网络研讨会开发聊天机器人Python04

DevelopingaChatbotinPython

-MrityunjayaSingh,Scientist-ENIC-MoHUADivision

NewDelhi

Chatbots

Chatbotsarecomputerprogramthatsimulateshumanconversationthroughvoicecommandsortextchatsorbothinnaturallanguage,understandtheuser’sintentandsendresponsesbasedontheapplication’sbusinessrulesanddata.

Theycandecipherverbalorwrittenquestionsandprovideresponseswithappropriateinformationordirection.

ItcanbeconsideredasanenhancedchannelofuserinteractionwhichwouldmovefromInteractiveVoiceResponsetoIntelligentAssistantResponse.

TypesofChatbots

Therearetwokindsofchatbots:ScriptedbotsandArtificialIntelligence(AI)bots.

Scriptedbotislikearule-basedguidedconversationandperformslikeadecisiontreewhereeachactionbytheuserpromptsthebottotakeactionorrespond.Itengagesuserswithquestionsaboutpreferencesandservesupcontentandoffersrelevanttotheirresponses.

AIbotsarebuiltonMachineLearning(ML)andNaturalLanguageProcessing(NLP)capabilities.Theyarebasedonthehumancapabilityoflearningandabsorbinginformationowingtowhichtheyaremoreefficientandcanprocessmuchfasterthanhumansandmaycomeupwithmoresubtleresults.Themoretheconsumerengageswiththebotovertime,thesmarterthebotbecomesaboutconsumerpreferencesandisabletoservepersonalizedcontentandoffers.

TypesofChatbotFrameworks

TherearedifferenttypesofChatbotFrameworkssuchas–

1.Dialogflow2.Alexa3.Luis4.Wit5.Rasa

OftheseframeworksRasaisanOpenSourceframeworkfordevelopingtext-andvoice-basedchatbotsandassistants.Itcomprisesofmainlytwothings,RasaNLU,andRasaCore.ThebenefitofRasaStackisdataprivacyandzerorunningcost.Inotherframeworks,forcorpusdatatobeprocessedthroughtheirNLPengine,userchatsneedtobemovedthroughtheirservers.AndalsobeingopensourcenocostofusingtheplatforminRasa,thoughdeveloperscostwillbehigherashereyouneedexperienceddeveloperstodevelopandmaintainthechatbot.

Onecanchoosedialogfloworalexaifoneneedaquicksolution.Butforhighlyscalable,data

sensitiveassistantRasaStackisbetter.

AIChatbotArchitecture

AsintheAIbotarchitecturediagramsayforChattingPlatformSlackthechatstartsfromchattingplatformasinputrequestandendatchatplatformwithanautoresponse.Inbetweenthemessagepassesintosomanystages.HereNLUmodelsarechatbotAPIswhichextracttheinformationfromuserrequest.TheWeb-hookbackendissimplewebapplicationwhichprovidestherestfulAPI.NLUmodelextracttheinformationintheformofentitywhichworksasparameterintheserestfulAPIs.

AIbotArchitecture

AIChatbotArchitecture…

Itproducessomeresponsethatyougetasrobotresponse.TheonlymagiccomeswiththeNatural

LanguageProcessing(NLP)appliesintheseNLUs.Letsunderstanditwithanexamplebyte–”Order3

rosesformeoncomingMonday”.HereNLUshouldextractthreeinformation–

Product-Rose

User–CurrentLogin

ProductQuantity-3

Time–DatecorrespondingtoMonday

Ifyoursystemisintelligentenoughtoextracttheseparameter,Youcaneasilydevelopthebackendforit.Chatbotcansavesomuchtimeforconsumertounderstandtheproductuserinterface.TheseNLUAPIalsoprovidevoiceinterfacetouser.Onceyouspeakanythingtobot,itfirstconvertsspeechtotext.Onceitgetsthetext,NLPcomesintopicture.

ChatbotsprocessingLanguages!

Chatbotcanlooklikeanormalapp.Thereisanapplicationlayer,acorpusdatabaseandAPIsto

callexternalservices.Inacaseofthechatbot,UIisreplacedwithchatinterface.

