Papers by Masoud Jalili Sabet
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. |
CaMEL: Case Marker Extraction without Labels (2022.acl-long)
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| Challenge: | Existing models for morphological case marking and semantic content are not isomorphic. |
| Approach: | They propose a model that extracts case markers from a multilingual corpus using a noun phrase chunker and an alignment system. |
| Outcome: | The proposed model can extract case markers in 83 languages and visualise similarities and differences between case systems and annotate fine-grained deep cases in languages where they are not overtly marked. |
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
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| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Graph Neural Networks for Multiparallel Word Alignment (2022.findings-acl)
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| Challenge: | Generally, word alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. |
| Approach: | They propose a multiparallel word alignment graph and graph neural networks to exploit it . they add and remove edges from the initial alignments and generalize the model . |
| Outcome: | The proposed method outperforms previous work on three word alignment datasets and on a downstream task. |