Papers by Badr M. Abdullah
Attention on Multiword Expressions: A Multilingual Study of BERT-based Models with Regard to Idiomaticity and Microsyntax (2025.findings-naacl)
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| Challenge: | Specifically, models fine-tuned on semantic tasks tend to distribute attention to idiomatic expressions more evenly across layers. |
| Approach: | They analyze attention patterns of encoder-only models towards two distinct types of Multiword Expressions (MWEs) idioms present challenges in semantic non-compositionality, while MSUs demonstrate unconventional syntactic behavior that does not conform to standard grammatical categorizations. |
| Outcome: | The proposed models show that fine-tuned models allocate attention to idiomatic expressions more evenly across layers. |
It’s Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems (2025.acl-long)
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| Challenge: | idioms are defined as words with a figurative meaning not deducible from their individual components. |
| Approach: | They compare idiom translation as compared to conventional news translation in two languages . they compare MT and SLT systems with MT, Large Language Models and cascaded alternatives . |
| Outcome: | The proposed systems show better handling of idioms than standard news translation systems. |
Familiar words but strange voices: Modelling the influence of speech variability on word recognition (2021.eacl-srw)
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| Challenge: | Despite the lack of acoustic-phonetic invariance in speech, listeners can reliably recognize spoken words despite the lack aural-phonemic invariancy. |
| Approach: | They propose a deep neural model which is trained to retrieve the meaning of a word given its spoken form, a task which resembles that faced by a human listener. |
| Outcome: | The proposed model is more sensitive to dialectical variation than gender variation and more related to related languages. |
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English (2020.coling-main)
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Marius Mosbach, Stefania Degaetano-Ortlieb, Marie-Pauline Krielke, Badr M. Abdullah, Dietrich Klakow
| Challenge: | Despite the high performance of transformer-based language models, we still lack understanding of the kind of linguistic knowledge they learn and rely on. |
| Approach: | They evaluate three transformer-based language models and test their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. |
| Outcome: | The models capture grammatical and semantic knowledge, but they lack model-specific weaknesses especially on semantic knowledge. |
Do we read what we hear? Modeling orthographic influences on spoken word recognition (2021.eacl-srw)
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| Challenge: | Existing theories and models of spoken word recognition focus on accessing lexical knowledge given an acoustic realization of a word form. |
| Approach: | They propose two models that instantiate hypotheses regarding the influence of orthography on spoken word recognition. |
| Outcome: | The proposed models reproduce human-like behavior in different ways and provide testable hypotheses for future research on the source of orthographic effects in spoken word recognition. |