Papers by Israfel Salazar
SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation (2025.emnlp-main)
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| Challenge: | N-gram-based evaluation metrics are unreliable due to low correlation to human judgments. |
| Approach: | They propose a metric that rewards correct details and penalizes incorrect ones. |
| Outcome: | The proposed metric matches the performance of open-source LLM-based metrics in correlation to human judgments while being far more efficient. |
Long Story Short: Disentangling Compositionality and Long-Caption Understanding in Contrastive VLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks for vision-language models treat compositionality and long-caption understanding in isolation. |
| Approach: | They analyze when compositional reasoning and long-caption understanding transfer across tasks and when this relationship fails. |
| Outcome: | The proposed model can generalize on poorly grounded captions and with strong visual grounding, while architectural choices can limit compositional learning. |
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation (2025.findings-emnlp)
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Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, Thamar Solorio
| Challenge: | a human-curated benchmark of over 5,800 triples of images is used to evaluate multimodal translation systems. |
| Approach: | They introduce a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. |
| Outcome: | The results show that visual context improves translation quality in culturally-specific items . |