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(LREC) – COLING – ACL 2018
UdoHahn
Statistics
LREC2018 COLING2018 ACL2018
Submissions 1102 1.017(129withdrawn!) 1.621(L:1045|S:576)
Acceptedpapers 718 331 L:256/1018|S:125/526=381
Acceptanceratio 65.0%(NC:„inclusive“) 37,3% 24.7%
#reviewers 1.273 1.029 1.610
#participants ~1200 ~700 ~1500
Ranking B A–[àB+] A+
Ourcontributions Buechel&Hahn(main) Buechel&Hahn(main) ECONLPWorkshop(org)
Lohretal.(main) Hellrichetal.(demo) ECONLPWorkshop:Buecheletal.(short)
COLING 2018 – Santa Fé, New Mexico, USA 84.000Einwohner,2200mHöhe(!)
COLING 2018 - Tutorials
• NLPforConversations:Sentiment,Summarization,andGroupDynamics–GabrielMurray,GiuseppeCareniniandShafiqJoty• DeepBayesianLearningandUnderstanding–Jen-TzungChien• DeepLearningforDialogueSystems–Yun-NungChen,AsliCelikyilmazandDilekHakkani-Tur• PracticalParsingforDownstreamApplications–DanielDakotaandSandraKübler• FrameSemanticsacrossLanguages:TowardsaMultilingualFrameNet–CollinBaker,MichaelEllsworth,MiriamPetruckandSwabhaSwayamdipta• Data-DrivenTextSimplification–SanjaŠtajnerandHoracioSaggion
COLING 2018 – Major Themes
• Sentiment+EmotionAnalysis(I-IV),Humor/Sarcasm/Rumor(I-II)• Embeddings(I-III),DistributionalSemantics• MachineLearning(I-II)• Parsing(I-II),Generation(I-II)• Coreference,Discourse(I-II)• Low-ResourceLanguages• Ethics• HistoricalLinguistics(L.change)
• NamedEntityRecognition(I-II),InformationExtraction(I-II)• DialogSystems(I-II),QA(I-II)• MachineTranslation(I-III)• Summarization• Multimodality
COLING 2018 - Keynotes • JamesPustejovsky:VisualizingMeaning–ModelingCommunicationthroughMultimodalSimulations• Human-computer/humanrobotinteraction• Contextual/situatedgrounding(niceVoxWorldvideo,blocksworld:„graspacup“)• VoxML:modelinglanguageforconstructingcontextualized3Dvirtualrealization• Multiplelogicscombined
• HannahRohde:Whyareyoutellingmethis?Relevanceandinformativityinlanguageprocessing• Inferenceofrelevanceandprocessingof(un)informativeinformation(fromahuman/cognitive)perspective
• Discoursecoherencerelations:implicit(inferable)vs.explicitrelations–psycholinguisticexperiments(insertionofappropriateadverbialsintoblanktext,givendiscoursecontext);self-pacedreadingtimeexperimentstodetermineinferenceforrelevanceandredundancyfor(lackof)informativeness,e.g.,colorrelativetopiecesofclothingandfruits
• Min-YenKan:Research–fastandslow• Accelerationofscience:arXiv,GitHub/JupyterNotebook,SharedTasks• Researchfast:worksbutwedon’tknowwhy;researchfast+slow:itworksandwethinkweknowwhyandwe’lladvocateforit
• FabiolaHenri:Investigatingadiscriminativeapproachtocreolization• Standardclaim:creolelanguage(simplificationsofEuropeanlanguages)donothavemorphology–yet,theyhaveone(thoughsimpler)[25/47slidesshown]
COLING 2018 – Interesting Stuff & Observations
• AndreyKutuzov,LiljaØvrelid,TerrenceSzymanski,ErikVelldal:Diachronicwordembeddingsandsemanticshifts:asurvey• AndrewMooreandPaulRayson:BringingreplicationandreproductiontogetherwithgeneralisabilityinNLP:ThreereproductionstudiesforTargetDependentSentimentAnalysis• Hugedifferencesbetweenoriginalpublicationandreplicationstudieswhendomainsandgenresarechanged
• INCEptionannotationtool(TUDarmstadt)
COLING 2018 – Interesting Stuff & Observations
• ZiedBouraoui,ShoaibJameel,StevenSchockaert:RelationInductioninWordEmbeddingsRevisited• Cosine-basedwordsimilaritygivescounter-intuitiveresults:(horses,horse)vs.(berlin,germany)• SolutioncombinesBayesianlinearregressionsimilaritybetweensandtandhighprobabilityofs-t,sandtbeinginasemanticrelation
• AlanAkbik,DuncanBlythe,RolandVollgraf:ContextualStringEmbeddingsforSequenceLabeling• ZalandoWEframework(flair)increaseseffectiveness(WEandsystemarebeingdistributed)• Problems:Wordambiguitysolvedbycontextualizingembeddings;fixedvocabulary->meaningfulembeddingsforanytypeofwordàproposecontextualstringembeddings(character-based)!
