Papers by Bamdev Mishra
Geometry-aware domain adaptation for unsupervised alignment of word embeddings (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for learning bilingual word embeddings have been used in natural language processing. |
| Approach: | They propose a manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and target languages. |
| Outcome: | The proposed approach outperforms state-of-the-art optimal transport based approach on bilingual lexicon induction task across several language pairs. |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
| Outcome: | The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks. |
SAJA: A Simple Approach to Judge Alignment for LLM-as-a-Judge (2026.acl-industry)
Copied to clipboard
| Challenge: | Current approaches to evaluate text at scale require multiple calls and per-dataset prompt tuning. |
| Approach: | They propose a model-agnostic approach to evaluate judge alignment that uses a lightweight calibration head. |
| Outcome: | a new model with SAJA matches more complex systems across four evaluation paradigms . it outperforms uncalibrated models on MT-Bench pairwise preference and competitive performance on five classification benchmarks compared to uncalibred models . |