第四課開放領(lǐng)域機(jī)器人模型

Seq2seq模型的evaluationBeamsearch:Seq2seq模型 BeamsearchBeamsearchSequencetosequencelearningwithNeuralNetworks(Sutskever2014,cited>1700)ANeuralConversationalModel模型evaluationHowNOTToEvaluateYourDialogueSystem:AnEmpiricalStudyofUnsupervisedEvaluationMetricsforDialogueResponseGenerationTrainingEnd-to-EndDialogueSystemswiththeUbuntuDialogueCorpus(2017)Towardsanautomaticturingtest:learningtoevaluatedialogueresponsesBeamSequencetosequencelearningwithNeuralNetworks(Sutskever2014,cited>1700)ANeuralConversationalModelSequence-to-SequenceLearningasBeam-SearchOptimizationTrainingEnd-to-EndDialogueSystemswiththeUbuntuDialogueCorpusSequencetoSequenceLearningwith實(shí)驗(yàn)表明LSTM可以處理非常長的句子(didnotsufferonverylongsetnences)SequencetoSequenceLearningwith表達(dá)向量(vectorrepresentation)使用beamsearch從句子的表達(dá)向量出發(fā)，使用SequencetoSequenceLearningwith相對于RNNLM(input&outputperfectly，seq2seq模型的一個是，input和output句SequencetoSequenceLearningwithSequencetoSequenceLearningwithSequencetoSequenceLearningwith ANeuralConversational將前述的MT問題中引入的的seq2seq(simplicity,generality)模型應(yīng)用于 給出(query,response)以后，seq2seq結(jié)構(gòu)方便 objectivefunctionbeingoptimizeddoesnotcapturetheactualobjectiveachievedthroughhumancommunication,whichistypicallylongertermandbasedonexchangeofinformationratherthannextstepprediction.)ANeuralConversationalQueryResponseANeuralConversational

Evaluation和訓(xùn)練樣本中的真實(shí)句子的crossentropy）使用合適的Metric衡量產(chǎn)生的句子是非常EvaluationBLEU(BiLingualEvaluation"thecloseramachinetranslationistoaprofessionalhumantranslation,thebetteritis““BLEUisdesignedtoapproximatehumanjudgementatacorpuslevel,andperformsbadlyifusedtoevaluatethequalityofindividualsentences.”——wikipediaHowNOTToEvaluateYourDialogue 兩個回復(fù)如果不看contextTrainingEnd-to-EndDialogueSystemwithHumanWord-overlapBLEU,METEOR,word-embeddingmetrics/Vector-baseEmbeddingaverageGreedymatchingVectorextremaTrainingEnd-to-EndDialogueSystemwithVector-basemetrics側(cè)重比較生成的句子和真實(shí)樣本的語意EmbeddingaveragescorecosinesimilarityGreedymatchingscore：尋找生成的句子和真實(shí)句子中最Vectorextremascore：提取句中單詞的詞向量的值，然后計算cosinesimilarityHowNOTToEvaluateYourDialoguemetric都是辣雞“Wefindthatallmetricsshoweitherweakornocorrelationwithhumanjudgements,despitethefactthatwordoverlapmetricshavebeenusedextensivelyintheli evaluatingdialogueresponsemodels” (onlyasmallpositivecorrelationonchitchat correlationatallonthetechnicalUDC)LearningtoEvaluateDialogue使用機(jī)器學(xué)習(xí) 個好的(decoding)BEAMBeamsearch1

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