Papers by Simon Dobnik
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer (2022.findings-acl)
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
| Approach: | They propose a multi-modal transformer that learns syntactic and semantic representations about entities and relations grounded in objects at the level of masked self-attention and cross-modal attention. |
| Outcome: | The proposed model learns syntactic and semantic representations about objects and relations cross-modally and unimodally. |
Shami: A Corpus of Levantine Arabic Dialects (L18-1)
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| Challenge: | Modern Standard Arabic is the official written language used in education and media . however, the spoken language varies widely across the Arab world . |
| Approach: | They construct a levantine dialect corpus covering data from four dialects spoken in four countries . they describe rules for pre-processing without affecting the meaning so that it is processable by NLP tools. |
| Outcome: | The proposed corpus is larger than existing corpora in terms of size, words and vocabularies. |
Pseudonymization Categories across Domain Boundaries (2024.lrec-main)
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Maria Irena Szawerna, Simon Dobnik, Therese Lindström Tiedemann, Ricardo Muñoz Sánchez, Xuan-Son Vu, Elena Volodina
| Challenge: | Linguistic data can contain personal information, which is limited in accessibility . a universal system of tags for categorizing PIIs could be developed to replace them . |
| Approach: | They analyze tagsets used for anonymization and pseudonymization to find out what kinds of PII appear in different domains. |
| Outcome: | The proposed system would allow for dynamic pseudonymization while keeping the data readable and useful for future research. |
Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data (D19-55)
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| Challenge: | a new corpus of unstructured data from social media is presenting challenges to NLP research . standardisation is neither natural nor universal, it is rather a human invention. |
| Approach: | They compile a parallel corpus of Arabic textual data matched with human annotations . they use a deep neural model designed to deal with context-dependent spelling correction . |
| Outcome: | The proposed model performs best with two CNN sub-network encoders and an LSTM decoder . pre-processing data token-by-token with edit-distance aligner significantly improves performance . |