5,485 research outputs found
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks.Comment: Accepted for ACL Repl4NLP 201
A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.Comment: In Proceedings of the Semantic Parsing Workshop at ACL 2014
(forthcoming
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based question answering models by comparing them to a question
answering baseline that queries the web and extracts the answer only from web
snippets, without access to the target knowledge-base. We investigate this
approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
language, and find that our model obtains reasonable performance (35 F1
compared to 41 F1 of state-of-the-art). We find in our analysis that our model
performs well on complex questions involving conjunctions, but struggles on
questions that involve relation composition and superlatives.Comment: *sem 201
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