Challengetoabotishowcanitunderstandtheintentoftheuser.Thebotsarefirsttrainedwiththeactualdata.Developersuselogsofconversationsalreadyavailabletoanalyzewhatusersaretryingtoaskandwhatdoesthatmean.Withacombinationof

MachineLearning

modelsandtoolsbuilt,developersmatchquestionsthatuserasksandanswerswiththebestsuitableanswer.Forexample:Ifauserisasking“Whereismypaymentreceipt?”and“Ihavenotreceivedapaymentreceipt”,meanthesamething.

Developersstrengthisintrainingthemodelssothatthechatbotisabletoconnectbothofthosequestionstocorrectintentandasanoutputproducesthecorrectanswer.Ifthereisnoextensivedataavailable,differentAPIsdatacanbeusedtotrainthechatbot.

HowChatbotActuallyWorks?

Thechatbotsworkbyadopting3classificationmethods:

PatternMatching

Botsusepatternmatchingtoclassifythetextandproduceasuitableresponsefortheusers.Astandardstructureofthesepatternsis“ArtificialIntelligenceMarkupLanguage”(AIML).Asimplepatternmatchingexampleis:

PatternMatching…

Themachinethengivesanoutput:

Human:DoyouknowwhoAbrahamLincolnis?

Robot:AbrahamLincolnwastheUSPresidentduringAmericancivilwar.

Chatbot

knowstheansweronlybecausehisorhernameisintheassociatedpattern.Similarly,chatbotsrespondtoanythingrelatingittotheassociatedpatterns.Butitcannotgobeyondtheassociatedpattern.Totakeittoanadvancedlevelalgorithmscanhelp.

HowChatbotActuallyWorks…

Algorithms

Foreachkindofquestion,auniquepatternmustbeavailableinthedatabasetoprovideasuitableresponse.Withlotsofcombinationonpatterns,itcreatesahierarchicalstructure.Weusealgorithmstoreducetheclassifiersandgeneratethemoremanageablestructure.Computerscientistscallita“Reductionist”approach-inordertogiveasimplifiedsolution,itreducestheproblem.

MultinomialNaiveBayesistheclassicalgorithmfortextclassificationandNLP.Foraninstance,let’sassumeasetofsentencesaregivenwhicharebelongingtoaparticularclass.Withnewinputsentence,eachwordiscountedforitsoccurrenceandisaccountedforitscommonalityandeachclassisassignedascore.Thehighestscoredclassisthemostlikelytobeassociatedwiththeinputsentence.

Algorithms…

ForexampleSampleTrainingset

class:greeting“Howyoudoing?”“goodmorning”“hithere”

FewsampleInputsentenceclassification:

input:“Hellogoodmorning”term:“hello”(nomatches)Term:“good”(class:greeting)term:“morning”(class:greeting)classification:greeting(score=2)

Algorithms…

Withthehelpofequation,wordmatchesarefoundforgivensomesamplesentencesforeachclass.Classificationscoreidentifiestheclasswiththehighesttermmatchesbutitalsohassomelimitations.Thescoresignifieswhichintentismostlikelytothesentencebutdoesnotguaranteeitistheperfectmatch.Highestscoreonlyprovidestherelativitybase.

HowChatbotActuallyWorks…

ArtificialNeuralNetworks

NeuralNetworksareawayofcalculatingtheoutputfromtheinputusingweightedconnectionswhicharecalculatedfromrepeatediterationswhiletrainingthedata.Eachstepthroughthetrainingdataamendstheweightsresultingintheoutputwithaccuracy.

ArtificialNeuralNetworks…

Asdiscussedearlierherealso,eachsentenceisbrokendownintodifferentwordsandeachwordthenisusedasinputfortheneuralnetworks.Theweightedconnectionsarethencalculatedbydifferentiterationsthroughthetrainingdatathousandsoftimes.Eachtimeimprovingtheweightstomakingitaccurate.Thetraineddataofneuralnetworkisacomparablealgorithmmoreandlesscode.Whenthereisacomparablysmallsample,wherethetrainingsentenceshave200differentwordsand20classes,thenthatwouldbeamatrixof200×20.Butthismatrixsizeincreasesbyntimesmoregraduallyandcomputationaleffortincreases.Inthiskindofsituation,processingspeedshouldbeconsiderablyhigh.

