3,080 research outputs found
Structured Access in Sentence Comprehension
This thesis is concerned with the nature of memory access during the construction of long-distance dependencies in online sentence comprehension. In recent years, an intense focus on the computational challenges posed by long-distance dependencies has proven to be illuminating with respect to the characteristics of the architecture of the human sentence processor, suggesting a tight link between general memory access procedures and sentence processing routines (Lewis & Vasishth 2005; Lewis, Vasishth, & Van Dyke 2006; Wagers, Lau & Phillips 2009). The present thesis builds upon this line of research, and its primary aim is to motivate and defend the hypothesis that the parser accesses linguistic memory in an essentially structured fashion for certain long-distance dependencies. In order to make this case, I focus on the processing of reflexive and agreement dependencies, and ask whether or not non-structural information such as morphological features are used to gate memory access during syntactic comprehension. Evidence from eight experiments in a range of methodologies in English and Chinese is brought to bear on this question, providing arguments from interference effects and time-course effects that primarily syntactic information is used to access linguistic memory in the construction of certain long-distance dependencies. The experimental evidence for structured access is compatible
with a variety of architectural assumptions about the parser, and I present one implementation of this idea in a parser based on the ACT-R memory architecture. In the context of such a content-addressable model of memory, the claim of structured access is equivalent to the claim that only syntactic cues are used to query memory. I argue that structured access reflects an optimal parsing strategy in the context of a noisy, interference-prone cognitive architecture: abstract structural cues are favored over lexical feature cues for certain structural dependencies in order to minimize memory interference in online processing
Proceedings
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 268 pages.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
Designing Statistical Language Learners: Experiments on Noun Compounds
The goal of this thesis is to advance the exploration of the statistical
language learning design space. In pursuit of that goal, the thesis makes two
main theoretical contributions: (i) it identifies a new class of designs by
specifying an architecture for natural language analysis in which probabilities
are given to semantic forms rather than to more superficial linguistic
elements; and (ii) it explores the development of a mathematical theory to
predict the expected accuracy of statistical language learning systems in terms
of the volume of data used to train them.
The theoretical work is illustrated by applying statistical language learning
designs to the analysis of noun compounds. Both syntactic and semantic analysis
of noun compounds are attempted using the proposed architecture. Empirical
comparisons demonstrate that the proposed syntactic model is significantly
better than those previously suggested, approaching the performance of human
judges on the same task, and that the proposed semantic model, the first
statistical approach to this problem, exhibits significantly better accuracy
than the baseline strategy. These results suggest that the new class of designs
identified is a promising one. The experiments also serve to highlight the need
for a widely applicable theory of data requirements.Comment: PhD thesis (Macquarie University, Sydney; December 1995), LaTeX
source, xii+214 page
Multi-VALUE: A Framework for Cross-Dialectal English NLP
Dialect differences caused by regional, social, and economic factors cause
performance discrepancies for many groups of language technology users.
Inclusive and equitable language technology must critically be dialect
invariant, meaning that performance remains constant over dialectal shifts.
Current systems often fall short of this ideal since they are designed and
tested on a single dialect: Standard American English (SAE). We introduce a
suite of resources for evaluating and achieving English dialect invariance. The
resource is called Multi-VALUE, a controllable rule-based translation system
spanning 50 English dialects and 189 unique linguistic features. Multi-VALUE
maps SAE to synthetic forms of each dialect. First, we use this system to
stress tests question answering, machine translation, and semantic parsing.
Stress tests reveal significant performance disparities for leading models on
non-standard dialects. Second, we use this system as a data augmentation
technique to improve the dialect robustness of existing systems. Finally, we
partner with native speakers of Chicano and Indian English to release new
gold-standard variants of the popular CoQA task. To execute the transformation
code, run model checkpoints, and download both synthetic and gold-standard
dialectal benchmark datasets, see http://value-nlp.org.Comment: ACL 202
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