Papers by Ibrahim Sharaf
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP (2021.eacl-demos)
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| Challenge: | Multi-task learning (MTL) has become a standard repertoire in natural language processing (NLP) it enables neural networks to learn tasks in parallel while leveraging the benefits of sharing parameters. |
| Approach: | They propose a toolkit for fine-tuning contextualized embeddings in multi-task settings. |
| Outcome: | The proposed toolkit supports a variety of natural language processing tasks . it enables neural networks to learn tasks in parallel while leveraging the benefits of sharing parameters. |
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding (2021.naacl-main)
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Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović, Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, Barbara Plank
| Challenge: | Lack of publicly available evaluation data for low-resource languages limits progress in SLU . despite advances in neural modeling for slot and intent detection, datasets for SLU remain limited. |
| Approach: | They propose a joint learning approach with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
| Outcome: | The proposed model can learn English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |