Papers by Kee Kiat Koo
Deep Metric Learning to Hierarchically Rank - An Application in Product Retrieval (2023.emnlp-industry)
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Kee Kiat Koo, Ashutosh Joshi, Nishaanth Reddy, Karim Bouyarmane, Ismail Tutar, Vaclav Petricek, Changhe Yuan
| Challenge: | e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. |
| Approach: | They propose a method to identify duplicate and near-duplicate products across stores . they use Hierarchical Ranked Multi Similarity Loss to learn hierarchical metric space . |
| Outcome: | The proposed model outperforms baselines in terms of catalog coverage and precision of the mappings. |
Structured Object Language Modeling (SO-LM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising (2024.emnlp-industry)
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| Challenge: | Structured objects generation is a challenging problem for existing Large Language Models. |
| Approach: | They propose a self-supervised method to train an LLM to perform the task natively without prompt-engineering. |
| Outcome: | The proposed method matches or outperforms prompt-engineered state-of-the-art models while being more cost-efficient. |