7 research outputs found
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Semantic parsing from denotations faces two key challenges in model training:
(1) given only the denotations (e.g., answers), search for good candidate
semantic parses, and (2) choose the best model update algorithm. We propose
effective and general solutions to each of them. Using policy shaping, we bias
the search procedure towards semantic parses that are more compatible to the
text, which provide better supervision signals for training. In addition, we
propose an update equation that generalizes three different families of
learning algorithms, which enables fast model exploration. When experimented on
a recently proposed sequential question answering dataset, our framework leads
to a new state-of-the-art model that outperforms previous work by 5.0% absolute
on exact match accuracy.Comment: Accepted at EMNLP 201
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet
its success relies on the availability of large amounts of supervised data. Our
goal is to learn a neural semantic parser when only prior knowledge about a
limited number of simple rules is available, without access to either annotated
programs or execution results. Our approach is initialized by rules, and
improved in a back-translation paradigm using generated question-program pairs
from the semantic parser and the question generator. A phrase table with
frequent mapping patterns is automatically derived, also updated as training
progresses, to measure the quality of generated instances. We train the model
with model-agnostic meta-learning to guarantee the accuracy and stability on
examples covered by rules, and meanwhile acquire the versatility to generalize
well on examples uncovered by rules. Results on three benchmark datasets with
different domains and programs show that our approach incrementally improves
the accuracy. On WikiSQL, our best model is comparable to the SOTA system
learned from denotations