Papers by Umar Farooq Minhas
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |