819 research outputs found
Entity Recognition at First Sight: Improving NER with Eye Movement Information
Previous research shows that eye-tracking data contains information about the
lexical and syntactic properties of text, which can be used to improve natural
language processing models. In this work, we leverage eye movement features
from three corpora with recorded gaze information to augment a state-of-the-art
neural model for named entity recognition (NER) with gaze embeddings. These
corpora were manually annotated with named entity labels. Moreover, we show how
gaze features, generalized on word type level, eliminate the need for recorded
eye-tracking data at test time. The gaze-augmented models for NER using
token-level and type-level features outperform the baselines. We present the
benefits of eye-tracking features by evaluating the NER models on both
individual datasets as well as in cross-domain settings.Comment: Accepted at NAACL-HLT 201
What to do about non-standard (or non-canonical) language in NLP
Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.Comment: KONVENS 201
Predicting Native Language from Gaze
A fundamental question in language learning concerns the role of a speaker's
first language in second language acquisition. We present a novel methodology
for studying this question: analysis of eye-movement patterns in second
language reading of free-form text. Using this methodology, we demonstrate for
the first time that the native language of English learners can be predicted
from their gaze fixations when reading English. We provide analysis of
classifier uncertainty and learned features, which indicates that differences
in English reading are likely to be rooted in linguistic divergences across
native languages. The presented framework complements production studies and
offers new ground for advancing research on multilingualism.Comment: ACL 201
Sequence classification with human attention
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP. Specifically, we use estimated human attention derived from eye-tracking corpora to regularize attention functions in recurrent neural networks. We show substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language
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