9 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
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation
Recent cross-lingual cross-modal works attempt to extend Vision-Language
Pre-training (VLP) models to non-English inputs and achieve impressive
performance. However, these models focus only on understanding tasks utilizing
encoder-only architecture. In this paper, we propose ERNIE-UniX2, a unified
cross-lingual cross-modal pre-training framework for both generation and
understanding tasks. ERNIE-UniX2 integrates multiple pre-training paradigms
(e.g., contrastive learning and language modeling) based on encoder-decoder
architecture and attempts to learn a better joint representation across
languages and modalities. Furthermore, ERNIE-UniX2 can be seamlessly fine-tuned
for varieties of generation and understanding downstream tasks. Pre-trained on
both multilingual text-only and image-text datasets, ERNIE-UniX2 achieves SOTA
results on various cross-lingual cross-modal generation and understanding tasks
such as multimodal machine translation and multilingual visual question
answering.Comment: 13 pages, 2 figure