Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computation and Language

arXiv:2109.07348 (cs)
[Submitted on 15 Sep 2021 (v1), last revised 19 May 2022 (this version, v2)]

Title:Cross-lingual Transfer of Monolingual Models

Authors:Evangelia Gogoulou, Ariel Ekgren, Tim Isbister, Magnus Sahlgren
View PDF
Abstract:Recent studies in zero-shot cross-lingual learning using multilingual models have falsified the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. Inspired by this advancement, we introduce a cross-lingual transfer method for monolingual models based on domain adaptation. We study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform the native English model independently of the source language. After probing the English linguistic knowledge encoded in the representations before and after transfer, we find that semantic information is retained from the source language, while syntactic information is learned during transfer. Additionally, the results of evaluating the transferred models in source language tasks reveal that their performance in the source domain deteriorates after transfer.
Comments: Accepted to LREC 2022
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2109.07348 [cs.CL]
  (or arXiv:2109.07348v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2109.07348

Submission history

From: Evangelia Gogoulou [view email]
[v1] Wed, 15 Sep 2021 15:00:53 UTC (580 KB)
[v2] Thu, 19 May 2022 15:02:20 UTC (879 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2021-09
Change to browse by:
cs
cs.LG

References & Citations

DBLP - CS Bibliography

listing | bibtex
Ariel Ekgren
Tim Isbister
Magnus Sahlgren
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)