.
Essentially, our model is trained end-to-end whereby the parameters of the LSTM
are optimized in a way that best explains the correlation between question and
answer representations. In addition, our proposed deep learning architecture
requires no extensive feature engineering. Via extensive experiments, we show
that HD-LSTM outperforms many other neural architectures on two popular
benchmark QA datasets. Empirical studies confirm the effectiveness of
holographic composition over the neural tensor layer.Comment: SIGIR 2017 Full Pape
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