2023年自然语言处理算法鲁棒性研究思考报告

00各类自然语言处理算法快速发展，在很多任务上甚至超越人类00各类自然语言处理算法快速发展，在很多任务上甚至超越人类4400算法在实际应用中的效果却不尽如人意00算法在实际应用中的效果却不尽如人意00不经过鲁棒性评估会面临巨大风险7700模型对测试数据的微小变化非常敏感9900模型对测试数据的微小变化非常敏感00模型对测试数据的微小变化非常敏感00为什么会这样？问题1:为什么基于基准测试集合和常用评价指标的模式不能反映上述问题?问题2:深度神经网络模型到底学习到了什么?问题3:现阶段自然语言处理算法鲁棒性究竟怎么样?00为什么会这样？问题1:为什么基于基准测试集合和常用评价指标的模式不能反映上述问题?问题2:深度神经网络模型到底学习到了什么?问题3:现阶段自然语言处理算法鲁棒性究竟怎么样?11数据集上存在偏置–WINOGRANDEHaveneurallanguagemoverestimatingthetruecapabilitiesofmachinecommonsense?11数据集上存在偏置–WINOGRANDE11数据集上存在偏置–WINOGRANDE1.RoBERTafine-tunedonasmallsubsetofthedataset.2.Anensembleoflinearclassifiers(logisticregressions)3.Trainedonrandomsubsetsofthedata4.Determinewhethertherepresentationisstronglyindicativeofthecorrectansweroption5.Discardthecorrespondinginstances11数据集上存在偏置–WINOGRANDE11数据集上存在偏置–WINOGRANDEFuetal.,RethinkingGeneralizationofNeuralModels:ANamedEntityRecognitionCaseStudy，AAAI2020Liuetal.,EXPLAINABOARD:AnExplainableLeaderboard1.基准集合构建时通常存在数据偏置2.粗粒度的评测指标不能够全面反映模型特性00为什么会这样？问题1:为什么基于基准测试集合和常用评价指标的模式不能反映上述问题?问题2:深度神经网络模型到底学习到了什么?问题3:现阶段自然语言处理算法鲁棒性究竟怎么样?Sundararajanetal.,Axiomaticattributionfordeepnetworks.2017基于Bert的用户检索词---文章语义匹配模型AttentionheadsexhibitingpatternsAttentionheadscorrespondingtolinguisticphenomenaThebestperformingattentionsheadsofBERTonWSJdependencyparsingoftenexhibitingsimilarCertainattentionheadscorreAttention-basedprobingclassifierdemonstratedthatsubstantialsyntacticinformationcoulHowupweightingaparticulartrainingexample(xi,yi)intheHowthischangeinthemodelparameterswouldtrainingset{(x1,y1),…,((xn,yn)}byε!wouldchangetheinturnaffectthelossofthetestinputlearnedmodelparametersθHanetal.,Explainingblackboxpredictionsandunveiling00问题2：深度神经网络模型到底学习到了什么？非常初步的猜想,大规模数据分析和实验中3.预训练语言模型学习到了部分复述(paraphrase)的相似表示覆盖了人工构造的基础特征,以及人工很难构造的特征高阶综合00为什么会这样？问题1:为什么基于基准测试集合和常用评价指标的模式不能反映上述问题?问题2:深度神经网络模型到底学习到了什么?问题3:现阶段自然语言处理算法鲁棒性究竟怎么样?Hauseretal.,BERTisRobust!ACaseAgainstSynonym-BasedAdversarialExamplesinTextClassificatiSietal.,BenchmarkingRobustneSietal.,BenchmarkingRobustnessofMachineReadingCompr….@AmericanAirservicewas完备性-20种通用变形、60种任务特有变形、数千种变形组合可接受-所有变形基于语言学知识分析功能-对评测结果给出可视化分析报告“HelovesNLP”istransformedint拼写错误反义词“HewasborninChina”“HewasborninLlanfairpwllgwyngyllgogeryc看“看看,”“看一看,”“看了看,”and“看了一看.”“Thereisanappleonthedesk”“Thereisanimponderableonthedesk”原始集合Shebecameanurseandworkedinahospital.ItoldJohntocomeearly,buthefailed.TheriverderivesfromsouthernAmerica.Marrywouldliketoteachkidsinthekindergarten.Thestormdestroyedmanyhousesinthevillage.√√√•Plausibility(Lambertetal.,2010)measureswhetherthetextisreasonableandwrittenbynativespeakers.Sentencesordocumentsthatarenatural,appropriate,logicallycorrect,andmeaningfulinthecontextwillreceiveahigherplausibilityscore.Textsthatarelogicallyorsemanticallyinconsistentorcontaininappropriatevocabularywillreceivealowerplausibilityscore.•Grammaticality(Newmeyer,1983)measureswhetherthetextcontainssyntaxerrors.Itreferstotheconformityofthetexttotherulesdefinedbythespecificgrammarofalanguage.Gui,Tao,etal."Textflint:UnifiedmultilingualrobustnessevaluationtoELMO+BiLSTM+CRFBert+BiLSTM+CRFUANETS-LSTM meta-taggerLM-LSTM-CRFBILSTM-LAN BiLSTM-aux CRF++CNN+BiLSTM+CRF xlnet-large-cased xlnet-base-cased roberta-largeroberta-basebert-large-uncasedbert-base-uncased albert-xxlarge-v2albert-base-v2Match-lstm LCF-BERTBERT-SPCBERT-BASEAEN-BERTTNetTD-LSTM MGANLSTMATAE-LSTMMemNetAEN-GloveParadigmaticRelationMorphologySyntaxPragmaticsParadigmaticRelationMorphologyABSAABSAContractionKeyboardOcrSpellingErrorTyposWordCase-lowerWordCase-titleWordCase-upperInsertAdvSwapNamedEntAddPuncAppendIrrTwitterTypeMLMSuggestionSwapNumSwapSyn-WordNetContractionKeyboardOcrSpellingErrorTyposWordCase-lowerWordCase-titleWordCase-upperInsertAdvSwapNamedEntAddPuncAppendIrrTwitterTypeMLMSuggestionSwapNumSwapSyn-WordNetSwapSyn-WordEmbeddingSwapSyn-WordEmbeddingMRC-SQuAD2.0MRC-SQuAD1.1SM-qqpSM-mrpcNLI-SNLINLI-MNLI-mmNLI-MNLI-mCOREF-OntonotesABSA-SemEval2014-RestaurantABSA-SemEval2014-LaptopSA-Yelp-BinarySA-IMDBDP-PTBPOS-WSJParadigmaticRelationMorphologySyntaxPragmaticsParadigmaticRelationMorphologyContractionKeyboardOcrSpellingErrorWordCase-lowerWordCase-titleWordCase-upperInsertAdvSwapNamedEntAddPuncAppendIrrTwitterTypeMLMSuggestionSwapNumSwapSyn-WordEmbeddingSwapSyn-WordNetContractionKeyboardOcrSpellingErrorWordCase-lowerWordCase-titleWordCase-upperInsertAdvSwapNamedEntAddPuncAppendIrrTwitterTypeMLMSuggestionSwapNumSwapSyn-WordEmbeddingSwapSyn-WordNetGlobalSemanticsGlobalSemanti