{"_buckets": {"deposit": "645d2323-891e-4dec-a0c6-6f2cf634c1e6"}, "_deposit": {"created_by": 23, "id": "2001305", "owners": [23], "pid": {"revision_id": 0, "type": "depid", "value": "2001305"}, "status": "published"}, "_oai": {"id": "oai:repository.nii.ac.jp:02001305", "sets": ["1697763112205"]}, "author_link": [], "control_number": "2001305", "item_30002_conference34": {"attribute_name": "会議記述", "attribute_value_mlt": [{"subitem_conference_country": "JPN", "subitem_conference_date": {"subitem_conference_date_language": "en", "subitem_conference_end_day": "15", "subitem_conference_end_month": "12", "subitem_conference_end_year": "2023", "subitem_conference_period": "December 12-15, 2023", "subitem_conference_start_day": "12", "subitem_conference_start_month": "12", "subitem_conference_start_year": "2023"}, "subitem_conference_names": [{"subitem_conference_name": "NTCIR-17 Conference", "subitem_conference_name_language": "en"}], "subitem_conference_sequence": "17", "subitem_conference_sponsors": [{"subitem_conference_sponsor": "National Institute of Informatics", "subitem_conference_sponsor_language": "en"}], "subitem_conference_venues": [{"subitem_conference_venue": "National Institute of Informatics", "subitem_conference_venue_language": "en"}]}]}, "item_30002_creator2": {"attribute_name": "作成者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Zih An Lin", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Hsiao Min Li", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Adam Lin", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yun Ching Kao", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Chia Shen Hsu", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yao Chung Fan", "creatorNameLang": "en"}]}]}, "item_30002_date11": {"attribute_name": "日付", "attribute_value_mlt": [{"subitem_date_issued_datetime": "2023-12-12", "subitem_date_issued_type": "Issued"}]}, "item_30002_description9": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "In the exploration of the task \"Identifying Attack and Support Argumentative Relations in Social Media Discussion Threads\" (SMDT) [1], we aim to discern differences between proponents and adversaries in financial discussions on the internet. For classification tasks such as these, fine-tuning Transformer models like BERT [2] is an intuitive approach. In this study, we build upon this foundation by incorporating the Masked Language Model technique to enrich the model’s domain knowledge within the financial field. Furthermore, we optimize the model’s performance by adjusting the weights in the loss function. Experimental results confirm that both methods effectively enhance the model’s performance. This research introduces three simple yet effective methods to improve the Transformer model’s ability for SMDT. 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Quack at the NTCIR-17 FinArg-1 Task : Boosting and MLM Enhanced Financial Knowledge Sequence Classification
https://doi.org/10.20736/0002001305
https://doi.org/10.20736/0002001305