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| 本文作者： 我在思考中 | 2021-11-10 15:37 |
多標(biāo)簽文本分類(lèi)是自然語(yǔ)言處理中的一類(lèi)經(jīng)典任務(wù)，訓(xùn)練模型為給定文本標(biāo)記上不定數(shù)目的類(lèi)別標(biāo)簽。然而實(shí)際應(yīng)用時(shí)，各類(lèi)別標(biāo)簽的訓(xùn)練數(shù)據(jù)量往往差異較大（不平衡分類(lèi)問(wèn)題），甚至是長(zhǎng)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(duì)不平衡分類(lèi)問(wèn)題，但由于多標(biāo)簽文本分類(lèi)的場(chǎng)景下類(lèi)別標(biāo)簽間存在關(guān)聯(lián)，現(xiàn)有方法會(huì)導(dǎo)致對(duì)高頻標(biāo)簽的過(guò)采樣。本項(xiàng)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(biāo)簽文本分類(lèi)中的應(yīng)用。基于通用數(shù)據(jù)集 (Reuters-21578，90 個(gè)標(biāo)簽) 和生物醫(yī)學(xué)領(lǐng)域數(shù)據(jù)集（PubMed，18211 個(gè)標(biāo)簽）的多組實(shí)驗(yàn)，我們發(fā)現(xiàn)一類(lèi)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(lèi)損失函數(shù)對(duì)圖像識(shí)別模型的效果提升，而我們的工作進(jìn)一步證明其在自然語(yǔ)言處理中的有效性。
多標(biāo)簽文本分類(lèi)是自然語(yǔ)言處理（NLP）的核心任務(wù)之一，旨在為給定文本從標(biāo)簽庫(kù)中找到多個(gè)相關(guān)標(biāo)簽，可應(yīng)用于搜索（Prabhu et al., 2018）和產(chǎn)品分類(lèi)（Agrawal et al., 2013）等諸多場(chǎng)景。圖 1 展示了通用多標(biāo)簽文本分類(lèi)數(shù)據(jù)集 Reuters-21578 的樣例數(shù)據(jù)（Hayes and Weinstein, 1990）。
圖2 Reuters-21578的長(zhǎng)尾分布和標(biāo)簽連鎖現(xiàn)象。
圖3 損失函數(shù)的具體設(shè)計(jì)。
表1 實(shí)驗(yàn)用數(shù)據(jù)集的基本信息
表2 實(shí)驗(yàn)結(jié)果對(duì)比
羅氏集團(tuán)制藥部門(mén)中國(guó) CIO 施涪軍：該工作來(lái)自于合作團(tuán)隊(duì)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(yǔ)料往往更專(zhuān)業(yè)，而標(biāo)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(biāo)注的長(zhǎng)尾分布等問(wèn)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(lèi)別平衡，進(jìn)而實(shí)現(xiàn)整體的模型效果提升。很高興看到此策略在面臨類(lèi)似問(wèn)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(shí)共創(chuàng)。
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