Papers by Hamza Khan
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)
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| Challenge: | Several video understanding applications require the ability of temporal reasoning. |
| Approach: | They propose a video large language model for temporal reasoning and fine-grained understanding in long videos. |
| Outcome: | The proposed model outperforms existing methods in time and motion studies and temporal action segmentation evaluations. |
Controlled Data Generation via Insertion Operations for NLU (2022.naacl-industry)
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| Challenge: | a new approach to annotate live traffic is emerging to be cost-effective and efficient . manual data annotation is expensive and not preferred for meeting customer privacy expectations . |
| Approach: | They propose a targeted synthetic data generation technique by inserting tokens into a given semantic signature. |
| Outcome: | The proposed approach achieves the same accuracy as training with all available data on a voice assistant dataset. |