Therearemultiplevariationsinneuralnetworks,algorithmsaswellaspatternsmatchingcode.Complexitymayalsoincreaseinsomeofthevariations.Butthefundamentalremainsthesame,andtheimportantworkisthatofclassification.

NaturalLanguageUnderstanding(NLU)

Ithas3specificconceptslike:

Entities:EntitybasicallyrepresentsaconceptinyourChatbot.ItmightbeapaymentsysteminEcommerceChatbot,NGOinNGODarpanPortaletc.

Intents:Itisbasicallytheactionchatbotshouldperformwhentheusersaysomething.Forinstance,intentcantriggersamethingifusertypes“Iwanttoorderaredpairofshoes”,“Doyouhaveredshoes?Iwanttoorderthem”or“Showmesomeredpairofshoes”,alloftheseuser’stextshowtriggersinglecommandgivingusersoptionsforRedpairofshoes.

Context:WhenaNLUalgorithmanalyzesasentence,itdoesnothavethehistoryoftheuserconversation.Itmeansthatifitreceivestheanswertoaquestionithasjustasked,itwillnotrememberthequestion.Fordifferentiatingthephasesduringthechatconversation,it’sstateshouldbestored.Itcaneitherbeflagslike“OrderingPizza”orparameterslike“Restaurant:‘Dominos’”.Withcontext,youcaneasilyrelateintentswithnoneedtoknowwhatwasthepreviousquestion.

NaturalLanguageProcessing(NLP)

NaturalLanguageProcessing(NLP)Chatbottakessomecombinationofstepstoconverttheuser’stextorspeechintostructureddatathatisusedtoselecttherelatedanswer.SomeoftheNaturalLanguageProcessingstepsare:

Tokenization:TheNLPdividesastringofwordsintopiecesortokensthatarelinguisticallysymbolicorare

differentlyusefulfortheapplication.

NamedEntityRecognition:Thechatbotprogrammodellooksforcategoriesofwords,likethenameoftheproduct,theuser’snameoraddress,whateverdataisrequired.

Normalization:TheChatbotprogrammodelprocessesthetextinanefforttofindcommonspellingmistakesortypographicalerrorsthatmighteffectwhattheuserintentstoconvey.ThisgivesmorehumanlikeeffectoftheChatbottotheusers.

DependencyParsing:TheChatbotlooksfortheobjectsandsubjects-verbs,nounsandcommonphrasesinthe

user’stexttofinddependentandrelatedphrasesthatusersmightbetryingtoconvey.

SentimentAnalysis:Triestolearniftheuserishavingagoodexperienceorifaftersomepointthechatshouldbeforwardedtothehuman.

NaturalLanguageProcessing(NLP)…

LikemostoftheApplications,theChatbotisalsoconnectedtotheDatabase.Theknowledge

baseorthedatabaseofinformationisusedtofeedthechatbotwiththeinformationneededtogiveasuitableresponsetotheuser.Dataofuser’sactivitiesandwhetherornotyourchatbotwasabletomatchtheirquestions,iscapturedinthedatastore.NLPtranslateshumanlanguageintoinformationwithacombinationofpatternsandtextthatcanbemappedintherealtimetofindapplicableresponses.

ThereareNLPservicesandapplicationsprogramminginterfaces(APIs)thatareusedtobuildthechatbotsandmakeitpossibleforalltypeoforganisations,small,mediumandlargescaletousechatbotservices.ThemainpointhereisthatSmartBotshavethepotentialtohelpincreaseyourbasebyimprovingthesupportservicesandasaresultbooststheefficiencyaswellasprofitsfortheorganisation.

