Papers by Isaiah Onando Mulang’
Fine-tuning BERT with Focus Words for Explanation Regeneration (2020.starsem-1)
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| Challenge: | Existing approaches to explain the correct answer in multiple-choice QA are low in F-scores and lack of performance. |
| Approach: | They introduce a lightweight focus feature in a transformer-based NLP model and examine performance improvements. |
| Outcome: | The proposed model achieves the highest scores, second only to a computationally intensive system. |
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D19-53)
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| Challenge: | TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration tackles explanation generation for elementary science questions. |
| Approach: | They propose a hybrid pipelined machine learning model and rule-based system to address MIER-19 . they use a featurerich learning-to-rank machine learning and a rule-driven system to rerank the LTR model predictions. |
| Outcome: | The proposed model was ranked fourth in the evaluation, close to the second and third ranked teams, achieving 39.4% MAP. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)
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Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, Jens Lehmann
| Challenge: | Existing approaches to target end-to-end entity linking over knowledge bases are not efficient. |
| Approach: | They propose a modular approach to target end-to-end entity linking over knowledge bases. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases. |