2,705 research outputs found
Baselines and test data for cross-lingual inference
The recent years have seen a revival of interest in textual entailment,
sparked by i) the emergence of powerful deep neural network learners for
natural language processing and ii) the timely development of large-scale
evaluation datasets such as SNLI. Recast as natural language inference, the
problem now amounts to detecting the relation between pairs of statements: they
either contradict or entail one another, or they are mutually neutral. Current
research in natural language inference is effectively exclusive to English. In
this paper, we propose to advance the research in SNLI-style natural language
inference toward multilingual evaluation. To that end, we provide test data for
four major languages: Arabic, French, Spanish, and Russian. We experiment with
a set of baselines. Our systems are based on cross-lingual word embeddings and
machine translation. While our best system scores an average accuracy of just
over 75%, we focus largely on enabling further research in multilingual
inference.Comment: To appear at LREC 201
Zero-Shot Cross-Lingual Transfer with Meta Learning
Learning what to share between tasks has been a topic of great importance
recently, as strategic sharing of knowledge has been shown to improve
downstream task performance. This is particularly important for multilingual
applications, as most languages in the world are under-resourced. Here, we
consider the setting of training models on multiple different languages at the
same time, when little or no data is available for languages other than
English. We show that this challenging setup can be approached using
meta-learning, where, in addition to training a source language model, another
model learns to select which training instances are the most beneficial to the
first. We experiment using standard supervised, zero-shot cross-lingual, as
well as few-shot cross-lingual settings for different natural language
understanding tasks (natural language inference, question answering). Our
extensive experimental setup demonstrates the consistent effectiveness of
meta-learning for a total of 15 languages. We improve upon the state-of-the-art
for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA
dataset). A comprehensive error analysis indicates that the correlation of
typological features between languages can partly explain when parameter
sharing learned via meta-learning is beneficial.Comment: Accepted as long paper in EMNLP2020 main conferenc
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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