• Contextualizationisrealizedbycombiningembeddingsfromleft2rightandright2left:outperformsword-levelembeddings;butconcatenatestringembeddingswithstandardwordembeddings!VerygoodresultsforNEtasks(English,German)–movefromword-leveltochar-levellanguagemodelling
• JieYang,ShuailongLiang,YueZhang:DesignChallengesandMisconceptionsinNeuralSequenceLabeling• Nicesurveyofproblemsofreproducibilityofsequencelabelingtasks(POS,NER,chunking)àcommonframeworkforcomparison(hyperparameters,datasets,etc.):NCRF++(GitHub)
COLING 2018 – Workshop on Trolling, Aggression and Cyberbullying
• KeynoteTalk–RadaMihalcea:WhatHidesBehindOnlineIdentity(andAnonymity)• Cyber-selfoftendifferentfromrealself:live24-7:addictivebehavior,fearofmissingout,perfect(eatingdisorders,lowself-esteem),partofunnaturallylargegroups(isolation,negativerelations),differentself(bullying,prediation)àidentitydeception(pretendingtobesomeoneyouarenot)
• Linguisticstyledifferentwhenengaginginidentitydeception:age,binarygender• Newidentitydeceptiondataset(AMT):openendedquestions(600individuals)relatedtoportrayoneof4fakeidentities(18/65aged:m/f)usingrealandfakeidentity(imagineyouare18/65:f/m)–canwerecognizeinstancesofidentitydeception?(e.g.,femaleclaimingtobemale):result:85.8(usingSVM)onallidentities
• Findinggenderdeceivers(predictinggenderdeception):86%menlying(beingfemales)arehardertoidentifythanwomenpretendingtobemen
• Findingagedeceivers(predictingagedeception):82.7%(oldpeoplearebettertodeceivethanyoungones)
• Howtospotaliar(relatedtogenderandage)?LIWC,n-grams,wordembeddings• Lyingaboutothers(fakenews)notaboutthemselves:celebrityandpoliticaldomains(ngrams,punctuation,LIWC,readability,syntaxasfeatures)–ngrams,readabilitybutparticularlyLIWCareperformingwell
• Humanvs.automaticfakenewsdetection:74-76%(system,70-80%(humans)• Morethanjustlanguage…videoclipsfrom121realtrialsàgestureannotation
COLING 2018 – Workshop on Trolling, Aggression and Cyberbullying • RiTUAL-UHatTRAC2018SharedTask:AggressionIdentification:NiloofarSafiSamghabadi,DeepthiMave,SudiptaKarandThamarSolorio• 130registered,30submitted,25papers• Annotateddatasetof2languages:Hindi(1200tweets)&English(1269tweets):3classes–overlyaggressive,covertlyaggressive,non-aggressive,postsfromFacebookpages(politicalgroupsandnews)
• Methods:LSTM,RNN(2ndbestusedSVMwithcharandword-leveln-grams)–dangerofoverfittingwhensurprisedatasethadtobeanalyzed(particularlyforHindi)
• Summary:NNapproachesnotnecessarilybetterthanstandardML;dataaugmentation(translation,pseudolabelling)usingotherresourcesiseffective;carefulpreprocessingpaysoff
• Semeval2019:task6–HateEvalandOffensEval(startednow),trainingatendof2018,testingbeginof2019
COLING 2018 – LaTech Workshop
• KeynoteTalkbyTedUnderwood:MeasurementandHumanPerspective• Perspectivalknowledge–crucialforqualitativevs.quantitativeapproachesto(digital)humanities
• Bayesianapproachescantakethisintoaccount
COLING 2018 – German Impact • TUDarmstadt[6]
• IliaKuznetsovandIrynaGurevych-FromTexttoLexicon:BridgingtheGapbetweenWordEmbeddingsandLexicalResources• SteffenEger,JohannesDaxenberger,ChristianStabandIrynaGurevych–Cross-lingualArgumentationMining:MachineTranslation(andabitofProjection)isAll
