diff --git a/data/xml/2022.wassa.xml b/data/xml/2022.wassa.xml index bbf69bc6ea..4a177e74c8 100644 --- a/data/xml/2022.wassa.xml +++ b/data/xml/2022.wassa.xml @@ -449,7 +449,7 @@ Polite Task-oriented Dialog Agents: To Generate or to Rewrite? - DiogoSilva + DiogoSilva DavidSemedo JoãoMagalhães 304-314 diff --git a/data/xml/2024.acl.xml b/data/xml/2024.acl.xml index 7c1aee974b..eabce4626e 100644 --- a/data/xml/2024.acl.xml +++ b/data/xml/2024.acl.xml @@ -9658,7 +9658,7 @@ JoãoBordaloNOVA School of Science and Technology VascoRamos RodrigoValério - DiogoGlória-SilvaUniversidade NOVA de Lisboa + DiogoGlória-SilvaUniversidade NOVA de Lisboa YonatanBittonGoogle MichalYaromResearch, Google IdanSzpektorGoogle diff --git a/data/xml/2024.eacl.xml b/data/xml/2024.eacl.xml index 46c180c795..6145530096 100644 --- a/data/xml/2024.eacl.xml +++ b/data/xml/2024.eacl.xml @@ -1054,7 +1054,7 @@ Plan-Grounded Large Language Models for Dual Goal Conversational Settings - DiogoGlória-SilvaUniversidade NOVA de Lisboa + DiogoGlória-SilvaUniversidade NOVA de Lisboa RafaelFerreiraUniversidade NOVA de Lisboa DiogoTavaresUniversidade NOVA de Lisboa DavidSemedoUniversidade NOVA de Lisboa and Universidade NOVA de Lisboa diff --git a/data/xml/2024.emnlp.xml b/data/xml/2024.emnlp.xml index 2c32db9d75..94cf3a2281 100644 --- a/data/xml/2024.emnlp.xml +++ b/data/xml/2024.emnlp.xml @@ -16421,7 +16421,7 @@ Show and Guide: Instructional-Plan Grounded Vision and Language Model - DiogoGlória-SilvaUniversidade NOVA de Lisboa + DiogoGlória-SilvaUniversidade NOVA de Lisboa DavidSemedoUniversidade NOVA de Lisboa and Universidade NOVA de Lisboa JoaoMagalhaesUniversidade Nova de Lisboa 21371-21389 diff --git a/data/xml/2025.semeval.xml b/data/xml/2025.semeval.xml index 8a1d032cea..9631c23389 100644 --- a/data/xml/2025.semeval.xml +++ b/data/xml/2025.semeval.xml @@ -3218,7 +3218,7 @@ Julia S.Dollis Daniel M.Pedrozo Artur M. A.Novais - Diogo F. C.Silva + Diogo F. C.Silva Arlindo R.Galvão Filho 2305-2310 This paper investigates the impact of data quality and processing strategies on emotion recognition in Brazilian Portuguese (PTBR) texts. We focus on data distribution, linguistic context, and augmentation techniques such as translation and synthetic data generation. To evaluate these aspects, we conduct experiments on the PTBR portion of the BRIGHTER dataset, a manually curated multilingual dataset containing nearly 100,000 samples, of which 4,552 are in PTBR. Our study encompasses both multi-label emotion detection (presence/absence classification) and emotion intensity prediction (0 to 3 scale), following the SemEval 2025 Track 11 setup. Results demonstrate that emotion intensity labels enhance model performance after discretization, and that smaller multilingual models can outperform larger ones in low-resource settings. Our official submission ranked 6th, but further refinements improved our ranking to 3rd, trailing the top submission by only 0.047, reinforcing the significance of a data-centric approach in emotion recognition. diff --git a/data/yaml/name_variants.yaml b/data/yaml/name_variants.yaml index 376d223213..1207148aa6 100644 --- a/data/yaml/name_variants.yaml +++ b/data/yaml/name_variants.yaml @@ -919,6 +919,16 @@ - canonical: {first: Hatte, last: Blejer} variants: - {first: Hatte R., last: Blejer} +- canonical: {first: Diogo, last: Glória-Silva} + variants: + - {first: Diogo, last: Silva} + id: diogo-silva-nova + orcid: 0000-0002-4420-7455 + institution: NOVA University of Lisbon, School of Science and Technology + comment: NOVA +- canonical: {first: Diogo, last: Glória-Silva} + id: diogo-silva + comment: May refer to several people - canonical: {first: André, last: Blessing} variants: - {first: Andre, last: Blessing}