7,362 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
Findings of the 2019 Conference on Machine Translation (WMT19)
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
We describe PARANMT-50M, a dataset of more than 50 million English-English
sentential paraphrase pairs. We generated the pairs automatically by using
neural machine translation to translate the non-English side of a large
parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M
can be a valuable resource for paraphrase generation and can provide a rich
source of semantic knowledge to improve downstream natural language
understanding tasks. To show its utility, we use ParaNMT-50M to train
paraphrastic sentence embeddings that outperform all supervised systems on
every SemEval semantic textual similarity competition, in addition to showing
how it can be used for paraphrase generation
Capturing lexical variation in MT evaluation using automatically built sense-cluster inventories
The strict character of most of the existing Machine Translation (MT) evaluation metrics does not permit them to capture lexical variation in translation. However, a central
issue in MT evaluation is the high correlation that the metrics should have with human judgments of translation quality. In order to achieve a higher correlation, the identification of sense correspondences between the compared translations becomes really important. Given
that most metrics are looking for exact correspondences, the evaluation results are often misleading concerning translation quality. Apart from that, existing metrics do not permit one to make a conclusive estimation of the impact of Word Sense Disambiguation techniques into
MT systems. In this paper, we show how information acquired by an unsupervised semantic analysis method can be used to render MT evaluation more sensitive to lexical semantics. The sense inventories built by this data-driven method are incorporated into METEOR: they replace WordNet for evaluation in English and render METEOR’s synonymy module operable in French. The evaluation results demonstrate that the use of these inventories gives rise to an increase in the number of matches and the correlation with human judgments of translation quality, compared to precision-based metrics
Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
Text alignment and text quality are critical to the accuracy of Machine
Translation (MT) systems, some NLP tools, and any other text processing tasks
requiring bilingual data. This research proposes a language independent
bi-sentence filtering approach based on Polish (not a position-sensitive
language) to English experiments. This cleaning approach was developed on the
TED Talks corpus and also initially tested on the Wikipedia comparable corpus,
but it can be used for any text domain or language pair. The proposed approach
implements various heuristics for sentence comparison. Some of them leverage
synonyms and semantic and structural analysis of text as additional
information. Minimization of data loss was ensured. An improvement in MT system
score with text processed using the tool is discussed.Comment: arXiv admin note: text overlap with arXiv:1509.09093,
arXiv:1509.0888
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