Papers by Aditya Khan
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ (2026.eacl-srw)
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York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz, En-Shiun Annie Lee
| Challenge: | Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score. |
| Approach: | They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance. |
| Outcome: | The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks. |
Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation (2026.acl-srw)
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| Challenge: | Rather than relying on a single static ensemble or language-specific weighting, DMM adapts the metric combination based on properties of the source segment. |
| Approach: | They propose a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing metrics. |
| Outcome: | The proposed framework outperforms linear and Gaussian process-based ensembles across multiple language pairs and introducing soft conditioning yields gains over linear models. |