Artificial Intelligence and Natural Language: 11th Conference, AINL 2022, Saint Petersburg, Russia, April 14–15, 2022, Revised Selected Papers

117 Pages·2023·6.359 MB·

by  Valentin Malykh, Andrey Filchenkov

Valentin Malykh
Andrey Filchenkov (Eds.)
Communications in Computer and Information Science 1731
Artificial Intelligence
and Natural Language
11th Conference, AINL 2022
Saint Petersburg, Russia, April 14–15, 2022
Revised Selected Papers
Communications
in Computer and Information Science 1731
EditorialBoardMembers
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PolytechnicInstituteofSetúbal,Setúbal,Portugal
AshishGhosh
IndianStatisticalInstitute,Kolkata,India
RaquelOliveiraPrates
FederalUniversityofMinasGerais(UFMG),BeloHorizonte,Brazil
LizhuZhou
TsinghuaUniversity,Beijing,China
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/7899
·
Valentin Malykh Andrey Filchenkov (Eds.)
Artificial Intelligence
and Natural Language
11th Conference, AINL 2022
Saint Petersburg, Russia, April 14–15, 2022
Revised Selected Papers
Editors
ValentinMalykh AndreyFilchenkov
HuaweiNoah’sArkLab ITMOUniversity
SaintPetersburg,Russia SaintPetersburg,Russia
ISSN 1865-0929 ISSN 1865-0937 (electronic)
CommunicationsinComputerandInformationScience
ISBN 978-3-031-23371-5 ISBN 978-3-031-23372-2 (eBook)
https://doi.org/10.1007/978-3-031-23372-2
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Preface
The10thConferenceonArtificialIntelligenceandNaturalLanguageConference(AINL
2022),heldduringApril14–15,2022,inSaintPetersburg,Russia,wasorganizedbythe
NLPSeminar(Russia)andITMOUniversity(Russia).Itaimedtobringtogetherexperts
intheareasoftextmining,speechtechnologies,dialoguesystems,informationretrieval,
machine learning, artificial intelligence, and robotics to create a platform for sharing
experiences,extendingcontacts,andsearchingforpossiblecollaboration.Since2012,
the AINL conference series has established a strong foothold in the Baltic region and
Russiawithastrongfocusonresearchandbuildingresourcesforlocallanguages.This
yearourconferencealsohostedtheRussian-EnglishBiomedicalMachineTranslation
Challenge,aimingtoimproveindustryandacademiacollaboration.
Wereceived20researchpapers.Thereviewingprocesswaschallenging.Wewere
abletoacceptonlyeightlongpapersresultingina40%acceptancerate.Weaccepted
also one long paper from the organizers of the Russian-English Biomedical Machine
TranslationChallengeandoneshortpaperfromitswinner.Intotal,27researchersfrom
differentdomainsandareaswereengagedinthedouble-blindreviewprocess.Eachpaper
receivedatleastthreereviewsandalmosthalfofthepapersreceivedfourreviews.The
papers describing theshared taskandthewinning systemareincluded inthisvolume
after a single blind review by the volume editors. The selected papers were presented
attheconference,coveringawiderangeoftopicsincludingsentimentanalysis,lexical
resources,documentretrieval,aspectextraction,dialogmodeling,textsummarization,
textgeneration,andexplainableartificialintelligence.Mostofthepresentedpaperswere
devotedtoprocessingtextualdata.Inaddition,theconferenceprogramincludedindustry
talksfromleadingcompaniesintheareaoftextprocessing.
We are grateful to everybody who submitted papers, who made wonderful talks,
andwhoparticipatedwithoutpublication.WeareindebtedtoourProgramCommittee
membersfortheirinsightfulreviews.Wealsowishtoexpressourdeepestgratitudetoour
organizationteam,AnnaandAlexanderBugrovsky,fortheirinvaluableeffortsduring
theconference.
October2022 ValentinMalykh
AndreyFilchenkov
Organization
GeneralChair
ValentinMalykh HuaweiNoah’sArkLab,Russia
LocalChair
AndreyFilchenkov ITMOUniversity,Russia
ProgramCommittee
EliorVila UniversityofElbasan“AleksandërXhuvani”,
Albania
BorisDobrov MoscowStateUniversity,Russia
VladimirIvanov InnopolisUniversity,Russia
VladimirPleshko RCOLtd.,Russia
ElizavetaKuzmenko UniversityofTrento,Italy
GeorgeMikros NationalandKapodistrianUniversityofAthens,
Greece
NataliaLoukachevitch MoscowStateUniversity,Russia
EvgenyKotelnikov VyatkaStateUniversity,Russia
DmitryChalyy YaroslavlStateUniversity,Russia
AnastasiaShimorina UniversityofLorraine/Loria,France
VeraEvdokimova St.PetersburgStateUniversity,Russia
MikhailAlexandrov AutonomousUniversityofBarcelona,Spain
DenisKirjanov HigherSchoolofEconomics,Russia
ErindBedalli UniversityofElbasan“AleksandërXhuvani”,
Albania
DmitryUstalov Yandex,Russia
TatianaShavrina HigherSchoolofEconomics,Russia
IrinaTemnikova QatarComputingResearchInstitute,Qatar
AlexeyMalafeev HigherSchoolofEconomics,Russia
ElenaBolshakova MoscowStateUniversity,Russia
MaximBuzdalov ITMOUniversity,Russia
