22 research outputs found
Neural Semantic Parsing over Multiple Knowledge-bases
A fundamental challenge in developing semantic parsers is the paucity of
strong supervision in the form of language utterances annotated with logical
form. In this paper, we propose to exploit structural regularities in language
in different domains, and train semantic parsers over multiple knowledge-bases
(KBs), while sharing information across datasets. We find that we can
substantially improve parsing accuracy by training a single
sequence-to-sequence model over multiple KBs, when providing an encoding of the
domain at decoding time. Our model achieves state-of-the-art performance on the
Overnight dataset (containing eight domains), improves performance over a
single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
number of model parameters.Comment: Accepted to ACL 201
Polyglot Semantic Parsing in APIs
Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particular, we focus on translating text to code signature
representations using the software component datasets of Richardson and Kuhn
(2017a,b). The advantage of such models is that they can be used for parsing a
wide variety of input natural languages and output programming languages, or
mixed input languages, using a single unified model. To facilitate modeling of
this type, we develop a novel graph-based decoding framework that achieves
state-of-the-art performance on the above datasets, and apply this method to
two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version
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