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Ningyu Xu

dblp:336/7014 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
0009-0002-3496-0272ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 44% Information extraction and text analysis · 44% Transfer learning and domain adaptation · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › multilingual NLP
cross-lingual syntactic transfer		 0.612022
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? · EMNLP 2022
Natural language and speech › Language models and text generation
multilingual language models		 0.612022
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? · EMNLP 2022
Machine learning › Transfer learning and domain adaptation › cross-lingual transfer
zero-shot cross-lingual transfer		 0.212022
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? · EMNLP 2022

Methods — techniques the papers use, named apart from their topics

self-supervised learning · 0.6
YearPublicationVenuePosition
2022 Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?
abstract
Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge.The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages.In this work, we investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages.We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.Such difference learnt via self-supervision plays a crucial role in the zero-shot transfer performance and can be predicted by variation in morphosyntactic properties between languages.These results suggest that mBERT properly encodes languages in a way consistent with linguistic diversity and provide insights into the mechanism of crosslingual transfer.
Ningyu Xu, Tao Gui, Ruotian Ma, Qi Zhang 0001, Jingting Ye, Menghan Zhang, Xuanjing Huang 0001
 EMNLP1