YouNeed!• Erik-LânDoDinh,SteffenEgerandIrynaGurevych–KillingFourBirdswithTwoStones:Multi-TaskLearningforNon-LiteralLanguageDetection• AndreasHanselowski,AvineshPVS,BenjaminSchiller,FelixCaspelherr,DebanjanChaudhuri,ChristianM.MeyerandIrynaGurevych–ARetrospectiveAnalysisof
theFakeNewsChallengeStance-DetectionTask• LisaBeinborn,TeresaBotschenandIrynaGurevych-MultimodalGroundingforLanguageProcessing• DaniilSorokinandIrynaGurevych–ModelingSemanticswithGatedGraphNeuralNetworksforKnowledgeBaseQuestionAnswering• Jan-ChristophKlie,MichaelBugert,BetoBoullosa,RichardEckartdeCastilho&IrynaGurevych–TheINCEpTIONPlatform:Machine-AssistedandKnowledge-
OrientedInteractiveAnnotation[demo]
• UStuttgart[4]• JeremyBarnes,RomanKlingerandSabineSchulteimWalde–ProjectingEmbeddingsforDomainAdaption:JointModelingofSentimentAnalysisinDiverse
Domains• EvgenyKimandRomanKlinger–WhoFeelsWhatandWhy?AnnotationofaLiteratureCorpuswithSemanticRolesofEmotions• LauraAnaMariaBostanandRomanKlinger-AnAnalysisofAnnotatedCorporaforEmotionClassificationinText• InaRoesiger,ArndtRiesterandJonasKuhn–Bridgingresolution:Taskdefinition,corpusresourcesandrule-basedexperiments• MarkusGärtner,SvenMayer,ValentinSchwind,EricHämmerle,EmineTurcan,FlorinRheinwald,GustavMurawski,LarsLischke&JonasKuhn–NLATool:an
ApplicationforEnhancedDeepTextUnderstanding[demo]
• LeibnizScienceCampus,Heidelberg/Mannheim[2,5]• InesRehbeinandJosefRuppenhofer–Sprucingupthetrees–Errordetectionintreebanks• JosefRuppenhofer,MichaelWiegand,RebeccaWilmandKatjaMarkert–Distinguishingaffixoidformationsfromcompounds• MarcSchulder,MichaelWiegandandJosefRuppenhofer–AutomaticallyCreatingaLexiconofVerbalPolarityShifters:Mono-andCrosslingualMethodsfor
German
COLING 2018 – German Impact • UTübingen[2]
• ZarahWeißandDetmarMeurers–ModelingtheReadabilityofGermanTargetingAdultsandChildren:Anempiricallybroadanalysisanditscross-corpusvalidation
• JohannesDellert–CombiningInformation-WeightedSequenceAlignmentandSoundCorrespondenceModelsforImprovedCognateDetection
• UHamburg[2]• SeidMuhieYimamandChrisBiemann–Par4Sim:AdaptiveParaphrasingforTextSimplification• ArneKöhn–IncrementalNaturalLanguageProcessing:Challenges,Strategies,andEvaluation
• UPassau[2]• MatthiasCetto,ChristinaNiklaus,AndréFreitasandSiegfriedHandschuh–Graphene:Semantically-LinkedPropositionsinOpen
InformationExtraction• ChristinaNiklaus,MatthiasCetto,AndréFreitasandSiegfriedHandschuh–ASurveyonOpenInformationExtraction• MatthiasCetto,ChristinaNiklaus,AndréFreitas&SiegfriedHandschuh–Graphene:aContext-PreservingOpenInformationExtraction
System[demo]
• BUWeimar[2]• MartinPotthast,TimGollub,KristofKomlossy,SebastianSchuster,MattiWiegmann,ErikaPatriciaGarcesFernandez,MatthiasHagen
andBennoStein–CrowdsourcingaLargeCorpusofClickbaitonTwitter• HenningWachsmuth,ManfredStede,RoxanneElBaff,KhalidAlKhatib,MariaSkeppstedtandBennoStein–ArgumentationSynthesis
followingRhetoricalStrategies
• FSUJena[1,5]• SvenBuechelandUdoHahn–EmotionRepresentationMappingforAutomaticLexiconConstruction(Mostly)PerformsonHumanLevel• JohannesHellrich,SvenBuechel&UdoHahn–JeSemE:InterleavingSemanticsandEmotionsinaWebServicefortheExplorationof
LanguageChangePhenomena[demo]
COLING 2018 – German Impact • UDuisburg-Essen
• SebastianDungs,AhmetAker,NorbertFuhrandKalinaBontcheva–CanRumourStanceAlonePredictVeracity?
