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FTRI at the NTCIR-18 FinArg-2 Task: Identify Temporal Reference in Earnings Conference Calls
https://doi.org/10.20736/0002002037
https://doi.org/10.20736/0002002037080e0b66-3f55-4f67-9ec3-f94f7cc22244
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
02-NTCIR18-FINARG-ErfinaA.pdf (600.5 KB)
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| アイテムタイプ | デフォルトアイテムタイプ（フル）(1) | |||||||||
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| 公開日 | 2025-06-06 | |||||||||
| タイトル | ||||||||||
| タイトル | FTRI at the NTCIR-18 FinArg-2 Task: Identify Temporal Reference in Earnings Conference Calls | |||||||||
| 言語 | en | |||||||||
| 作成者 |
Adhitia Erfina
× Adhitia Erfina
× Phuong Le-Hong
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| 内容記述 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | FinArg-2 is part of the NTCIR Financial Argument shared task series which aims to improve argument understanding in financial analysis. FinArg-2 aims to introduce "Temporal Inference of Financial Arguments" focusing on the assessment of temporal information, which is a distinct phenomenon in financial opinions. FTRI participates in FinArg-2 on the Earnings Conference Calls (ECC) subtask, where models must identify the temporal reference associated with an argument. At the initial stage we conducted experiments on variation of transformers models using several configurations at the preprocessing and training stages. BERT-Base-Uncased, BERT-Large-Uncased, and RoBERTa-Base-Uncased showed slightly superior performance compared to the other models. So, in the overall model that we created, we only fine-tuned those models as our baseline model. Our first model’s output FTRI_ECC_1, we use a transformer encoder approach with BERT-Large, resulting in 71.43% Micro F1 and 68.58% Macro F1. Our second model’s output FTRI_ECC_2, we use attention mask in Claim, Premise, and (Year + Quarter) approach with BERT-Base, resulting in 69.05% Micro F1 and 65.76% Macro F1. Our third model’s output FTRI_ECC_3, we use TF-IDF (Claim + Premise) + One-hot encoding (Year + Quarter) approach with BERT-Base, resulting in 77.38% Micro F1 and 75.07% Macro F1, which is the best results in this ECC Subtask. The evaluation results show that the 3 output models we created are in the top 4 among other participants based on Micro and Macro F1. | |||||||||
| 言語 | en | |||||||||
| 出版者 | ||||||||||
| 出版者 | NII Institutional Repository | |||||||||
| 言語 | en | |||||||||
| 日付 | ||||||||||
| 日付 | 2025-06-06 | |||||||||
| 日付タイプ | Issued | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||
| 資源タイプ | conference paper | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.20736/0002002037 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 関連情報 | ||||||||||
| 関連タイプ | isReferencedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://research.nii.ac.jp/ntcir/ntcir-18/index.html | |||||||||
| 言語 | en | |||||||||
| 関連名称 | NTCIR-18 Conference | |||||||||
| 開始ページ | ||||||||||
| 開始ページ | none | |||||||||
| 会議記述 | ||||||||||
| 会議名 | NTCIR-18 Conference | |||||||||
| 言語 | en | |||||||||
| 回次 | 18 | |||||||||
| 主催機関 | National Institute of Informatics | |||||||||
| 言語 | en | |||||||||
| 開始年 | 2025 | |||||||||
| 開始月 | 6 | |||||||||
| 開始日 | 10 | |||||||||
| 終了年 | 2025 | |||||||||
| 終了月 | 6 | |||||||||
| 終了日 | 13 | |||||||||
| 開催期間 | June 10-13, 2025 | |||||||||
| 言語 | en | |||||||||
| 開催会場 | National Institute of Informatics | |||||||||
| 言語 | en | |||||||||
| 開催国 | JPN | |||||||||
