Papers by Oleg Vasilyev
Linear Cross-Lingual Mapping of Sentence Embeddings (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that a sentence has less ambiguity than a single word . if the word semantics is changed in translation, then a better translation is possible. |
| Approach: | They propose a linear cross-lingual mapping to improve multilingual embeddings . they also consider deviation from orthogonality conditions as a measure of deficiency . |
| Outcome: | The proposed method improves the multilingual embeddings by allowing for a linear cross-lingual mapping. |
Is Human Scoring the Best Criteria for Summary Evaluation? (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on summary quality measure have shown that it correlates well with quality scores produced by human annotators. |
| Approach: | They propose to use a criterion that does not rely on human scores to judge summary quality . they propose to develop a method that can be used to determine the best measure from a family of measures . |
| Outcome: | The proposed measure could be used to determine the best summary quality measure from a family of measures. |
Does Summary Evaluation Survive Translation to Other Languages? (2022.naacl-main)
Copied to clipboard
| Challenge: | a quality summarization dataset requires the production and evaluation of summaries by trained humans and machines. |
| Approach: | They translate a summarization dataset in English and compare its performance to seven languages . they explore equivalence testing as an appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries . |
| Outcome: | The proposed method could be used in seven languages and compares performance across measures. |
Preserving Multilingual Quality While Tuning Query Encoder on English Only (2025.naacl-short)
Copied to clipboard
| Challenge: | Xiong et al., 2020, 2021b) and Dong eet., 2022) . Switching from one query encoder to another is easily feasible . |
| Approach: | They propose a general tuning technique that can be used to modify query representations for specific types of queries or domains while keeping precomputed and stored documents intact. |
| Outcome: | The proposed model preserves multilingual qualities and improves embedding qualities on different datasets. |