Papers by Ayyoob ImaniGooghari
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
Graph Algorithms for Multiparallel Word Alignment (2021.emnlp-main)
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Ayyoob ImaniGooghari, Masoud Jalili Sabet, Lutfi Kerem Senel, Philipp Dufter, François Yvon, Hinrich Schütze
| Challenge: | Word alignments are useful for typological research and can be used in machine translation systems. |
| Approach: | They propose to exploit the multiparallelity of parallel corpora by representing bilingual alignments as a graph and then predicting additional edges. |
| Outcome: | The proposed algorithm improves the accuracy of bilingual alignments by 28% over baseline algorithms. |
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging (2022.emnlp-main)
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| Challenge: | Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled training data. |
| Approach: | They propose a method for transferring labels from high-resource sources to low-resourced target languages using a graph-based label propagation method. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised POS tagging of low-resource languages. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
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Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus (2021.acl-demo)
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| Challenge: | 7000 languages worldwide are spoken, but most research is focused on English . multilinguality is essential for multilingual research, and is a key component of the process. |
| Approach: | They propose a wordaligned parallel corpus that can be browsed using an online tool . they use the word alignment tools SimAlign and BabelNet to find the alignments . |
| Outcome: | The proposed tool can be set up for any parallel corpus and explores its quality and properties. |
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages (2022.lrec-1)
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| Challenge: | Existing methods to extract named entity datasets from parallel corpora require large monolingual corporata or word aligners that are unavailable or perform poorly for underresourced languages. |
| Approach: | They propose a method for creating a multilingual named entity resource from parallel corpora and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. |
| Outcome: | The proposed method outperforms existing methods in two tasks. |