6,097 research outputs found
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Kuhlmann et al. (2011) to produce the first implementation of
worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition
systems. With our minimal features, we also present O(n^3) global training
methods. Finally, using ensembles including our new parsers, we achieve the
best unlabeled attachment score reported (to our knowledge) on the Chinese
Treebank and the "second-best-in-class" result on the English Penn Treebank.Comment: Proceedings of EMNLP, 2017. 12 page
Pika parsing: reformulating packrat parsing as a dynamic programming algorithm solves the left recursion and error recovery problems
A recursive descent parser is built from a set of mutually-recursive
functions, where each function directly implements one of the nonterminals of a
grammar. A packrat parser uses memoization to reduce the time complexity for
recursive descent parsing from exponential to linear in the length of the
input. Recursive descent parsers are extremely simple to write, but suffer from
two significant problems: (i) left-recursive grammars cause the parser to get
stuck in infinite recursion, and (ii) it can be difficult or impossible to
optimally recover the parse state and continue parsing after a syntax error.
Both problems are solved by the pika parser, a novel reformulation of packrat
parsing as a dynamic programming algorithm, which requires parsing the input in
reverse: bottom-up and right to left, rather than top-down and left to right.
This reversed parsing order enables pika parsers to handle grammars that use
either direct or indirect left recursion to achieve left associativity,
simplifying grammar writing, and also enables optimal recovery from syntax
errors, which is a crucial property for IDEs and compilers. Pika parsing
maintains the linear-time performance characteristics of packrat parsing as a
function of input length. The pika parser was benchmarked against the
widely-used Parboiled2 and ANTLR4 parsing libraries. The pika parser performed
significantly better than the other parsers for an expression grammar, although
for a complex grammar implementing the Java language specification, a large
constant performance impact was incurred per input character. Therefore, if
performance is important, pika parsing is best applied to simple to
moderate-sized grammars, or to very large inputs, if other parsing alternatives
do not scale linearly in the length of the input. Several new insights into
precedence, associativity, and left recursion are presented.Comment: Submitted to AC
Learning to Search for Dependencies
We demonstrate that a dependency parser can be built using a credit
assignment compiler which removes the burden of worrying about low-level
machine learning details from the parser implementation. The result is a simple
parser which robustly applies to many languages that provides similar
statistical and computational performance with best-to-date transition-based
parsing approaches, while avoiding various downsides including randomization,
extra feature requirements, and custom learning algorithms
A Credit Assignment Compiler for Joint Prediction
Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, implementing them has been burdensomely awkward. In this paper, we
show the search space can be defined by an arbitrary imperative program,
turning learning to search into a credit assignment compiler. Altogether with
the algorithmic improvements for the compiler, we radically reduce the
complexity of programming and the running time. We demonstrate the feasibility
of our approach on multiple joint prediction tasks. In all cases, we obtain
accuracies as high as alternative approaches, at drastically reduced execution
and programming time
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
We present a sequence-to-action parsing approach for the natural language to
SQL task that incrementally fills the slots of a SQL query with feasible
actions from a pre-defined inventory. To account for the fact that typically
there are multiple correct SQL queries with the same or very similar semantics,
we draw inspiration from syntactic parsing techniques and propose to train our
sequence-to-action models with non-deterministic oracles. We evaluate our
models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the
test set, a 2.1% absolute improvement over the models trained with traditional
static oracles assuming a single correct target SQL query. When further
combined with the execution-guided decoding strategy, our model sets a new
state-of-the-art performance at an execution accuracy of 87.1%
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
We present a constituency parsing algorithm that, like a supertagger, works
by assigning labels to each word in a sentence. In order to maximally leverage
current neural architectures, the model scores each word's tags in parallel,
with minimal task-specific structure. After scoring, a left-to-right
reconciliation phase extracts a tree in (empirically) linear time. Our parser
achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups
compared to current state-of-the-art parsers with comparable accuracies.Comment: ACL 202
Compiling Language Definitions: The ASF+SDF Compiler
The ASF+SDF Meta-Environment is an interactive language development
environment whose main application areas are definition of domain-specific
languages, generation of program analysis and transformation tools, production
of software renovation tools, and general specification and prototyping. It
uses conditional rewrite rules to define the dynamic semantics and other
tool-oriented aspects of languages, so the effectiveness of the generated tools
is critically dependent on the quality of the rewrite rule implementation.
