6,160 research outputs found
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
User interaction with voice-powered agents generates large amounts of
unlabeled utterances. In this paper, we explore techniques to efficiently
transfer the knowledge from these unlabeled utterances to improve model
performance on Spoken Language Understanding (SLU) tasks. We use Embeddings
from Language Model (ELMo) to take advantage of unlabeled data by learning
contextualized word representations. Additionally, we propose ELMo-Light
(ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our
findings suggest unsupervised pre-training on a large corpora of unlabeled
utterances leads to significantly better SLU performance compared to training
from scratch and it can even outperform conventional supervised transfer.
Additionally, we show that the gains from unsupervised transfer techniques can
be further improved by supervised transfer. The improvements are more
pronounced in low resource settings and when using only 1000 labeled in-domain
samples, our techniques match the performance of training from scratch on
10-15x more labeled in-domain data.Comment: To appear at AAAI 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
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