8,894 research outputs found
Multilingual Unsupervised Sentence Simplification
Progress in Sentence Simplification has been hindered by the lack of
supervised data, particularly in languages other than English. Previous work
has aligned sentences from original and simplified corpora such as English
Wikipedia and Simple English Wikipedia, but this limits corpus size, domain,
and language. In this work, we propose using unsupervised mining techniques to
automatically create training corpora for simplification in multiple languages
from raw Common Crawl web data. When coupled with a controllable generation
mechanism that can flexibly adjust attributes such as length and lexical
complexity, these mined paraphrase corpora can be used to train simplification
systems in any language. We further incorporate multilingual unsupervised
pretraining methods to create even stronger models and show that by training on
mined data rather than supervised corpora, we outperform the previous best
results. We evaluate our approach on English, French, and Spanish
simplification benchmarks and reach state-of-the-art performance with a totally
unsupervised approach. We will release our models and code to mine the data in
any language included in Common Crawl
Automatic Identification of AltLexes using Monolingual Parallel Corpora
The automatic identification of discourse relations is still a challenging
task in natural language processing. Discourse connectives, such as "since" or
"but", are the most informative cues to identify explicit relations; however
discourse parsers typically use a closed inventory of such connectives. As a
result, discourse relations signaled by markers outside these inventories (i.e.
AltLexes) are not detected as effectively. In this paper, we propose a novel
method to leverage parallel corpora in text simplification and lexical
resources to automatically identify alternative lexicalizations that signal
discourse relation. When applied to the Simple Wikipedia and Newsela corpora
along with WordNet and the PPDB, the method allowed the automatic discovery of
91 AltLexes.Comment: 6 pages, Proceedings of Recent Advances in Natural Language
Processing (RANLP 2017
Adapting a general parser to a sublanguage
In this paper, we propose a method to adapt a general parser (Link Parser) to
sublanguages, focusing on the parsing of texts in biology. Our main proposal is
the use of terminology (identication and analysis of terms) in order to reduce
the complexity of the text to be parsed. Several other strategies are explored
and finally combined among which text normalization, lexicon and
morpho-guessing module extensions and grammar rules adaptation. We compare the
parsing results before and after these adaptations
Automated text simplification as a preprocessing step for machine translation into an under-resourced language
In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language. We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT). We explore three different scenarios for using the ATS in MT: (1) using the raw output of the ATS; (2) automatically filtering out the sentences with low grammaticality and meaning preservation scores; and (3) performing a minimal manual correction of the ATS output. Our results show improvement in fluency of the translation regardless of the chosen scenario, and difference in success of the three scenarios depending on the MT approach used (PBMT or NMT) with regards to improving translation fluency and post-editing effort
Optimality Theory as a Framework for Lexical Acquisition
This paper re-investigates a lexical acquisition system initially developed
for French.We show that, interestingly, the architecture of the system
reproduces and implements the main components of Optimality Theory. However, we
formulate the hypothesis that some of its limitations are mainly due to a poor
representation of the constraints used. Finally, we show how a better
representation of the constraints used would yield better results
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