CS378:NaturalLanguageProcessingLecture1:Introduc:on

GregDurre=

introvideo

actualcourse

Administrivia

‣ Coursewebsite(includingsyllabus):h=p://www.cs.utexas.edu/~gdurre=/courses/fa2020/cs378.shtml

‣ Piazza:linkonthecoursewebsite

‣ Myofficehours:seecoursewebsite

‣ TA:TanyaGoyal;Proctor:ShivangSingh.SeewebsiteforOHs

‣ Lecture:TuesdaysandThursdays9:30am-10:45am

‣ Allofficehoursstartnextweek,butIwillstayarounda\erthisclassifyouhaveques:ons

CourseRequirements

‣ CS429

‣ Recommended:CS331,familiaritywithprobabilityandlinearalgebra,programmingexperienceinPython

‣ Helpful:ExposuretoAIandmachinelearning(e.g.,CS342/343/363)

Enrollment

‣ Assignment0isoutnow(op:onal):

‣ Ifthisseemslikeit’llbechallengingforyou,comeandtalktome(thisissmaller-scalethantheotherassignments,whicharesmaller-scalethanthefinalproject)

‣ Ifyouarepast25onthewaitlist,youhavealowchanceofgedngintotheclass,butwehavetoseehowitprogresses

FormatandAccessibility

‣ Requiredequipment:devicetomakeZoomcallswith,somewaytodohomework

‣ LabmachinesavailableviaSSH

‣ AGPUisnotrequiredtocompletetheassignments!HavingaGPUorGCPcreditscouldbehelpfulifyouwanttopursueanindependentproject

‣ Lectureswillbuildin:mefordiscussion,in-classexercises,andques:ons.Addi:onalmaterialisavailableasvideostowatcheitherbeforeora\erlectures

‣ We’lldoplentyofdiscussiongroupsinclass.Piazzaisalsoavailabletofindteammates

What’sthegoalofNLP?‣ Beabletosolveproblemsthatrequiredeepunderstandingoftext

‣ Example:dialoguesystemsSiri,what’syourfavoritekindof

movie?

Ilikesuperheromovies!

What’scomeoutrecently?

TheAvengers

MachineTransla:on

中共中央政治局7⽉30⽇召开会议，会议分析研究当前经济形势，部署下半年经济⼯作。

ThePoli:calBureauoftheCPCCentralCommi=eeheldamee:ngonJuly30toanalyzeandstudythecurrenteconomicsitua:onandplaneconomicworkinthesecondhalfoftheyear.

People’sDaily,August10,2020

ThePoli:calBureauoftheCPCCentralCommi=ee July30 holdamee:ng

Translate

Ques:onAnsweringWhenwasAbrahamLincolnborn?

February12,1809

Name BirthdayLincoln,Abraham 2/12/1809

Washington,George 2/22/1732Adams,John 10/30/1735

Theparkhasatotaloffivevisitorcenters

five

HowmanyvisitorscentersarethereinRockyMountainNa:onalPark?

maptoBirthdayfield

NLPAnalysisPipeline

Syntac:cparses

Coreferenceresolu:on

En:tydisambigua:on

Discourseanalysis

Summarize

Extractinforma:on

Answerques:ons

Iden:fysen:ment

‣ NLPisaboutbuildingthesepieces!(largelyusingsta:s:calapproaches)

Translate

TextAnalysis Applica=onsText Annota=ons

Howdowerepresentlanguage?Labels

Sequences/tags

Trees

Text

themoviewasgood +Beyoncéhadoneofthebestvideosofall6me subjec=ve

TomCruisestarsinthenewMissionImpossiblefilmPERSON WORK_OF_ART

Ieatcakewithicing

PPNP

S

NPVP

VBZ NNflightstoMiami

λx.flight(x)∧dest(x)=Miami

Howdoweusetheserepresenta:ons?

Labels

Sequences

Trees

TextAnalysisText

‣Mainques:on:Whatrepresenta:onsdoweneedforlanguage?Whatdowewanttoknowaboutit?Whatambigui:esdoweneedtoresolve?

…

Applica=ons

Learntree-to-treemachinetransla:onmodels

end-to-endmodels

…

Whyislanguagehard?(andhowcanwehandlethat?)

LanguageisAmbiguous!

