117 research outputs found
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is
crucial for natural language understanding. Supervised approaches have achieved
impressing performances when large-scale corpora are available for
resource-rich languages such as English. While for the low-resource languages
with no annotated SRL dataset, it is still challenging to obtain competitive
performances. Cross-lingual SRL is one promising way to address the problem,
which has achieved great advances with the help of model transferring and
annotation projection. In this paper, we propose a novel alternative based on
corpus translation, constructing high-quality training datasets for the target
languages from the source gold-standard SRL annotations. Experimental results
on Universal Proposition Bank show that the translation-based method is highly
effective, and the automatic pseudo datasets can improve the target-language
SRL performances significantly.Comment: Accepted at ACL 202
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Word ordering is a constrained language generation task taking unordered
words as input. Existing work uses linear models and neural networks for the
task, yet pre-trained language models have not been studied in word ordering,
let alone why they help. We use BART as an instance and show its effectiveness
in the task. To explain why BART helps word ordering, we extend analysis with
probing and empirically identify that syntactic dependency knowledge in BART is
a reliable explanation. We also report performance gains with BART in the
related partial tree linearization task, which readily extends our analysis.Comment: COLING 202
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