Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Information Systems Frontiers
Erschienen in: Information Systems Frontiers 1/2021

28.02.2020

Towards End-to-End Multilingual Question Answering

verfasst von: Ekaterina Loginova, Stalin Varanasi, Günter Neumann

Erschienen in: Information Systems Frontiers | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Multilingual question answering (MLQA) is a critical part of an accessible natural language interface. However, current solutions demonstrate performance far below that of monolingual systems. We believe that deep learning approaches are likely to improve performance in MLQA drastically. This work aims to discuss the current state-of-the-art and remaining challenges. We outline requirements and suggestions for practical parallel data collection and describe existing methods, benchmarks and datasets. We also demonstrate that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required. We hope that our overview will re-ignite interest in multilingual question answering, especially with regard to neural approaches.
Vorheriger Artikel TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes
Nächster Artikel An Approach to Extracting Topic-guided Views from the Sources of a Data Lake

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
4
The parameters are as follows: skip-gram, window 5, negative-sampling rate − 1/1000.
 
Literatur
Aceves-Pérez, R.M., Montes-y Gómez, M., Villaseñor-Pineda, L. (2007) Enhancing cross-language question answering by combining multiple question translations. In: International Conference on Intelligent Text Processing and Computational Linguistics, Springer, pp 485–493.
Almarwani, N., Diab, M. (2017) GW\_QA at SemEval-2017 task 3: question answer re-ranking on Arabic fora. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp 344–348.
Attia, M., Samih, Y., Elkahky, A., Kallmeyer, L. (2018) Multilingual multi-class sentiment classification using convolutional neural networks. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018), European Language Resource Association, URL http://​aclweb.​org/​anthology/​L18-1101.
Bahdanau, D., Cho, K., Bengio, Y. (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:14090473.
Banerjee, S., Chakma, K., Naskar, S. K., Das, A., Rosso, P., Bandyopadhyay, S., & Choudhury, M. (2016a). Overview of the mixed script information retrieval (msir) at fire-2016. Organization (ORG), 67, 24.
Banerjee, S., Naskar, S. K., Rosso, P., Bandyopadhyay, S. (2016b) The first cross-script code-mixed question answering corpus. In: MultiLingMine@ ECIR, pp 56–65.
Barman, U., Das, A., Wagner, J., Foster, J. (2014) Code mixing: A challenge for language identification in the language of social media. In: Proceedings of the first workshop on computational approaches to code switching, pp 13–23.
Bender, E. M. (2009) Linguistically naïve!= language independent: why nlp needs linguistic typology. In: Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous?, pp 26–32.
Boldrini, E., Ferrández, S., Izquierdo, R., Tomás, D., Vicedo, J. L. (2009) A parallel corpus labeled using open and restricted domain ontologies. In: International Conference on Intelligent Text Processing and Computational Linguistics, Springer, pp 346–356.
Bouma, G., Kloosterman, G., Mur, J., Van Noord, G., Van Der Plas, L., Tiedemann, J. (2007) Question answering with joost at clef 2007. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 257–260.
Cabrio, E., Cojan, J., Aprosio, A. P., Magnini, B., Lavelli, A., Gandon, F. (2012) Qakis: an open domain qa system based on relational patterns. In: International Semantic Web Conference, ISWC 2012.
Cabrio, E., Cimiano, P., Lopez, V., Ngomo, A. C. N., Unger, C., Walter, S. (2013) Qald-3: Multilingual question answering over linked data. CLEF (Working Notes) 38.
Chakma, K., & Das, A. (2016). Cmir: A corpus for evaluation of code mixed information retrieval of hindi-english tweets. Computación y Sistemas, 20(3), 425–434.CrossRef
Chandu, K. R., Chinnakotla, M., Black, A. W., Shrivastava, M. (2017) Webshodh: A code mixed factoid question answering system for web. In: International Conference of the Cross-Language Evaluation Forum for European Languages, Springer, pp 104–111.
Chandu, K., Loginova, E., Gupta, V., van Genabith, J., Neuman, G., Chinnakotla, M., Nyberg E, Black AW (2018) Code-mixed question answering challenge: Crowd-sourcing data and techniques. In: Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pp 29–38.
