2,580 research outputs found
Improving a Strong Neural Parser with Conjunction-Specific Features
While dependency parsers reach very high overall accuracy, some dependency
relations are much harder than others. In particular, dependency parsers
perform poorly in coordination construction (i.e., correctly attaching the
"conj" relation). We extend a state-of-the-art dependency parser with
conjunction-specific features, focusing on the similarity between the conjuncts
head words. Training the extended parser yields an improvement in "conj"
attachment as well as in overall dependency parsing accuracy on the Stanford
dependency conversion of the Penn TreeBank
Cross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page
Movie Description
Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Pretrained contextual representation models (Peters et al., 2018; Devlin et
al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new
release of BERT (Devlin, 2018) includes a model simultaneously pretrained on
104 languages with impressive performance for zero-shot cross-lingual transfer
on a natural language inference task. This paper explores the broader
cross-lingual potential of mBERT (multilingual) as a zero shot language
transfer model on 5 NLP tasks covering a total of 39 languages from various
language families: NLI, document classification, NER, POS tagging, and
dependency parsing. We compare mBERT with the best-published methods for
zero-shot cross-lingual transfer and find mBERT competitive on each task.
Additionally, we investigate the most effective strategy for utilizing mBERT in
this manner, determine to what extent mBERT generalizes away from language
specific features, and measure factors that influence cross-lingual transfer.Comment: EMNLP 2019 Camera Read
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