22 research outputs found
Universal Semantic Parsing
Universal Dependencies (UD) offer a uniform cross-lingual syntactic
representation, with the aim of advancing multilingual applications. Recent
work shows that semantic parsing can be accomplished by transforming syntactic
dependencies to logical forms. However, this work is limited to English, and
cannot process dependency graphs, which allow handling complex phenomena such
as control. In this work, we introduce UDepLambda, a semantic interface for UD,
which maps natural language to logical forms in an almost language-independent
fashion and can process dependency graphs. We perform experiments on question
answering against Freebase and provide German and Spanish translations of the
WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation.
Results show that UDepLambda outperforms strong baselines across languages and
datasets. For English, it achieves a 4.9 F1 point improvement over the
state-of-the-art on GraphQuestions. Our code and data can be downloaded at
https://github.com/sivareddyg/udeplambda.Comment: EMNLP 201
Towards Universal Semantic Tagging
The paper proposes the task of universal semantic tagging---tagging word
tokens with language-neutral, semantically informative tags. We argue that the
task, with its independent nature, contributes to better semantic analysis for
wide-coverage multilingual text. We present the initial version of the semantic
tagset and show that (a) the tags provide semantically fine-grained
information, and (b) they are suitable for cross-lingual semantic parsing. An
application of the semantic tagging in the Parallel Meaning Bank supports both
of these points as the tags contribute to formal lexical semantics and their
cross-lingual projection. As a part of the application, we annotate a small
corpus with the semantic tags and present new baseline result for universal
semantic tagging.Comment: 9 pages, International Conference on Computational Semantics (IWCS
Syntax-mediated semantic parsing
Querying a database to retrieve an answer, telling a robot to perform an action, or
teaching a computer to play a game are tasks requiring communication with machines
in a language interpretable by them. Semantic parsing is the task of converting human
language to a machine interpretable language. While human languages are sequential in
nature with latent structures, machine interpretable languages are formal with explicit
structures. The computational linguistics community have created several treebanks to
understand the formal syntactic structures of human languages. In this thesis, we use
these to obtain formal meaning representations of languages, and learn computational
models to convert these meaning representations to the target machine representation.
Our goal is to evaluate if existing treebank syntactic representations are useful for
semantic parsing.
Existing semantic parsing methods mainly learn domain-specific grammars which
can parse human languages to machine representation directly. We deviate from this
trend and make use of general-purpose syntactic grammar to help in semantic parsing.
We use two syntactic representations: Combinatory Categorial Grammar (CCG) and
dependency syntax. CCG has a well established theory on deriving meaning representations
from its syntactic derivations. But there are no CCG treebanks for many languages
since these are difficult to annotate. In contrast, dependencies are easy to annotate and
have many treebanks. However, dependencies do not have a well established theory for
deriving meaning representations. In this thesis, we propose novel theories for deriving
meaning representations from dependencies.
Our evaluation task is question answering on a knowledge base. Given a question,
our goal is to answer it on the knowledge base by converting the question to an executable
query. We use Freebase, the knowledge source behind Google’s search engine,
as our knowledge base. Freebase contains millions of real world facts represented in a
graphical format. Inspired from the Freebase structure, we formulate semantic parsing
as a graph matching problem, i.e., given a natural language sentence, we convert it into
a graph structure from the meaning representation obtained from syntax, and find the
subgraph of Freebase that best matches the natural language graph.
Our experiments on Free917, WebQuestions and GraphQuestions semantic parsing
datasets conclude that general-purpose syntax is more useful for semantic parsing than
induced task-specific syntax and syntax-agnostic representations