8,264 research outputs found
Connectionist Inference Models
The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
Question Generation (QG) is fundamentally a simple syntactic transformation;
however, many aspects of semantics influence what questions are good to form.
We implement this observation by developing Syn-QG, a set of transparent
syntactic rules leveraging universal dependencies, shallow semantic parsing,
lexical resources, and custom rules which transform declarative sentences into
question-answer pairs. We utilize PropBank argument descriptions and VerbNet
state predicates to incorporate shallow semantic content, which helps generate
questions of a descriptive nature and produce inferential and semantically
richer questions than existing systems. In order to improve syntactic fluency
and eliminate grammatically incorrect questions, we employ back-translation
over the output of these syntactic rules. A set of crowd-sourced evaluations
shows that our system can generate a larger number of highly grammatical and
relevant questions than previous QG systems and that back-translation
drastically improves grammaticality at a slight cost of generating irrelevant
questions.Comment: Some of the results in the paper were incorrec
Scene Graph Generation by Iterative Message Passing
Understanding a visual scene goes beyond recognizing individual objects in
isolation. Relationships between objects also constitute rich semantic
information about the scene. In this work, we explicitly model the objects and
their relationships using scene graphs, a visually-grounded graphical structure
of an image. We propose a novel end-to-end model that generates such structured
scene representation from an input image. The model solves the scene graph
inference problem using standard RNNs and learns to iteratively improves its
predictions via message passing. Our joint inference model can take advantage
of contextual cues to make better predictions on objects and their
relationships. The experiments show that our model significantly outperforms
previous methods for generating scene graphs using Visual Genome dataset and
inferring support relations with NYU Depth v2 dataset.Comment: CVPR 201
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