9,030 research outputs found
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
Can Subcategorisation Probabilities Help a Statistical Parser?
Research into the automatic acquisition of lexical information from corpora
is starting to produce large-scale computational lexicons containing data on
the relative frequencies of subcategorisation alternatives for individual
verbal predicates. However, the empirical question of whether this type of
frequency information can in practice improve the accuracy of a statistical
parser has not yet been answered. In this paper we describe an experiment with
a wide-coverage statistical grammar and parser for English and
subcategorisation frequencies acquired from ten million words of text which
shows that this information can significantly improve parse accuracy.Comment: 9 pages, uses colacl.st
Evaluation of LTAG parsing with supertag compaction
One of the biggest concerns that has been raised over the feasibility of using large-scale LTAGs in NLP is the amount of redundancy within a grammar¿s elementary tree set. This has led to various proposals on how best to represent grammars in a way that makes them compact and easily maintained (Vijay-Shanker and Schabes, 1992; Becker, 1993; Becker, 1994; Evans, Gazdar and Weir, 1995; Candito, 1996). Unfortunately, while this work can help to make the storage of grammars more efficient, it does nothing to prevent the problem reappearing when the grammar is processed by a parser and the complete set of trees is reproduced. In this paper we are concerned with an approach that addresses this problem of computational redundancy in the trees, and evaluate its effectiveness
Learning Language Representations for Typology Prediction
One central mystery of neural NLP is what neural models "know" about their
subject matter. When a neural machine translation system learns to translate
from one language to another, does it learn the syntax or semantics of the
languages? Can this knowledge be extracted from the system to fill holes in
human scientific knowledge? Existing typological databases contain relatively
full feature specifications for only a few hundred languages. Exploiting the
existence of parallel texts in more than a thousand languages, we build a
massive many-to-one neural machine translation (NMT) system from 1017 languages
into English, and use this to predict information missing from typological
databases. Experiments show that the proposed method is able to infer not only
syntactic, but also phonological and phonetic inventory features, and improves
over a baseline that has access to information about the languages' geographic
and phylogenetic neighbors.Comment: EMNLP 201
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