{"id":943,"date":"2025-08-15T13:19:26","date_gmt":"2025-08-15T05:19:26","guid":{"rendered":"https:\/\/ainlp.tw\/?p=943"},"modified":"2025-08-15T13:24:29","modified_gmt":"2025-08-15T05:24:29","slug":"nycu-nlp-at-semeval-2025-task-11-assembling-small-language-models-for-multilabel-emotion-detection-and-intensity-prediction","status":"publish","type":"post","link":"https:\/\/ainlp.tw\/index.php\/2025\/08\/15\/nycu-nlp-at-semeval-2025-task-11-assembling-small-language-models-for-multilabel-emotion-detection-and-intensity-prediction\/","title":{"rendered":"NYCU-NLP at SemEval-2025 Task 11: Assembling Small Language Models for Multilabel Emotion Detection and Intensity Prediction"},"content":{"rendered":"\n<p>Zhe-Yu Xu, Yu-Hsin Wu, and\u00a0Lung-Hao Lee*.<\/p>\n\n\n\n<p>In\u00a0<em>Proceedings of The 19th International Workshop on Semantic Evaluation (SemEval-2025)<\/em>, pages 1129\u20131135.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-medium-font-size\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p>This study describes the design of the NYCU-NLP system for the SemEval-2025 Task 11 that focuses on multi-lingual text- based emotion analysis. We instruction- tuned three small language models: Gemma-2 (27B), Mistral-small-3 (22B), and Phi-4 (14B) and then assembled them as our main system architecture. Our NYCU-NLP system participated the English Track A for multilabel emotion detection and English Track B for emotion intensity prediction. Experimental results show our best-performing submission produced a macro-averaging F1 score of 0.8225, ranking second of 74 participating teams for Track A, and ranked second among 36 teams for Track B with a Pearson correlation coefficient of 0.8373 in the task official rankings.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1538\" height=\"886\" src=\"https:\/\/ainlp.tw\/wp-content\/uploads\/2025\/08\/semeval_2025.png\" alt=\"\" class=\"wp-image-941\"\/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Zhe-Yu Xu, Yu-Hsin Wu, and\u00a0Lung-Hao Lee*<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[6],"tags":[48,43,17],"class_list":["post-943","post","type-post","status-publish","format-standard","hentry","category-achievements","tag-48","tag-emotion","tag-semeval"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/posts\/943","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/comments?post=943"}],"version-history":[{"count":2,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/posts\/943\/revisions"}],"predecessor-version":[{"id":945,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/posts\/943\/revisions\/945"}],"wp:attachment":[{"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/media?parent=943"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/categories?post=943"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ainlp.tw\/index.php\/wp-json\/wp\/v2\/tags?post=943"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}