MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910680542609408 |
|---|---|
| author | Anugraha, David Kuwanto, Garry Susanto, Lucky Wijaya, Derry Tanti Winata, Genta Indra |
| author_facet | Anugraha, David Kuwanto, Garry Susanto, Lucky Wijaya, Derry Tanti Winata, Genta Indra |
| contents | We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Gaussian Processes. MetaMetrics-MT enhances existing MT metrics by optimizing their correlation with human judgments. Our experiments on the WMT24 metric shared task dataset demonstrate that MetaMetrics-MT outperforms all existing baselines, setting a new benchmark for state-of-the-art performance in the reference-based setting. Furthermore, it achieves comparable results to leading metrics in the reference-free setting, offering greater efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration Anugraha, David Kuwanto, Garry Susanto, Lucky Wijaya, Derry Tanti Winata, Genta Indra Computation and Language Artificial Intelligence Machine Learning We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Gaussian Processes. MetaMetrics-MT enhances existing MT metrics by optimizing their correlation with human judgments. Our experiments on the WMT24 metric shared task dataset demonstrate that MetaMetrics-MT outperforms all existing baselines, setting a new benchmark for state-of-the-art performance in the reference-based setting. Furthermore, it achieves comparable results to leading metrics in the reference-free setting, offering greater efficiency. |
| title | MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.00390 |