Chen, M., Zaniolo, C. (2017) Learning multi-faceted knowledge graph embeddings for natural language processing. In: IJCAI, pp 5169–5170.
Chen, G., Chen, C., Xing, Z., Xu, B. (2016) Learning a dual-language vector space for domain-specific cross-lingual question retrieval. In: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp 744–755.
Chen, D., Fisch, A., Weston, J., Bordes, A. (2017) Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:170400051.
Choudhury, M., Chittaranjan, G., Gupta, P., Das, A. (2014) Overview of fire 2014 track on transliterated search. Proceedings of FIRE pp 68–89.
Cimiano, P. (2009) Flexible semantic composition with dudes. In: Proceedings of the Eighth International Conference on Computational Semantics, Association for Computational Linguistics, pp 272–276.
Devlin, J., Chang, M. W., Lee, K., Toutanova, K. (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805.
Du, X., Shao, J., Cardie, C. (2017) Learning to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:170500106.
Feng, M., Xiang, B., Glass, M. R., Wang, L., Zhou, B. (2015) Applying deep learning to answer selection: A study and an open task. In: Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on, IEEE, pp 813–820.
Ferrández, S., Ferrández, A. (2007) The negative effect of machine translation on cross–lingual question answering. In: International Conference on Intelligent Text Processing and Computational Linguistics, Springer, pp 494–505.
Ferrandez, O., Spurk, C., Kouylekov, M., Dornescu, I., Ferrandez, S., Negri, M., Izquierdo, R., Tomas, D., Orasan, C., Neumann, G., et al. (2011). The qall-me framework: A specifiable-domain multilingual question answering architecture. Web Semantics: Science, Services and Agents on the World Wide Web, 9(2), 137–145.CrossRef
Forner, P., Peñas, A., Agirre, E., Alegria, I., Forăscu, C., Moreau, N., Osenova, P., Prokopidis, P., Rocha, P., Sacaleanu, B. et al. (2008) Overview of the clef 2008 multilingual question answering track. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 262–295.
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096–2030.
Ghosh, S., Ghosh, S., Das, D. (2017) Complexity metric for code-mixed social media text. arXiv preprint arXiv:170701183.
Glavas, G., Litschko, R., Ruder, S., Vulic, I. (2019) How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions. arXiv preprint arXiv:190200508.
Gong, Y., Bowman, S. R. (2017) Ruminating reader: Reasoning with gated multi-hop attention. arXiv preprint arXiv:170407415.
Gupta, P., Bali, K., Banchs, R. E., Choudhury, M., Rosso, P. (2014) Query expansion for mixed-script information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, ACM, pp 677–686.
Haas, C., Riezler, S. (2015) Response-based learning for machine translation of open-domain database queries. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1339–1344.
Hadla, L. S., Hailat, T. M., Al-Kabi, M. N. (2014) Evaluating arabic to english machine translation. Editorial Preface 5(11).
Hakimov, S., Jebbara, S., Cimiano, P. (2017) Amuse: Multilingual semantic parsing for question answering over linked data. In: International Semantic Web Conference, Springer, pp 329–346.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.CrossRef
Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., & Ngonga Ngomo, A. C. (2017). Survey on challenges of question answering in the semantic web. Semantic Web, 8(6), 895–920.CrossRef
Honnibal, M., Johnson, M. (2015) An improved non-monotonic transition system for dependency parsing. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp 1373–1378.
Jaech, A., Mulcaire, G., Hathi, S., Ostendorf, M., Smith, N. A. (2016) Hierarchical character-word models for language identification. arXiv preprint arXiv:160803030.
Jauhiainen, T., Lui, M., Zampieri, M., Baldwin, T., Lindén, K. (2018) Automatic language identification in texts: A survey. arXiv preprint arXiv:180408186.
Jia, R., Liang, P. (2017) Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:170707328.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G. et al. (2016) Google’s multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv:161104558.
Joty, S., Nakov, P., Màrquez, L., Jaradat, I. (2017) Cross-language learning with adversarial neural networks: Application to community question answering. arXiv preprint arXiv:170606749.
