7,552 research outputs found
Zero-Shot Relation Extraction via Reading Comprehension
We show that relation extraction can be reduced to answering simple reading
comprehension questions, by associating one or more natural-language questions
with each relation slot. This reduction has several advantages: we can (1)
learn relation-extraction models by extending recent neural
reading-comprehension techniques, (2) build very large training sets for those
models by combining relation-specific crowd-sourced questions with distant
supervision, and even (3) do zero-shot learning by extracting new relation
types that are only specified at test-time, for which we have no labeled
training examples. Experiments on a Wikipedia slot-filling task demonstrate
that the approach can generalize to new questions for known relation types with
high accuracy, and that zero-shot generalization to unseen relation types is
possible, at lower accuracy levels, setting the bar for future work on this
task.Comment: CoNLL 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
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