ChatbotwithRasaStackandPython

RASAstackisanopen-sourceAItoolandbeinganopensourceframework,itiseasytocustomize.Clientsusually

donotwanttosharetheirdataandmajorityofthetoolsavailablearecloud-basedandprovidesoftwareasaservice.WithRASA,Onecanbuild,deployorhostRasainternallyinyourserverorenvironmentwithcompletecontrolonit.

Rasacomesupwith2components—

RasaNLU—alibraryfornaturallanguageunderstanding(NLU)whichdoestheclassificationofintentandextracttheentityfromtheuserinputandhelpsbottounderstandwhattheuserissaying.

RasaCore—achatbotframeworkwithmachinelearning-baseddialoguemanagementwhichtakesthestructuredinputfromtheNLUandpredictsthenextbestactionusingaprobabilisticmodellikeLSTMneuralnetwork.

NLUandCoreareindependentandonecanuseNLUwithoutCore,andviceversa.ThoughRasarecommendsusingboth.

RasaNLU

RasaNaturalLanguageUnderstanding(NLU)comprisesofIntentClassification&EntityextractionandcreateastructuredoutputwhichcanbefedintoRasaCore.

Weneedtoteachourbottounderstandourmessagesfirst.Forthat,wehavetotraintheNLUmodelwithinputsinasimpletextformatandextractstructureddata.Wewillachievethisbydefiningtheintentsandprovidingafewwaysusersmightexpressthem.

Tomakethiswork,weneedtodefinesomefiles.Letsfirstunderstandthesefiles.

NLUtrainingfile:Itcontainssometrainingdataintermsofuserinputsalongwiththemappingofintentsandentitiespresentineachofthem.Themorevaryingexamplesyouprovide,betteryourbot’sNLUcapabilitiesbecome.

RasaNLU…

Storiesfile:Thisfilecontainssampleinteractionstheuserandbotwillhave.Rasa(Core)

createsaprobablemodelofinteractionfromeachstory.

Domainfile:Thisfilelistsalltheintents,entities,actions,templatesandsomemoreinformation.Thetemplatesarenothingbutthesamplebotreplywhichcanbeusedasactions.

Objective

LetusbuildtheBot(sayNewsie)whichwillhelptheusertogetthenewsfromaroundtheworldandalsointhespecificcategorieslikesports,business,entertainment,technologiesandmore.

Installation&Setup

Letsfirstdotheenvironmentsetupandforthat,weneedtoinstallpython.WecanusesayConda(miniconda)tosetupthevirtualenvironmentforPython.

First,installtheConda(miniconda)aspertheOS.AfterinstallationchecktheCondaversion.

$conda--versionorconda–V

Incasethereisaneedtoupgrade,runbthefollowingcommand

$condaupdateconda

OnceCondaisinstalledweshouldcreatethevirtualenvironmentandproceedwithfurther

installationofRasaNLUpackages

.

Oncethepackageinstallationisdone,Let'screatetheprojectstructure.Wehavenamedthe

projectasnewsieandsothisisourbaseprojectdirectory.Fortraining/datafiles,wecreateadatadirectoryundernewsieandcreatethetrainingfilenlu.mdinthat.

$mkdirnewsie

$cdnewsie

$mkdirdata

$cddata

$touchnlu.md

ThetrainingdataforRasaNLUisstructuredintodifferentparts:commonexamples,synonyms,regexfeaturesandlookuptables.Whilecommonexamplesistheonlypartthatismandatory,includingotherswillhelptheNLUmodellearnthedomainwithfewerexamplesandalsohelpitbemoreconfidentofitspredictions.

##intent:greet

hey

hello

Hi

##intent:fine_ask

Iamgood,howareyoudoing?

I'mfine,howareyou?

I'mgood,howareyou?##intent:fine_normal

Iamdoinggreat

I'mfine

I'mgood

##regex:zipcode[0-9]{5}

RasaNLU…

##intent:news

RasaNLU…

Sharesomelatestnewsaroundthe[world](category)?

Sharesomelatestnewsin[sports](category)?