The ASF+SDF rewrite rule compiler generates C code, thus taking advantage of
C's portability and the sophisticated optimization capabilities of current C
compilers as well as avoiding potential abstract machine interface bottlenecks.
It can handle large (10 000+ rule) language definitions and uses an efficient
run-time storage scheme capable of handling large (1 000 000+ node) terms. Term
storage uses maximal subterm sharing (hash-consing), which turns out to be more
effective in the case of ASF+SDF than in Lisp or SML. Extensive benchmarking
has shown the time and space performance of the generated code to be as good as
or better than that of the best current rewrite rule and functional language
compilers.Comment: 36 pages, 5 figure
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers
We generalize Cohen, G\'omez-Rodr\'iguez, and Satta's (2011) parser to a
family of non-projective transition-based dependency parsers allowing
polynomial-time exact inference. This includes novel parsers with better
coverage than Cohen et al. (2011), and even a variant that reduces time
complexity to , improving over the known bounds in exact inference for
non-projective transition-based parsing. We hope that this piece of theoretical
work inspires design of novel transition systems with better coverage and
better run-time guarantees.
Code available at https://github.com/tzshi/nonproj-dp-variants-naacl2018Comment: Proceedings of NAACL-HLT 2018. 6 pages. This version fixes display
issue in an author nam
Two Local Models for Neural Constituent Parsing
Non-local features have been exploited by syntactic parsers for capturing
dependencies between sub output structures. Such features have been a key to
the success of state-of-the-art statistical parsers. With the rise of deep
learning, however, it has been shown that local output decisions can give
highly competitive accuracies, thanks to the power of dense neural input
representations that embody global syntactic information. We investigate two
conceptually simple local neural models for constituent parsing, which make
local decisions to constituent spans and CFG rules, respectively. Consistent
with previous findings along the line, our best model gives highly competitive
results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3%
on CTB 5.1.Comment: COLING 201
Online Object Tracking, Learning and Parsing with And-Or Graphs
This paper presents a method, called AOGTracker, for simultaneously tracking,
learning and parsing (TLP) of unknown objects in video sequences with a
hierarchical and compositional And-Or graph (AOG) representation. %The AOG
captures both structural and appearance variations of a target object in a
principled way. The TLP method is formulated in the Bayesian framework with a
spatial and a temporal dynamic programming (DP) algorithms inferring object
bounding boxes on-the-fly. During online learning, the AOG is discriminatively
learned using latent SVM to account for appearance (e.g., lighting and partial
occlusion) and structural (e.g., different poses and viewpoints) variations of
a tracked object, as well as distractors (e.g., similar objects) in background.
Three key issues in online inference and learning are addressed: (i)
maintaining purity of positive and negative examples collected online, (ii)
controling model complexity in latent structure learning, and (iii) identifying
critical moments to re-learn the structure of AOG based on its intrackability.
The intrackability measures uncertainty of an AOG based on its score maps in a
frame. In experiments, our AOGTracker is tested on two popular tracking
benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks,
and the VOT benchmarks --- VOT 2013, 2014, 2015 and TIR2015 (thermal imagery
tracking). In the former, our AOGTracker outperforms state-of-the-art tracking
algorithms including two trackers based on deep convolutional network. In the
latter, our AOGTracker outperforms all other trackers in VOT2013 and is
comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.Comment: 17 pages, Reproducibility: The source code is released with this
paper for reproducing all results, which is available at
https://github.com/tfwu/RGM-AOGTracke
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