• UMünchen• WenpengYin,YadollahYaghoobzadehandHinrichSchütze–RecurrentOne-HopPredictionsforReasoningoverKnowledgeGraphs
• UdesSaarlandes,SpokenLanguageSystems• MarcSchulder,MichaelWiegandandJosefRuppenhofer–AutomaticallyCreatingaLexiconofVerbalPolarityShifters:Mono-andCrosslingualMethodsforGerman
• UniversityofMannheim• SanjaˇStajnerandIoanaHulpus–AutomaticAssessmentofConceptualTextComplexityUsingKnowledgeGraphs
• TUDresden• KilianGebhardt–Genericrefinementofexpressivegrammarformalismswithanapplicationtodiscontinuousconstituentparsing
• FUBerlin,DepartmentofLiteraryStudies• TimoBaumann,HusseinHusseinandBurkhardMeyer-Sickendiek–StyleDetectionforFreeVersePoetryfromTextandSpeech
• [Fach-]HochschuleHannover• JeanCharbonnierandChristianWartena–UsingWordEmbeddingsforUnsupervisedAcronymDisambiguation
• ZalandoResearch,Berlin• AlanAkbik,DuncanBlytheandRolandVollgraf–ContextualStringEmbeddingsforSequenceLabeling
• UDÜsseldorf• AndreasvanCranenburgh–ActiveDOP:anActiveLearningConstituencyTreebankAnnotationTool[demo]
• HITSHeidelberg• Mark-ChristophMüller|MichaelStrube–Transparent,Efficient,andRobustWordEmbeddingAccesswithWOMBAT[demo]
• KITKarlsruhe• FlorianDessloch,Thanh-LeHa,MarkusMüller,JanNiehues,ThaiSonNguyen,Ngoc-QuanPham,ElizabethSalesky,MatthiasSperber,SebastianStüker,ThomasZenkel&Alexander
Waibel–KITLectureTranslator:MultilingualSpeechTranslationwithOne-ShotLearning[demo]
• JvGUFrankfurt/Main• DanielBaumartz|TolgaUslu|AlexanderMehler–LTV:LabeledTopicVector[demo]
ACL 2018 – Melbourne, Australia 5MEinwohner(LargerM.,31mHöhe)
ACL 2018
• Hugegrowthinmembership(3000++)• Asian&PacificChapteroftheACLfounded(AACL)• CallforpapersforaspecialissueofComputationalLinguisticson“Computationalapproachesinhistoricallinguisticsafterthequantitativeturn”–typological(phylogenetic)approachessoughtafter(MPI)
ACL 2018 - Tutorials
• 100ThingsYouAlwaysWantedtoKnowaboutSemantics&PragmaticsButWereAfraidtoAsk–EmilyM.Bender
• NeuralApproachestoConversationalAI–JianfengGao,MichelGalley,andLihongLi• VariationalInferenceandDeepGenerativeModels–WilkerAzizandPhilipSchulz• ConnectingLanguageandVisiontoActions–PeterAnderson,AbhishekDas,andQiWu
• BeyondMultiwordExpressions:ProcessingIdiomsandMetaphors–ValiaKordoni• NeuralSemanticParsing–LukeZettlemoyer,MattGardner,PradeepDasigi,SrinivasanIyer,andAlaneSuhr
• DeepReinforcementLearningforNLP–WilliamYangWang,JiweiLi,andXiaodongHe• Multi-lingualEntityDiscoveryandLinking–AvirupSil,HengJi,DanRoth,andSilviu-PetruCucerzan
ACL 2018 – Major Themes
• MachineLearning(I-III)• Semantics(I-II),WordSemantics(I-II)• Parsing(I-II),SemanticParsing(I-III),Generation(I-II)• Discourse(I-II)• Inference&Reasoning• Resources,Annotation• Language&DocumentModels