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T. (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:160701759.
Kalouli, A. L., Kaiser, K., Hautli-Janisz, A., Kaiser, G. A., Butt, M. (2018) A multilingual approach to question classification. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).
Kim, Y. (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:14085882.
King, B., Abney, S. (2013) Labeling the languages of words in mixed-language documents using weakly supervised methods. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1110–1119.
Kingma, D. P., Ba, J. (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980.
Lai, S, Xu, L., Liu, K., Zhao, J. (2015) Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI conference on artificial intelligence.
Lample, G., Conneau, A., Denoyer, L., Ranzato, M. (2017) Unsupervised machine translation using monolingual corpora only. arXiv preprint arXiv:171100043.
Lee, K., Yoon, K., Park, S., Hwang, S. W. (2018) Semi-supervised training data generation for multilingual question answering. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).
Lui, M., Baldwin, T. (2011) Cross-domain feature selection for language identification. In: Proceedings of 5th international joint conference on natural language processing, pp 553–561.
Luong, T., Pham, H., Manning, C. D. (2015) Bilingual word representations with monolingual quality in mind. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp 151–159.
Magnini, B., Romagnoli, S., Vallin, A., Herrera, J., Peñas, A., Peinado, V., Verdejo, F., de Rijke, M. (2003a) Creating the disequa corpus: a test set for multilingual question answering. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 487–500.
Magnini, B., Romagnoli, S., Vallin, A., Herrera, J., Penas, A., Peinado, V., Verdejo, F., de Rijke, M. (2003b) The multiple language question answering track at clef 2003. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 471–486.
Magnini, B., Vallin, A., Ayache, C., Erbach, G., Peñas, A., De Rijke, M., Rocha, P., Simov, K., Sutcliffe, R. (2004) Overview of the clef 2004 multilingual question answering track. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 371–391.
Martino, G. D. S., Romeo, S., Barrón-Cedeno, A., Joty, S., Marquez, L., Moschitti, A., Nakov, P. (2017) Cross-language question re-ranking. arXiv preprint arXiv:171001487.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J. (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119.
Mohammad, S. M., Salameh, M., & Kiritchenko, S. (2016). How translation alters sentiment. Journal of Artificial Intelligence Research, 55, 95–130.CrossRef
Molina, G., AlGhamdi, F., Ghoneim, M., Hawwari, A., Rey-Villamizar, N., Diab, M., Solorio, T. (2016) Overview for the second shared task on language identification in code-switched data. In: Proceedings of the Second Workshop on Computational Approaches to Code Switching, pp 40–49.
Nakov, P., Màrquez, L., Magdy, W., Moschitti, A., Glass, J., Randeree, B. (2015) Semeval-2015 task 3: Answer selection in community question answering. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp 269–281.
Nakov, P., Mãrquez, L., Moschitti, A., Magdy, W., Mubarak, H., Abed Alhakim Freihat, Glass, J., Randeree, B. (2016) Semeval-2016 task 3: Community question answering. In: Bethard S, Cer DM, Carpuat M, Jurgens D, Nakov P, Zesch T (eds) SemEval@NAACL-HLT, The Association for Computer Linguistics, pp 525–545, URL http://​dblp.​uni-trier.​de/​db/​conf/​semeval/​semeval2016.​htmlNak ovMMMMFGR16.
Nakov, P., Hoogeveen, D., Màrquez, L., Moschitti, A., Mubarak, H., Baldwin, T., Verspoor, K. (2017) Semeval-2017 task 3: Community question answering. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp 27–48.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A. (2017) Automatic differentiation in pytorch.
Raghavi, K. C., Chinnakotla, M. K., Shrivastava, M. (2015) Answer ka type kya he?: Learning to classify questions in code-mixed language. In: Proceedings of the 24th International Conference on World Wide Web, ACM, pp 853–858.
Rahman, M. M., Hisamoto, S., Duh, K. (2019) Query expansion for cross-language question re-ranking. arXiv preprint arXiv:190407982.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P. (2016) Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:160605250.
Rajpurkar, P., Jia, R., Liang, P. (2018) Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:180603822.
