Papers by Frederico Belcavello
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 . |
Frame2: A FrameNet-based Multimodal Dataset for Tackling Text-image Interactions in Video (2024.lrec-main)
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Frederico Belcavello, Tiago Timponi Torrent, Ely E. Matos, Adriana S. Pagano, Maucha Gamonal, Natalia Sigiliano, Lívia Vicente Dutra, Helen de Andrade Abreu, Mairon Samagaio, Mariane Carvalho, Franciany Campos, Gabrielly Azalim, Bruna Mazzei, Mateus Fonseca de Oliveira, Ana Carolina Loçasso Luz, Lívia Pádua Ruiz, Júlia Bellei, Amanda Pestana, Josiane Costa, Iasmin Rabelo, Anna Beatriz Silva, Raquel Roza, Mariana Souza, Igor Oliveira
| Challenge: | et al., 2016) describe a multimodal dataset built from a Brazilian travel TV show . frameNet is composed of frames and their associated roles in a network of typed frame-to-frame relations. |
| Approach: | They present a multimodal dataset built from a Brazilian travel TV show annotated for FrameNet categories for both text and image communicative modes. |
| Outcome: | The proposed dataset includes 230 minutes of video annotated for FrameNet categories . the model can be applied to other communicative modes, i.e., images . |