3,735 research outputs found
Constituent Parsing as Sequence Labeling
We introduce a method to reduce constituent parsing to sequence labeling. For
each word w_t, it generates a label that encodes: (1) the number of ancestors
in the tree that the words w_t and w_{t+1} have in common, and (2) the
nonterminal symbol at the lowest common ancestor. We first prove that the
proposed encoding function is injective for any tree without unary branches. In
practice, the approach is made extensible to all constituency trees by
collapsing unary branches. We then use the PTB and CTB treebanks as testbeds
and propose a set of fast baselines. We achieve 90.7% F-score on the PTB test
set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In
addition, sacrificing some accuracy, our approach achieves the fastest
constituent parsing speeds reported to date on PTB by a wide margin.Comment: EMNLP 2018 (Long Papers). Revised version with improved results after
fixing evaluation bu
Assessment of Pre-Trained Models Across Languages and Grammars
We present an approach for assessing how multilingual large language models
(LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to
recover constituent and dependency structures by casting parsing as sequence
labeling. To do so, we select a few LLMs and study them on 13 diverse UD
treebanks for dependency parsing and 10 treebanks for constituent parsing. Our
results show that: (i) the framework is consistent across encodings, (ii)
pre-trained word vectors do not favor constituency representations of syntax
over dependencies, (iii) sub-word tokenization is needed to represent syntax,
in contrast to character-based models, and (iv) occurrence of a language in the
pretraining data is more important than the amount of task data when recovering
syntax from the word vectors.Comment: Accepted at IJCNLP-AACL 202
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Sequence tagging models for constituent parsing are faster, but less accurate
than other types of parsers. In this work, we address the following weaknesses
of such constituent parsers: (a) high error rates around closing brackets of
long constituents, (b) large label sets, leading to sparsity, and (c) error
propagation arising from greedy decoding. To effectively close brackets, we
train a model that learns to switch between tagging schemes. To reduce
sparsity, we decompose the label set and use multi-task learning to jointly
learn to predict sublabels. Finally, we mitigate issues from greedy decoding
through auxiliary losses and sentence-level fine-tuning with policy gradient.
Combining these techniques, we clearly surpass the performance of sequence
tagging constituent parsers on the English and Chinese Penn Treebanks, and
reduce their parsing time even further. On the SPMRL datasets, we observe even
greater improvements across the board, including a new state of the art on
Basque, Hebrew, Polish and Swedish.Comment: NAACL 2019 (long papers). Contains corrigendu
Combination Strategies for Semantic Role Labeling
This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context. We evaluate the proposed
inference strategies in the framework of the CoNLL-2005 shared task using only
automatically-generated syntactic information. The extensive experimental
evaluation and analysis indicates that all the proposed inference strategies
are successful -they all outperform the current best results reported in the
CoNLL-2005 evaluation exercise- but each of the proposed approaches has its
advantages and disadvantages. Several important traits of a state-of-the-art
SRL combination strategy emerge from this analysis: (i) individual models
should be combined at the granularity of candidate arguments rather than at the
granularity of complete solutions; (ii) the best combination strategy uses an
inference model based in learning; and (iii) the learning-based inference
benefits from max-margin classifiers and global feedback
Viable Dependency Parsing as Sequence Labeling
We recast dependency parsing as a sequence labeling problem, exploring
several encodings of dependency trees as labels. While dependency parsing by
means of sequence labeling had been attempted in existing work, results
suggested that the technique was impractical. We show instead that with a
conventional BiLSTM-based model it is possible to obtain fast and accurate
parsers. These parsers are conceptually simple, not needing traditional parsing
algorithms or auxiliary structures. However, experiments on the PTB and a
sample of UD treebanks show that they provide a good speed-accuracy tradeoff,
with results competitive with more complex approaches.Comment: Camera-ready version to appear at NAACL 2019 (final peer-reviewed
manuscript). 8 pages (incl. appendix
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