About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Ryokan Ri
- sl:arxiv_num : 2110.08151
- sl:arxiv_published : 2021-10-15T15:28:38Z
- sl:arxiv_summary : Recent studies have shown that multilingual pretrained language models can be
effectively improved with cross-lingual alignment information from Wikipedia
entities. However, existing methods only exploit entity information in
pretraining and do not explicitly use entities in downstream tasks. In this
study, we explore the effectiveness of leveraging entity representations for
downstream cross-lingual tasks. We train a multilingual language model with 24
languages with entity representations and show the model consistently
outperforms word-based pretrained models in various cross-lingual transfer
tasks. We also analyze the model and the key insight is that incorporating
entity representations into the input allows us to extract more
language-agnostic features. We also evaluate the model with a multilingual
cloze prompt task with the mLAMA dataset. We show that entity-based prompt
elicits correct factual knowledge more likely than using only word
representations. Our source code and pretrained models are available at
https://github.com/studio-ousia/luke.@en
- sl:arxiv_title : mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models@en
- sl:arxiv_updated : 2022-03-30T14:27:20Z
- sl:bookmarkOf : https://arxiv.org/abs/2110.08151
- sl:creationDate : 2022-04-17
- sl:creationTime : 2022-04-17T23:20:52Z
- sl:relatedDoc :
Documents with similar tags (experimental)