Whatisgoingonin[technology](category)?##lookup:currencies<!--lookuptablelist-->

USD

Euro

##intent:thanks

Thanks

Thankyousomuch

##intent:bye

No,Iamgoodasofnow.Bye

Bye,haveagoodday

RasaNLU…

‘##intent:’definesthenameoftheintentand[word](entity)definestheentitye.g.—

[word](category).WehavedefinedmultipleexamplesforBottounderstanditbetter.

Nowwehavetodefinethepipelinethroughwhichthisdatawillflowandintentclassification&entityextractioncanbedoneforthebot.‘spacy_sklearn’isusedaspipeline,createdafilenlu_config.ymlinthebaseprojectdirectory(i.e.newsie)andaddedthepipelineinthat.language:"en"

pipeline:"spacy_sklearn“

NowasourNLUdataandpipelineareready,itistimetotrainthebot.Wecandosoeitherby

runningthescriptinterminalorwecancreateapythonfileandrunit.

Ifusingthescriptinterminal(don’tforgettoactivatethevirtualenvwhereallthepackageswere

installed.Inourcaseitisbotenv)

$condaactivatebotenv

$cdnewsie

$python3-mrasa_nlu.train-cnlu_config.yml--datadata/nlu.md-omodels--fixed_model_namenlu--projectcurrent–verbose

ThiswilltraintheNLUmodelandsaveitat‘models/current/nlu’.ThesamepathispassedtoNLUInterpretertoparsesomesampleintentsfromtheusertoseeifNLUisabletoclassifytheintentandextracttheentitiescorrectly.

RasaCore

Finally,thetrainingofBotisconsideredsuccessfulwhereitisabletounderstandthenatural

languagebutwestillneedtobuildthedialoguessothatbotcanrespondtothemessages.

Aftertrainingthebotweneedtobuildadialoguesmanagementforbottorespondtothemessages.

RasaCoreHighLevelArchitecture

RasaCore—DialogManagement

Earlierwetalkedaboutthestorieswhicharenothingbutthesampleinteractionbetweentheuserandthechatbot.Theuser’sinputsareexpressedasintentswithcorrespondingentities,andchatbotresponsesareexpressedasactions.

Fordialogtraining,Rasahas4maincomponents—

Domain(domain.yml)

Thedomainconsistsoffivekeypartsconsistingofintents,entities,slots,actions,and

templates.Wealreadydiscussedthefirsttwointhepreviouspart,let’sunderstandtherest.

slots:slotsarebasicallybot’smemory.Theyactasakey-valuestorewhichcanbeusedtostoreinformationtheuserprovided(e.gtheirhomecity)aswellasinformationgatheredabouttheoutsideworld(e.g.theresultofadatabasequery).

RasaCore—DialogManagement…

actions:arenothingbutbotsresponsetouserinput.Thereare3kindsofactionsinRasa

Core:defaultactions,utteractions&customactions

templates:templatesaremessagesthebotwillsendbacktotheuser.

Stories(stories.md)

Oncewehavedefinedourdomainnowlet’screateasampleuser-botinteractionasbelow.

Letsputitdowninafile.Let’snamethisfileasstories.mdandkeptitinthedatadirectorywherewekeptthenlu.mdearlier.

$cdnewsie

$cddata

$touchstories.md

Policies(policy.yml)

Therasacorepoliciesdecidewhichactiontotakeateverystepintheconversation.Therearedifferentpoliciestochoosefrom,andonecanincludemultiplepoliciesinasinglerasacoreAgentbutateveryturn,thepolicywhichpredictsthenextactionwiththehighestconfidencewillbeused.

Wehaveconfiguredabasicpolicy(policy.yml)forourbotasshownbelowwhichhasFallbackPolicyaswell.Thefallbackpolicycomesintopicturewhen‘nlu_threshold’&‘core_threshold’meetsthelevelsdefinedinthepolicywhichmeansthatbotisnotabletounderstandtheusermessageanditrespondswith‘utter_default’.

RasaCore—DialogManagement…

CustomActions(actions.py)

Wehavealreadydefinedtheutteractionbyaddinganutterancetemplatetothedomainfilebutifw