Papers by Md Mahfuz Ibn Alam
LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages (2023.emnlp-main)
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| Challenge: | Currently, existing systems cannot accurately identify most of the world's 7000 languages due to lack of data and computational challenges. |
| Approach: | They propose a misprediction-resolution hierarchical model, LIMIT, that reduces error by 55% on a children's stories dataset and by 40% on 'fLORES-200' benchmark. |
| Outcome: | The proposed model reduces error by 55% on the MCS-350 and 40% on the FLORES-200 benchmarks. |
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
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| Challenge: | Bemba is the most populous language of Zambia but lacks resources for research . despite its significance, Bemba remains under-resourced and lacking in high-quality data and resources for NLP experiments and language technologies. |
| Approach: | They propose a large multimodal dataset for Bemba that includes images, transcriptions and translations. |
| Outcome: | The proposed dataset is based on images, transcriptions and translations of Bemba speakers . it provides baselines on speech recognition, machine translation and speech translation tasks . |
Language and Speech Technology for Central Kurdish Varieties (2024.lrec-main)
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| Challenge: | a recent study focused on the Kurdish language, a less-resourced Indo-European language spoken by over 30 million speakers. |
| Approach: | They propose to develop resources for language and speech technology for Kurdish . they report the performance of machine translation, automatic speech recognition and language identification . |
| Outcome: | The proposed model is based on transcribing movies and TV series as an alternative to fieldwork. |
SD-QA: Spoken Dialectal Question Answering for the Real World (2021.findings-emnlp)
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| Challenge: | Existing QA benchmarks do not account for errors that speech recognition models might introduce . evaluating production-ready QA systems on data that is not representative of real-world inputs is problematic . |
| Approach: | They construct a multi-dialect, spoken QA benchmark on five languages with 68k audio prompts in 24 dialects from 255 speakers. |
| Outcome: | The proposed model is based on 68k audio prompts in 24 dialects from 255 speakers. |
CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation (2024.findings-eacl)
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| Challenge: | Neural machine translation systems exhibit limited robustness in handling source-side linguistic variations. |
| Approach: | They propose a dialectal benchmark to quantify the robustness of MT systems to handle source-side linguistic variations. |
| Outcome: | The proposed benchmark demonstrates that large MT models face challenges translating dialectal variants. |