Deokgyu Seo

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2024

pdf bib
OZemi at SemEval-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation
Hidetsune Takahashi | Xingru Lu | Sean Ishijima | Deokgyu Seo | Yongju Kim | Sehoon Park | Min Song | Kathylene Marante | Keitaro-luke Iso | Hirotaka Tokura | Emily Ohman
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.
Search
Co-authors
Venues
Fix data