Jinan Zou, Hai Cao, Yanxi Liu, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
{"title":"FinNLP-2022 ERAI任务中的UOA:利用类标签描述进行财务意见挖掘","authors":"Jinan Zou, Hai Cao, Yanxi Liu, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi","doi":"10.18653/v1/2022.finnlp-1.15","DOIUrl":null,"url":null,"abstract":"Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.","PeriodicalId":331851,"journal":{"name":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining\",\"authors\":\"Jinan Zou, Hai Cao, Yanxi Liu, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi\",\"doi\":\"10.18653/v1/2022.finnlp-1.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.\",\"PeriodicalId\":331851,\"journal\":{\"name\":\"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2022.finnlp-1.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.finnlp-1.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining
Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.