1,766 research outputs found
A Stronger Baseline for Multilingual Word Embeddings
Levy, Søgaard and Goldberg’s (2017) S-ID (sentence ID) method applies word2vec on tuples containing a sentence ID and a word from the sentence. It has been shown to be a strong baseline for learning multilingual embeddings. Inspired by recent work on concept based embedding learning we propose SC-ID, an extension to S-ID: given a sentence aligned corpus, we use sampling to extract concepts that are then processed in the same manner as S-IDs. We perform experiments on the Parallel Bible Corpus across 1000+ languages and show that SC-ID yields up to 6% performance increase in a word translation task. In ad- dition, we provide evidence that SC-ID is easily and widely applicable by reporting competitive results across 8 tasks on a EuroParl based corpus
Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Adversarial training (AT) is a powerful regularization method for neural
networks, aiming to achieve robustness to input perturbations. Yet, the
specific effects of the robustness obtained from AT are still unclear in the
context of natural language processing. In this paper, we propose and analyze a
neural POS tagging model that exploits AT. In our experiments on the Penn
Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages),
we find that AT not only improves the overall tagging accuracy, but also 1)
prevents over-fitting well in low resource languages and 2) boosts tagging
accuracy for rare / unseen words. We also demonstrate that 3) the improved
tagging performance by AT contributes to the downstream task of dependency
parsing, and that 4) AT helps the model to learn cleaner word representations.
5) The proposed AT model is generally effective in different sequence labeling
tasks. These positive results motivate further use of AT for natural language
tasks.Comment: NAACL 201
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
When silver glitters more than gold: Bootstrapping an Italian part-of-speech tagger for Twitter
We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter
data, in the context of the Evalita 2016 PoSTWITA shared task. We show that
training the tagger on native Twitter data enriched with little amounts of
specifically selected gold data and additional silver-labelled data scraped
from Facebook, yields better results than using large amounts of manually
annotated data from a mix of genres.Comment: Proceedings of the 5th Evaluation Campaign of Natural Language
Processing and Speech Tools for Italian (EVALITA 2016
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