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Title:	使用子主題建模的零樣本立場偵測 Zero-shot stance detection with subtopic modeling
Authors:	穆永綸 Mu, Yung-Lun
Contributors:	黃瀚萱 Huang, Hen-Hsen 穆永綸 Mu, Yung-Lun
Keywords:	立場偵測遷移學習零樣本學習 Stance detection Transfer learning Zero-shot learning
Date:	2022
Issue Date:	2022-12-02 15:23:22 (UTC+8)
Abstract:	立場偵測任務有助我們初步了解發文者對特定主題的態度，但立場偵測模型的建立往往受限於標籤資料的不足，本研究針對可充分運用既有標籤資料的零樣本立場偵測任務進行研究，此外，由於網路社群媒體儼然是當前了解民眾意向的重要媒介，我們聚焦網路社群文本的立場偵測任務。我們的模型同時考量了「主題獨立」以及「主題相依」2 種可遷移學習的文本特徵，提升模型的泛化能力，利用資料集的立場標籤與主題資訊，進行對抗學習與監督對比學習：透過立場與主題分類的對抗學習，加強模型對「主題獨立」的特徵表示，另以 2 種監督對比學習設定，在強化模型對「主題獨立」特徵表示的同時，也能凸顯「主題相依」的特徵表示，實驗顯示此方法有助於立場偵測任務。另我們也嘗試透過LDA 子主題建模，以LDA 主題模型產生的子主題詞組增加輸入模型的語意資訊，然由於 LDA 主題模型是以非監督學習方法建模，其過程並未考量立場偵測任務需求，使得子主題詞組的方法未能在每個主題都發揮效果。 Stance detection(SD) tasks give us an initial look at the attitudes of author on a particular topic. Though fully supervised SD model can achieve favorable performance, the lack of labeled data limits its availability. In this work, we focus on zero-shot stance detection(ZSSD). Besides, as online social media get popular, we take textual content from online community as our research target. To generalize stance features for unseen target, we consider both ” topic-invariant” and ” topic-dependent” features that are transferable between source and target domain. Specifically, to make the use of ”topic-invariant” features, a stance classifier and a topic discriminator is set for adversarial learning. To further generalize ” topic-invariant” and ” topic-dependent” stance features, contrastive learning strategy is deployed using the stance label and topic information from data-set. Experiments on benchmark data-set show that the proposed approach is feasible. Furthermore, we build LDA subtopic model for each target and augment the semantic information through the subtopic words. However, since our subtopic modeling task is unsupervised and independent from stance detection task, the beneficial of subtopic modeling turned out to be unstable.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 109971003
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109971003
Data Type:	thesis
DOI:	10.6814/NCCU202201678
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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