Miryam de Lhoneux
Biographic Data
| ID | 6536272 |
|---|---|
| NAME | Miryam de Lhoneux |
| GIVEN NAMES | Miryam |
| FAMILY NAME | de Lhoneux |
| SIGNATURE | DE LHONEUX M |
| AFFILIATIONS | University of Copenhagen |
| ORCID | 0000-0001-8844-2126 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Principled Framework for Evaluating on Typologically Diverse Languages
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, evaluating these systems on all the world’s languages is practically infeasible. To attain generalizability, representative language sampling is essential. Previous work argues that generalizable multilingual evaluation sets should contain languages with diverse typological…
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions
There is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural parser adding insights to this question. We look at transitivity and agreement information of auxiliary verb constructions (AVCs) in comparison to finite main verbs (FMVs). This comparison is motivated…
No prominent works on this page.
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions
There is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural parser adding insights to this question. We look at transitivity and agreement information of auxiliary verb constructions (AVCs) in comparison to finite main verbs (FMVs). This comparison is motivated…
A Principled Framework for Evaluating on Typologically Diverse Languages
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, evaluating these systems on all the world’s languages is practically infeasible. To attain generalizability, representative language sampling is essential. Previous work argues that generalizable multilingual evaluation sets should contain languages with diverse typological…
Natural Language Processing Techniques (2 works) · Topic Modeling (2 works) · Artificial Intelligence (1 works) · Computational and Text Analysis Methods (1 works) · Computational linguistics (1 works) · Computer Science (1 works) · Dependency (UML (1 works) · Dependency grammar (1 works) · Generalizability theory (1 works) · Grammar (1 works)