85,481 research outputs found
Language to logical form with neural attention
Semantic parsing aims at mapping natural language to machine interpretable
meaning representations. Traditional approaches rely on high-quality lexicons,
manually-built templates, and linguistic features which are either domain- or
representation-specific. In this paper we present a general method based on an
attention-enhanced encoder-decoder model. We encode input utterances into
vector representations, and generate their logical forms by conditioning the
output sequences or trees on the encoding vectors. Experimental results on four
datasets show that our approach performs competitively without using
hand-engineered features and is easy to adapt across domains and meaning
representations.Comment: Accepted by ACL-1
Learning an Executable Neural Semantic Parser
This paper describes a neural semantic parser that maps natural language
utterances onto logical forms which can be executed against a task-specific
environment, such as a knowledge base or a database, to produce a response. The
parser generates tree-structured logical forms with a transition-based approach
which combines a generic tree-generation algorithm with domain-general
operations defined by the logical language. The generation process is modeled
by structured recurrent neural networks, which provide a rich encoding of the
sentential context and generation history for making predictions. To tackle
mismatches between natural language and logical form tokens, various attention
mechanisms are explored. Finally, we consider different training settings for
the neural semantic parser, including a fully supervised training where
annotated logical forms are given, weakly-supervised training where denotations
are provided, and distant supervision where only unlabeled sentences and a
knowledge base are available. Experiments across a wide range of datasets
demonstrate the effectiveness of our parser.Comment: In Journal of Computational Linguistic
Lifecycle of neural semantic parsing
Humans are born with the ability to learn to perceive, comprehend and communicate
with language. Computing machines, on the other hand, only understand programming
languages. To bridge the gap between humans and computers, deep semantic parsers
convert natural language utterances into machine-understandable logical forms. The
technique has a wide range of applications ranging from spoken dialogue systems and
natural language interfaces. This thesis focuses on neural network-based semantic
parsing.
Traditional semantic parsers function with a domain-specific grammar that pairs
utterances and logical forms, and parse with a CKY-like algorithm in polynomial
time. Recent advances in neural semantic parsing reformulate the task as a sequence-to-
sequence learning problem. Neural semantic parsers parse a sentence in linear
time, and reduce the need for domain-specific assumptions, grammar learning, and
extensive feature engineering. But this modeling flexibility comes at a cost since
it is no longer possible to interpret how meaning composition is performed, given
that logical forms are structured objects (trees or graphs). Such knowledge plays
a critical role in understanding modeling limitations so as to build better semantic
parsers. Moreover, the sequence-to-sequence learning problem is fairly unconstrained,
both in terms of the possible derivations to consider and in terms of the target logical
forms which can be ill-formed or unexecutable. The first contribution of this thesis is
an improved neural semantic parser, which produces syntactically valid logical forms
following a transition system and grammar constrains. The transition system integrates
the generation of domain-general (i.e., valid tree-structures and language-specific predicates)
and domain-specific aspects (i.e., domain-specific predicates and entities) in a unified
way. The model employs various neural attention mechanisms to handle mismatches
between natural language and formal language—a central challenge in semantic parsing.
Training data to semantic parsers typically consists of utterances paired with logical
forms. Another challenge of semantic parsing concerns the annotation of logical forms,
which is labor-intensive. To write down the correct logical form of an utterance, one
not only needs to have expertise in the semantic formalism, but also has to ensure the
logical form matches the utterance semantics. We tackle this challenge in two ways.
On the one hand, we extend the neural semantic parser to a weakly-supervised setting
within a parser-ranker framework. The weakly-supervised setup uses training data
of utterance-denotation (e.g., question-answer) pairs, which are much easier to obtain
and therefore allow to scale semantic parsers to complex domains. Our framework
combines the advantages of conventional weakly-supervised semantic parsers and neural
semantic parsing. Candidate logical forms are generated by a neural decoder and
subsequently scored by a ranking component. We present methods to efficiently search
for candidate logical forms which involve spurious ambiguity—some logical forms do
not match utterance semantics but coincidentally execute to the correct denotation.
They should be excluded from training.
On the other hand, we focus on how to quickly engineer a practical neural semantic
parser for closed domains, by directly reducing the annotation difficulty of utterance-logical
form pairs. We develop an interface for efficiently collecting compositional
utterance-logical form pairs and then leverage the data collection method to train neural
semantic parsers. Our method provides an end-to-end solution for closed-domain
semantic parsing given only an ontology. We also extend the end-to-end solution to
handle sequential utterances simulating a non-interactive user session. Specifically,
the data collection interface is modified to collect utterance sequences which exhibit
various co-reference patterns. Then the neural semantic parser is extended to parse
context-dependent utterances.
In summary, this thesis covers the lifecycle of designing a neural semantic parser:
from model design (i.e., how to model a neural semantic parser with an appropriate
inductive bias), training (i.e., how to perform fully supervised and weakly supervised
training for a neural semantic parser) to engineering (i.e., how to build a neural semantic
parser from a domain ontology)
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