‣ HectorLevesque(2011):“Winogradschemachallenge”(nameda\erTerryWinograd,thecreatorofSHRDLU)

Thecitycouncilrefusedthedemonstratorsapermitbecausethey______violence

‣ >5datasetsinthelasttwoyearsexaminingthisproblemandcommonsensereasoning

‣ Referen:alambiguity

Thecitycouncilrefusedthedemonstratorsapermitbecausetheyadvocatedviolence

Thecitycouncilrefusedthedemonstratorsapermitbecausetheyfearedviolence

LanguageisAmbiguous!

examplecredit:DanKlein

‣ Syntac:candseman:cambigui:es:parsingneededtoresolvethese,butneedcontexttofigureoutwhichparseiscorrect

TeacherStrikesIdleKids

BanonNudeDancingonGovernor’sDesk

IraqiHeadSeeksArms

LanguageisReallyAmbiguous!

‣ Therearen’tjustoneortwopossibili:eswhichareresolvedpragma:cally

‣ Combinatoriallymanypossibili:es,manyyouwon’tevenregisterasambigui:es,butsystemss:llhavetoresolvethem

Itisreallyniceout

ilfaitvraimentbeau It’sreallyniceTheweatherisbeau:fulItisreallybeau:fuloutsideHemakestrulybeau:fulItfactactuallyhandsome

Whattechniquesdoweuse?(tocombinedata,knowledge,linguis:cs,etc.)

“AIwinter”rule-based,expertsystems

Unsup:topicmodels,grammarinduc:on

Collinsvs.Charniakparsers

Abriefhistoryof(modern)NLP

1980 1990 2000 2010 2020

earlieststatMTworkatIBM

Penntreebank

NP VP

S

Ratnaparkhitagger

NNP VBZ

Sup:SVMs,CRFs,NER,Sen:ment

Semi-sup,structuredpredic:on

Neural

Wherearewe?

‣ NLPconsistsof:analyzingandbuildingrepresenta:onsfortext,solvingproblemsinvolvingtext

‣ Theseproblemsarehardbecauselanguageisambiguous,requiresdrawingondata,knowledge,andlinguis:cstosolve

‣ Knowingwhichtechniquesuserequiresunderstandingdatasetsize,problemcomplexity,andalotoftricks!

‣ NLPencompassesallofthesethings

NLPvs.Computa:onalLinguis:cs

‣ NLP:buildsystemsthatdealwithlanguagedata

‣ CL:usecomputa:onaltoolstostudylanguage

Hamiltonetal.(2016)

OutlineoftheCourse‣ Classifica:on:linearandneural,wordrepresenta:ons(3.5weeks)‣ Textanalysis:taggingandparsing(3weeks)<=takesustothemidterm

‣ Genera:on,applica:ons:languagemodeling,machinetransla:on(3weeks)

‣ Ques:onanswering,pre-training(2weeks)

‣ CoverfundamentaltechniquesusedinNLP

‣ CovermodernNLPproblemsencounteredintheliterature:whataretheac:veresearchtopicsin2020?

‣ Understandhowtolookatlanguagedataandapproachlinguis:cphenomena

‣ Goals:

‣ Applica:onsandmiscellaneous(2.5weeks)

OutlineoftheCourse‣ Throughoutthecourse:ethicsandfairness‣ BroadertopicinMLthanjustNLP

‣ Howcanwemakesureoursystemsbenefitsociety,andeveryoneinit?

‣ Partsoflecturesdevotedtotopicsinethics,comprehensivediscussiononthelastclassday

‣ Nov3:op:onallecture

‣ Balancealgorithms,linguis:cs,data,ethics

Coursework

‣ Fiveassignments,worth45%ofgrade(A1-4:10%,A5:5%)

‣ Mixofwri:ngandimplementa:on;

‣ Assignment0isoutnow,op:onaldiagnos:c

‣ ~2weeksperassignmentexceptforA5

‣ 5“slipdays”throughoutthesemestertoturninassignments24hourslate.Otherwise,youlose15%creditperdaytheassignmentislate

‣ SubmissiononGradescope

Theseassignmentsrequireunderstandingtheconcepts,wri:ngperformantcode,andthinkingabouthowtodebugcomplexsystems.Theyarechallenging;startearly!

Thecoursestaffarenotheretodebugyourcode!Wewillhelpyouunderstandtheconceptsfromlectureandcomeupwithdebuggingstrategies

Coursework

‣ Finalproject(25%ofgrade)‣ Groupsof1or2‣ Standardproject:neuralnetworkmodelsforques:onanswering

‣ Independentprojectsarepossible:thesemustbeproposedearlier(togetyouthinkingearly)andwillbeheldtoahighstandard!

‣ Midterm(20%ofgrade),take-homeOctober14-16

‣ Similartowri=enhomeworkproblems

‣ In-classproblems(10%ofthegrade)

‣ ThesewillbedoneviaUTInstapoll.Youdon’thavetocometoclasstodothem

‣ Dropthelowest5

AcademicHonesty

‣ Youmayworkingroups,butyourfinalwriteupandcodemustbeyourown

‣ Don’tsharecodewithothers!

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