• MachineTranslation(I-II),Multilinguality(I-II)• InformationExtraction(I-III),TextMining,ArgumentMining• QuestionAnswering(I-III),DialogSystems&Discourse(I-III)• Sentiment(I-II),SocialMedia• Summarization(I-II)• Evaluation• Vision• Multimodality• InformationRetrieval• Psycholinguistics&CognitiveModeling
ACL 2018 - Keynotes • AntonvandenHengel:DeepNeuralNetwork,andsomethingsthey’renotverygoodat• 110MLpersons@AustralianInstituteforMachineLearning• Vision:benchmarkdrivenDL–whatifproblem,dataorinformationchanges• VisualQuestionAnswering(VQA)–givenpictures,askquestionsabouttheircontents,getanswers(trainingdata:images,questions,answers)requiresexplicitlycodedknowledge(e.g.,DBPedia)
• Solvingthelearningproblem:NeuralTuringMachine(computesreasonsforanswers)bycouplingimagerecognitionandNL–attentionasreasoning(attention=associativechaining?)–nofixedontology(VisualQuestionAnswering-CVVP17)
• LifetimeAchievementAward:MarkSteedman• HistoryofCombinatoryCategorialGrammar(CCG)–whyislanguagecombinatory?–fromLambdacalculustostatisticalCCG;CCGintheAgeofDL
• Semanticsmattersmore!whichsemanticrepresentation–unsolvedproblem?
ACL 2018 - Keynotes • CarolynPensteinRosé:WhoistheBridgeBetweentheWhatandtheHow?• Distinguishthewhatoflanguage,namelyitspropositionalcontent,andthehowoflanguage,oritsform,style,orframing.
• Whatbridgesbetweentheserealmsaresocialprocessesthatmotivatethelinguisticchoicesthatresultinspecificrealizationsofpropositionalcontentsituatedwithinsocialinteractions,designedtoachievesocialgoals.
• thistalkprobesintoaspecificqualityofdiscussionreferredtoasTransactivity,theextenttowhichacontributionarticulatesthereasoningofthespeaker,thatofaninterlocutor,andtherelationbetweenthem(associatedwithsolidarity,influence,expertisetransfer,andlearning)
ACL 2018 – Interesting Stuff & Observations • Significancetesting&NLP
• TheHitchhiker’sGuidetoTestingStatisticalSignificanceinNaturalLanguageProcessing:Dror,Baumer,Shlomov,andReichart• Only1/5ofNLPpapersusecorrectstatisticaltesting–surveyoftestingproblems• Decisionprocedureforselectionfpropertests:If(distributionisknown):selectparametrictest;IF
datasetissmall(..)…• ReplicabilityAnalysisforNaturalLanguageProcessing:TestingSignificancewithMultipleDatasets:Dror,Baumer,Bogomolov,andReichart[TACL]• LookattypeI/IIerrorsandoveralltypeerror• Ratherthanshowinghugetablesbetterspecifyk(FisherorBonferroni)andgivep-valuesforthesek
cases
• Smalltohugedatasetgeneralizations• Predictingaccuracyonlargedatasetsfromsmallerpilotdata:Johnson,Anderson,Dras,andSteedman
• Moredata=betteraccuracy,higherqualityproducesbetteraccuracy,buthowmuchdataareneeded(engineerquestion)àstatisticalpoweranalysis(Cohen,1992):howlargemustadatasetbetoguaranteestatisticalsignificance
• Idea:extrapolateperformancefromsmallpilotdatatopredictperformanceonmuchlargerdata–provide9extrapolationmethods(Powerlaw,inversesquare-root,biasedpowerlaw,etc.)on8textcorporausingFastTextclassifier;hyper-parameterestimationforeachtestconfiguration!
