2,947 research outputs found
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
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
QuesNet: A Unified Representation for Heterogeneous Test Questions
Understanding learning materials (e.g. test questions) is a crucial issue in
online learning systems, which can promote many applications in education
domain. Unfortunately, many supervised approaches suffer from the problem of
scarce human labeled data, whereas abundant unlabeled resources are highly
underutilized. To alleviate this problem, an effective solution is to use
pre-trained representations for question understanding. However, existing
pre-training methods in NLP area are infeasible to learn test question
representations due to several domain-specific characteristics in education.
First, questions usually comprise of heterogeneous data including content text,
images and side information. Second, there exists both basic linguistic
information as well as domain logic and knowledge. To this end, in this paper,
we propose a novel pre-training method, namely QuesNet, for comprehensively
learning question representations. Specifically, we first design a unified
framework to aggregate question information with its heterogeneous inputs into
a comprehensive vector. Then we propose a two-level hierarchical pre-training
algorithm to learn better understanding of test questions in an unsupervised
way. Here, a novel holed language model objective is developed to extract
low-level linguistic features, and a domain-oriented objective is proposed to
learn high-level logic and knowledge. Moreover, we show that QuesNet has good
capability of being fine-tuned in many question-based tasks. We conduct
extensive experiments on large-scale real-world question data, where the
experimental results clearly demonstrate the effectiveness of QuesNet for
question understanding as well as its superior applicability
- …