586 research outputs found
Weighted DAG Automata for Semantic Graphs
Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that parallels the frameworks of finite automata for strings and finite tree automata for trees. A possible starting point for such a framework is the formalism of directed acyclic graph (DAG) automata, defined by Kamimura and Slutzki and extended by Quernheim and Knight. In this article, we study the latter in depth, demonstrating several new results, including a practical recognition algorithm that can be used for inference and learning with models defined on DAG automata. We also propose an extension to graphs with unbounded node degree and show that our results carry over to the extended formalism
The problem with probabilistic DAG automata for semantic graphs
Semantic representations in the form of directed acyclic graphs (DAGs) have
been introduced in recent years, and to model them, we need probabilistic
models of DAGs. One model that has attracted some attention is the DAG
automaton, but it has not been studied as a probabilistic model. We show that
some DAG automata cannot be made into useful probabilistic models by the nearly
universal strategy of assigning weights to transitions. The problem affects
single-rooted, multi-rooted, and unbounded-degree variants of DAG automata, and
appears to be pervasive. It does not affect planar variants, but these are
problematic for other reasons.Comment: To appear in NAACL-HLT 201
Probabilistic graph formalisms for meaning representations
In recent years, many datasets have become available that represent natural language
semantics as graphs. To use these datasets in natural language processing (NLP), we
require probabilistic models of graphs. Finite-state models have been very successful
for NLP tasks on strings and trees because they are probabilistic and composable. Are
there equivalent models for graphs? In this thesis, we survey several graph formalisms,
focusing on whether they are probabilistic and composable, and we contribute several
new results. In particular, we study the directed acyclic graph automata languages
(DAGAL), the monadic second-order graph languages (MSOGL), and the hyperedge
replacement languages (HRL). We prove that DAGAL cannot be made probabilistic,
we explain why MSOGL also most likely cannot be made probabilistic, and we review
the fact that HRL are not composable. We then review a subfamily of HRL and
MSOGL: the regular graph languages (RGL; Courcelle 1991), which have not been
widely studied, and particularly have not been studied in an NLP context. Although
Courcelle (1991) only sketches a proof, we present a full, more NLP-accessible proof
that RGL are a subfamily of MSOGL. We prove that RGL are probabilistic and composable,
and we provide a novel Earley-style parsing algorithm for them that runs in
time linear in the size of the input graph. We compare RGL to two other new formalisms:
the restricted DAG languages (RDL; Bj¨orklund et al. 2016) and the tree-like
languages (TLL; Matheja et al. 2015). We show that RGL and RDL are incomparable;
TLL and RDL are incomparable; and either RGL are incomparable to TLL, or RGL
are contained within TLL. This thesis provides a clearer picture of this field from an
NLP perspective, and suggests new theoretical and empirical research directions
Polyglot Semantic Parsing in APIs
Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particular, we focus on translating text to code signature
representations using the software component datasets of Richardson and Kuhn
(2017a,b). The advantage of such models is that they can be used for parsing a
wide variety of input natural languages and output programming languages, or
mixed input languages, using a single unified model. To facilitate modeling of
this type, we develop a novel graph-based decoding framework that achieves
state-of-the-art performance on the above datasets, and apply this method to
two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing
should be modeled as graph prediction. But predicting graphs presents difficult
technical challenges, so it is simpler and more common to predict the
linearized graphs found in semantic parsing datasets using well-understood
sequence models. The cost of this simplicity is that the predicted strings may
not be well-formed graphs. We present recurrent neural network DAG grammars, a
graph-aware sequence model that ensures only well-formed graphs while
sidestepping many difficulties in graph prediction. We test our model on the
Parallel Meaning Bank---a multilingual semantic graphbank. Our approach yields
competitive results in English and establishes the first results for German,
Italian and Dutch.Comment: 9 pages, to appear in EMNLP201
Survey on Instruction Selection: An Extensive and Modern Literature Review
Instruction selection is one of three optimisation problems involved in the
code generator backend of a compiler. The instruction selector is responsible
of transforming an input program from its target-independent representation
into a target-specific form by making best use of the available machine
instructions. Hence instruction selection is a crucial part of efficient code
generation.
Despite on-going research since the late 1960s, the last, comprehensive
survey on the field was written more than 30 years ago. As new approaches and
techniques have appeared since its publication, this brings forth a need for a
new, up-to-date review of the current body of literature. This report addresses
that need by performing an extensive review and categorisation of existing
research. The report therefore supersedes and extends the previous surveys, and
also attempts to identify where future research should be directed.Comment: Major changes: - Merged simulation chapter with macro expansion
chapter - Addressed misunderstandings of several approaches - Completely
rewrote many parts of the chapters; strengthened the discussion of many
approaches - Revised the drawing of all trees and graphs to put the root at
the top instead of at the bottom - Added appendix for listing the approaches
in a table See doc for more inf
A Trichotomy for Regular Simple Path Queries on Graphs
Regular path queries (RPQs) select nodes connected by some path in a graph.
The edge labels of such a path have to form a word that matches a given regular
expression. We investigate the evaluation of RPQs with an additional constraint
that prevents multiple traversals of the same nodes. Those regular simple path
queries (RSPQs) find several applications in practice, yet they quickly become
intractable, even for basic languages such as (aa)* or a*ba*.
In this paper, we establish a comprehensive classification of regular
languages with respect to the complexity of the corresponding regular simple
path query problem. More precisely, we identify the fragment that is maximal in
the following sense: regular simple path queries can be evaluated in polynomial
time for every regular language L that belongs to this fragment and evaluation
is NP-complete for languages outside this fragment. We thus fully characterize
the frontier between tractability and intractability for RSPQs, and we refine
our results to show the following trichotomy: Evaluations of RSPQs is either
AC0, NL-complete or NP-complete in data complexity, depending on the regular
language L. The fragment identified also admits a simple characterization in
terms of regular expressions.
Finally, we also discuss the complexity of the following decision problem:
decide, given a language L, whether finding a regular simple path for L is
tractable. We consider several alternative representations of L: DFAs, NFAs or
regular expressions, and prove that this problem is NL-complete for the first
representation and PSPACE-complete for the other two. As a conclusion we extend
our results from edge-labeled graphs to vertex-labeled graphs and vertex-edge
labeled graphs.Comment: 15 pages, conference submissio
- …