ACL 2018 – Interesting Stuff & Observations • Scalabilityofnamedentitytypes
• 10ktypes• LearningNEtypeshierarchies• Murty,Verga,Vilnis,Radovanovic,andMcCallum:HierarchicalLossesandNewResourcesforFine-grainedEntityTypingandLinking
• Choi,Levy,Choi,andZettlemoyer:Ultra-FineEntityTyping[medicalappl.]• Subjectiveembeddings
• SearchingfortheX-Factor:Exploringcorpussubjectivityforwordembeddings:Tkachenko,Chia,andLauw
• Wikipedia(neutral,nobias)vs.Amazonreviews(subjective,opinionated)àobjectivevs.subjectiveembeddings;
• 3classificationstasks:sentiment,subjectivityandtopicclassification–similarperformanceofobjective/subjectiveembeddingsonobjectivetasksbutsubjectiveembeddingsbetterforsubjectivegenresàmoreinformationinsubjectiveembeddingsàsentimentvectors(SentiVec)=Word2Vec+LexicalResource
ACL 2018 – Interesting Stuff & Observations • Readingcomprehension(machinereading)• TheNarrativeQAReadingComprehensionChallenge:Koˇciský,Schwarz,Blunsom,Dyer,Hermann,Melis,andGrefenstette
• Narrative–constructanswersthatcannotbereadofffromthetext(requiresreasoningandcontext),takenfrombooksandmoviescripts
• Why,how,listquestionsanswered
• Languageunderstandinginthelarge• Whodunnit?CrimeDramaasaCaseforNaturalLanguageUnderstanding:Frermann,Cohen,andLapata
• Whodunnit–CSI(CrimeSceneInvestigation)ranfor15seasons(337episodesàlotsofdata),eachepisode40-64minutes.Video+audio+textualdata
• WhodunitphrasedasasequencelabelingproblemàLSTMdetective
ACL 2018 – BioNLP Workshop • 55participants• Newresources:
• AllenNLP@github(AllenAI)• CLEW–ClinicalLanguageEngineeringWorkbench(JonGriffith)@github
• Sub-wordinformationinpre-trainedbiomedicalwordrepresentations:evaluationandhyper-parameteroptimization:DieterGalea,IvanLaponogov,andKirillVeselkov• Word2vecvs.fastTextcomparison;optimizinghyper-parameterscangetperformanceimprovementscomparabletolatestarchitectures–lookatcharacters!
• InvitedPresentation:“ACorpuswithMulti-LevelAnnotationsofPatients,InterventionsandOutcomestoSupportLanguageProcessingforMedicalLiterature”–BenNye• Supportforevidence-basedmedicine,improveaccessibilityofmedicalliterature:5kabstractsfromPubMed,annotationbycrowdworkers(filteringveryimportant!);questionsdecomposedintoPICOcomponents(Participant/Problem,Intervention,Comparator,Outcomes)àincreasinglymoredetailedinfoisannotated
ACL 2018 – EcoNLP Workshop • ~40participants• Adobe,Zalando,Frenchinvestmentcompany• CausalityAnalysisofTwitterSentimentsandStockMarketReturns–NargesTabari,PiyushaBiswas,BhanuPraneeth,ArminSeyeditabari,MirsadHadzikadic,andWlodekZadrozny• CausalityanalysisofTwitterbasedonGrangercausality• Crowdworkersjudgesentimentoftweets(-2to+2,neg/pos)relatedtocompaniesà2000negs/8000postweets,9000neutrals
• usedpos/negdictionary(Loughran)• RandomForestandSVMclassifiers:80%F• Testwhethersenti(score)causesstockmarketreturnorstockreturncausessentiment(score)
ACL 2018 – German Impact • Co-Chair:IrynaGurevych,TUDarmstadt,Germany• UdesSaarlandes(Cl,MPI)
• JonasGroschwitz,MatthiasLindemann,MeaghanFowlie,MarkJohnson&AlexanderKoller–AMRdependencyparsingwithatypedsemanticalgebra[L]
• DominicSeyler,TatianaDembelova,LucianoDelCorro,JohannesHoffart&GerhardWeikum–AStudyoftheImportanceofExternalKnowledgeintheNamedEntityRecognitionTask[S]
