4 research outputs found
Event Identification as a Decision Process with Non-linear Representation of Text
We propose scale-free Identifier Network(sfIN), a novel model for event
identification in documents. In general, sfIN first encodes a document into
multi-scale memory stacks, then extracts special events via conducting
multi-scale actions, which can be considered as a special type of sequence
labelling. The design of large scale actions makes it more efficient processing
a long document. The whole model is trained with both supervised learning and
reinforcement learning.Comment: 8 pages, 8 figure
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201