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| 本文作者： 我在思考中 | 2021-11-10 15:37 |
多標簽文本分類是自然語言處理中的一類經(jīng)典任務(wù)，訓(xùn)練模型為給定文本標記上不定數(shù)目的類別標簽。然而實際應(yīng)用時，各類別標簽的訓(xùn)練數(shù)據(jù)量往往差異較大（不平衡分類問題），甚至是長尾分布，影響了所獲得模型的效果。重采樣（Resampling）和重加權(quán)（Reweighting）常用于應(yīng)對不平衡分類問題，但由于多標簽文本分類的場景下類別標簽間存在關(guān)聯(lián)，現(xiàn)有方法會導(dǎo)致對高頻標簽的過采樣。本項工作中，我們探討了優(yōu)化損失函數(shù)的策略，尤其是平衡損失函數(shù)在多標簽文本分類中的應(yīng)用。基于通用數(shù)據(jù)集 (Reuters-21578，90 個標簽) 和生物醫(yī)學(xué)領(lǐng)域數(shù)據(jù)集（PubMed，18211 個標簽）的多組實驗，我們發(fā)現(xiàn)一類分布平衡損失函數(shù)的表現(xiàn)整體優(yōu)于常用損失函數(shù)。研究人員近期發(fā)現(xiàn)該類損失函數(shù)對圖像識別模型的效果提升，而我們的工作進一步證明其在自然語言處理中的有效性。
多標簽文本分類是自然語言處理（NLP）的核心任務(wù)之一，旨在為給定文本從標簽庫中找到多個相關(guān)標簽，可應(yīng)用于搜索（Prabhu et al., 2018）和產(chǎn)品分類（Agrawal et al., 2013）等諸多場景。圖 1 展示了通用多標簽文本分類數(shù)據(jù)集 Reuters-21578 的樣例數(shù)據(jù)（Hayes and Weinstein, 1990）。
圖2 Reuters-21578的長尾分布和標簽連鎖現(xiàn)象。
圖3 損失函數(shù)的具體設(shè)計。
表1 實驗用數(shù)據(jù)集的基本信息
表2 實驗結(jié)果對比
羅氏集團制藥部門中國 CIO 施涪軍：該工作來自于合作團隊在生物醫(yī)學(xué)領(lǐng)域的深度學(xué)習(xí)應(yīng)用探索。相比于日常文本，生物醫(yī)學(xué)領(lǐng)域的語料往往更專業(yè)，而標注更稀疏，導(dǎo)致 AI 應(yīng)用面臨“最后一公里”的落地挑戰(zhàn)。本論文從稀疏標注的長尾分布等問題入手，由 CV 前沿研究引入損失函數(shù)并優(yōu)化，使得既有 NLP 模型可以在框架不變的情況下將訓(xùn)練資源向?qū)嵗^少的類別平衡，進而實現(xiàn)整體的模型效果提升。很高興看到此策略在面臨類似問題的日常文本上同樣有效，希望繼續(xù)與院校、企業(yè)在前沿技術(shù)的研究與應(yīng)用上扎實共創(chuàng)。
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