• StefanGrünewald,SophieHenning&AlexanderKoller–GeneralizedchartconstraintsforefficientPCFGandTAGparsing[S]
• PrabalAgarwal,JannikStrötgen,LucianoDelCorro,JohannesHoffart&GerhardWeikum–diaNED:Time-AwareNamedEntityDisambiguationforDiachronicCorpora[S]
• LMUMünchen• NinaPoerner,HinrichSchütze&BenjaminRoth–Evaluatingneuralnetworkexplanationmethodsusinghybriddocuments
andmorphosyntacticagreement[L]• ViktorHangya,FabienneBraune,AlexanderFraser&HinrichSchütze–TwoMethodsforDomainAdaptationofBilingual
Tasks:DelightfullySimpleandBroadlyApplicable[L]• PhilippDufter,MengjieZhao,MartinSchmitt,AlexanderFraser&HinrichSchütze–EmbeddingLearningThrough
MultilingualConceptInduction[L]• WenpengYin,DanRoth&HinrichSchütze–End-TaskOrientedTextualEntailmentviaDeepExplorationsofInter-Sentence
Interactions[S]• UHeidelberg
• TodorMihaylov&AnetteFrank–KnowledgeableReader:EnhancingCloze-StyleReadingComprehensionwithExternalCommonsenseKnowledge[L]
• JuliaKreutzer,JoshuaUyheng&StefanRiezler–ReliabilityandLearnabilityofHumanBanditFeedbackforSequence-to-SequenceReinforcementLearning[L]
• CarolinLawrence&StefanRiezler–ImprovingaNeuralSemanticParserbyCounterfactualLearningfromHumanBanditFeedback[L]
ACL 2018 – German Impact • UStuttgart
• JeremyBarnes|RomanKlinger|SabineSchulteimWalde–BilingualSentimentEmbeddings:JointProjectionofSentimentAcrossLanguages[L]
• MartinRiedl&SebastianPadó–ANamedEntityRecognitionShootoutforGerman[S]• BUWeimar
• HenningWachsmuth,ShahbazSyed&BennoStein–RetrievaloftheBestCounterargumentwithoutPriorTopicKnowledge[L]
• KhalidAlKhatib,HenningWachsmuth,KevinLang,JakobHerpel,MatthiasHagen&BennoStein–ModelingDeliberativeArgumentationStrategiesonWikipedia[L]
• RWTHAachen• WeiyueWang,DeruiZhu,TamerAlkhouli,ZixuanGan&HermannNey–NeuralHiddenMarkovModelforMachine
Translation[S]• AlbertZeyer,TamerAlkhouli&HermannNey–RETURNNasaGenericFlexibleNeuralToolkitwithApplicationto
TranslationandSpeechRecognition[demo]• UMannheim+UHamburg
• DmitryUstalov,AlexanderPanchenko,AndreyKutuzov,ChrisBiemann&SimonePaoloPonzetto–UnsupervisedSemanticFrameInductionusingTriclustering[S]
• FacebookResearch• HolgerSchwenk–FilteringandMiningParallelDatainaJointMultilingualSpace[S]
• UBielefeld• MatthiasHartung,HendrikterHorst,FrankGrimm,TimDiekmann,RomanKlinger&PhilippCimiano–SANTO:AWeb-
basedAnnotationToolforOntology-drivenSlotFilling[demo]
2018 Germany‘s Top Performers ACL2018 COLING2018 Σ
Ud.Saarlandes 4 1 5
LMUMünchen 3,5 1 4,5
UStuttgart 2 4,5 6,5
TUDarmstadt – 6,5 6,5
BUWeimar 2 2 4
UHeidelberg 3 – 3
RWTHAachen 1,5 – 1,5
UHamburg 0,5 2 2,5
LeibnizCampusMA/HD – 2,5 2,5
UPassau – 2,5 2,5
UTübingen – 2 2
UMannheim 0,5 1 1,5
FSUJena – 1,5 1,5
ACL2018 COLING2018 Σ
UBielefeld 1 – 1
FacebookRes. 1 – 1
UDuisburg-Essen – 1 1
TUDresden – 1 1
FUBerlin – 1 1
FHHannover – 1 1
Zalando – 1 1
UDüsseldorf – 0,5 0,5
HITSHeidelberg – 0,5 0,5
KITKarlsruhe – 0,5 0,5
JXvGFrankfurt/Main – 0,5 0,5
Over-all Summary
• Languageunderstandingisbackonthescene(aftermorethan30years!)–thisincludesreasoningtasks,e.g.,formachinereading,vision-basedQA• NeuralNetworks• Variousmodelsareinvestigated(GAN,attention-basedàcontext)• Incorporatingnon-textialresources(lexicons/terminologies,knowledgegraphs)• Transferlearning,multi-tasklearning• interpretability
• Methodologyunderlyingstatisticalanalysisiscriticiallyreflected