Abstract
Seq2seq models based on Recurrent Neural Networks
(RNNs) have recently received a lot of attention
in the domain of Semantic Parsing. While
in principle they can be trained directly on pairs
(natural language utterances, logical forms), their
performance is limited by the amount of available
data. To alleviate this problem, we propose to
exploit various sources of prior knowledge: the
well-formedness of the logical forms is modeled
by a weighted context-free grammar; the likelihood
that certain entities present in the input utterance
are also present in the logical form is modeled by
weighted finite-state automata. The grammar and
automata are combined together through an efficient
intersection algorithm to form a soft guide
(“background”) to the RNN.We test our method on
an extension of the Overnight dataset and show that
it not only strongly improves over an RNN baseline,
but also outperforms non-RNN models based
on rich sets of hand-crafted features.
Keywords
- Natural Language Processing: Question Answering
- Machine Learning: Deep Learning
- Machine Learning: Knowledge-based Learning
