81 research outputs found
The Importance of Category Labels in Grammar Induction with Child-directed Utterances
Recent progress in grammar induction has shown that grammar induction is
possible without explicit assumptions of language-specific knowledge. However,
evaluation of induced grammars usually has ignored phrasal labels, an essential
part of a grammar. Experiments in this work using a labeled evaluation metric,
RH, show that linguistically motivated predictions about grammar sparsity and
use of categories can only be revealed through labeled evaluation. Furthermore,
depth-bounding as an implementation of human memory constraints in grammar
inducers is still effective with labeled evaluation on multilingual transcribed
child-directed utterances.Comment: The 16th International Conference on Parsing Technologies (IWPT 2020
Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree Averaging
We address unsupervised discontinuous constituency parsing, where we observe
a high variance in the performance of the only previous model. We propose to
build an ensemble of different runs of the existing discontinuous parser by
averaging the predicted trees, to stabilize and boost performance. To begin
with, we provide comprehensive computational complexity analysis (in terms of P
and NP-complete) for tree averaging under different setups of binarity and
continuity. We then develop an efficient exact algorithm to tackle the task,
which runs in a reasonable time for all samples in our experiments. Results on
three datasets show our method outperforms all baselines in all metrics; we
also provide in-depth analyses of our approach
Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song
A pervasive belief with regard to the differences between human language and
animal vocal sequences (song) is that they belong to different classes of
computational complexity, with animal song belonging to regular languages,
whereas human language is superregular. This argument, however, lacks empirical
evidence since superregular analyses of animal song are understudied. The goal
of this paper is to perform a superregular analysis of animal song, using data
from gibbons as a case study, and demonstrate that a superregular analysis can
be effectively used with non-human data. A key finding is that a superregular
analysis does not increase explanatory power but rather provides for compact
analysis: Fewer grammatical rules are necessary once superregularity is
allowed. This pattern is analogous to a previous computational analysis of
human language, and accordingly, the null hypothesis, that human language and
animal song are governed by the same type of grammatical systems, cannot be
rejected.Comment: Accepted for publication by Royal Society Open Scienc
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