AmirBakarov HigherSchoolofEconomics,Russia
MariaTikhonova HigherSchoolofEconomics,Russia
SiddharthaBhattacharyya RCCInstituteofInformationTechnology,India
IlseyarAlimova KazanFederalUniversity,Russia
viii Organization
DmitryKravchenko Ben-GurionUniversityoftheNegev,Israel
NikolaiZolotykh UniversityofNizhniNovgorod,Russia
ArturAzarov St.PetersburgInstituteforInformaticsand
Automation,Russia
Contents
InferringImageBackgroundfromTextDescription ......................... 1
DmitryChizhikov, ValeriaEfimova, ViacheslavShalamov,
andAndreyFilchenkov
TopicalExtractiveSummarization ....................................... 14
KristinaZheltova,AnastasiaIanina,andValentinMalykh
TheSemanticShiftsoftheTopicalStructureintheCorpusofLentach
NewsPosts ........................................................... 27
IvanD.Mamaev,AlenaA.Mamaeva,andDariaA.Axenova
DevelopmentofFolkloreMotifClassifierUsingLimitedData ............... 40
MariaMatveevaandValentinMalykh
Morphological and Emotional Features of the Speech in Children
with Typical Development, Autism Spectrum Disorders and Down
Syndrome ............................................................ 49
OlesiaMakhnytkina,OlgaFrolova,andElenaLyakso
WikiMulti:ACorpusforCross-LingualSummarization ..................... 60
PavelTikhonovandValentinMalykh
RethinkingCrowdSourcingforSemanticSimilarity ........................ 70
ShaulSolomon,AdamCohn,HernanRosenblum,CheziHershkovitz,
andIvanP.Yamshchikov
InterplayofVisualandAcousticCuesofIronyPerception:ACaseStudy
ofActor’sSpeech ..................................................... 82
UlianaKochetkova,VeraEvdokimova,PavelSkrelin,RadaGerman,
andDariaNovoselova
Findings of Biomedical Russian to English Machine Translation
Competition .......................................................... 95
ElizavetaEzhergina, MariiaFedorova, ValentinMalykh,
andDariaPetrova
TranslationofMedicalTextswithEnsemblingandKnowledgeDistillation .... 102
DaniilLukichev
AuthorIndex ......................................................... 109
Inferring Image Background from Text
Description
B
Dmitry Chizhikov( ), Valeria Efimova , Viacheslav Shalamov ,
and Andrey Filchenkov
ITMO University, 49 Kronverksky Pr., St. Petersburg 197101, Russia
[email protected], {vefimova,shalamov,afilchenkov}@itmo.ru
Abstract. Overthepastfiveyears,text-to-imagesynthesishasachieved
impressive results. However, the generated images are still produced in
low resolution and suffer from indeterminacy. In order to improve this
situation,wesplittheimagesynthesistaskintoseparatepartswithpre-
dicting the layout of the scene as one of these tasks. We believe that
it could be started with the background, which is usually determined
by the location of the scene. Given a text, we can try to infer the loca-
tion described or implied by it. In this paper, we propose two methods
forobtainingthisinformationfromagiventext.Thefirstmethodcalled
LET(LocationExtractionTransformer)isintendedtoextractthewords
from a text, in which the scene location is directly mentioned. The sec-
ond method we propose we call LIT (Location Inference Transformer).
Itisintendedtoinferthescenelocationwhichisimpliedbythetext,but
notmentioneddirectly.Wehavealsocollectedtwodatasetstotrainand
test the corresponding models. These datasets are publicly available at
kaggle.com. We have compared the performance of our algorithm with
severalexistingapproacheswhichmightbeusedforextractingtheloca-
tion information from the text. The results obtained by both LET and
LIT have occurred to be more superior to other algorithms.
Keywords: Text-to-image generation · Natural language processing ·
·
Transformers Transfer learning
1 Introduction
Automatic generation of realistic-looking high-quality images from the text
descriptions (also known as text-to-image synthesis) is a complicated task with
a great potential for various computer vision applications, such as photo editing
and design creation. Most of the state-of-the-art text-to-image synthesis meth-
ods are based on Generative Adversarial Networks (GANs) [15] and Variational
Autoencoders(VAE)[9].Despiterecentimpressiveresults[14],producingacom-
plex scene with multiple objects based on the text description remains challeng-
ing [42]. GANs for text-to-image synthesis still suffer from a poor image qual-
ity [17]. Even state-of-the-art models cannot generate the fine-grained image
background [22,23].
(cid:2)c TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2022
V.MalykhandA.Filchenkov(Eds.):AINL2022,CCIS1731,pp.1–13,2022.
https://doi.org/10.1007/978-3-031-23372-2_1