Riedl, M., Biemann, C. (2016) Unsupervised compound splitting with distributional semantics rivals supervised methods. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 617–622.
Rücklé, A., Swarnkar, K., Gurevych, I. (2019) Improved cross-lingual question retrieval for community question answering. In: The World Wide Web Conference, ACM, pp 3179–3186.
Ruder, S. (2017) A survey of cross-lingual embedding models. arXiv preprint arXiv:170604902.
Sacaleanu, B., Neumann, G. (2006) Cross-cutting aspects of cross-language question answering systems. In: Proceedings of the Workshop on Multilingual Question Answering-MLQA’06.
Sasaki, Y., Lin, C. J., Chen, K. h, Chen, H. H. (2007) Overview of the ntcir-6 cross-lingual question answering task. In: Proceedings of the 6th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and Cross-Lingual Information Access, May 15–18, Citeseer, pp 153–163.
Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H. (2016) Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:161101603.
Sequiera, R., Choudhury, M., Gupta, P., Rosso, P., Kumar, S., Banerjee, S., Naskar, S. K., Bandyopadhyay, S., Chittaranjan, G., Das, A. et al. (2015) Overview of fire-2015 shared task on mixed script information retrieval. In: FIRE Workshops, vol 1587, pp 19–25.
Solorio, T., Blair, E., Maharjan, S., Bethard, S., Diab, M., Ghoneim, M., Hawwari, A., AlGhamdi, F., Hirschberg, J., Chang, A. et al. (2014) Overview for the first shared task on language identification in code-switched data. In: Proceedings of the First Workshop on Computational Approaches to Code Switching, pp 62–72.
Sugiyama, K., Mizukami, M., Neubig, G., Yoshino, K., Sakti, S., Toda, T., Nakamura, S. (2015) An investigation of machine translation evaluation metrics in cross-lingual question answering. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, pp 442–449.
Tan, M., dos Santos, C., Xiang, B., Zhou, B. (2016) Improved representation learning for question answer matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol 1, pp 464–473.
Tuggener, D. (2016) Incremental coreference resolution for german. PhD thesis, Universität Zürich.
Ture, F., Boschee, E. (2016) Learning to translate for multilingual question answering. arXiv preprint arXiv:160908210.
Unger, C., Forascu, C., Lopez, V., Ngomo, A. C. N., Cabrio, E., Cimiano, P., Walter, S. (2014) Question answering over linked data (qald-4).
Unger, C., Forascu, C., Lopez, V., Ngomo, A. C. N., Cabrio, E., Cimiano, P., Walter, S. (2015) Answering over linked data (qald-5). In: Working notes for CLEF 2015 conference.
Unger, C., Ngomo, A. C. N., Cabrio, E. (2016) 6th open challenge on question answering over linked data (qald-6). In: Semantic Web Evaluation Challenge, Springer, pp 171–177.
Upadhyay, S., Faruqui, M., Dyer, C., Roth, D. (2016) Cross-lingual models of word embeddings: An empirical comparison. arXiv preprint arXiv:160400425.
Usbeck, R., Ngomo, A. C. N., Haarmann, B., Krithara, A., Röder, M., Napolitano, G. (2017) 7th open challenge on question answering over linked data (qald-7). In: Semantic Web Evaluation Challenge, Springer, pp 59–69.
Usbeck, R., Gusmita, R. H., Ngomo, A. C. N., Saleem, M. (2018a) 9th challenge on question answering over linked data (qald-9). In: Semdeep/NLIWoD@ ISWC, pp 58–64.
Usbeck, R., Ngomo, A. C. N., Conrads, F., Röder, M., Napolitano, G. (2018b) 8th challenge on question answering over linked data (qald-8). language 7:1.
Vallin, A., Magnini, B., Giampiccolo, D., Aunimo, L., Ayache, C., Osenova, P., Peñas, A., De Rijke, M., Sacaleanu, B., Santos, D. et al. (2005) Overview of the clef 2005 multilingual question answering track. In: Workshop of the Cross-Language Evaluation Forum for European Languages, Springer, pp 307–331.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., Polosukhin, I. (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, pp 5998–6008.
Veyseh, A. P. B. (2016) Cross-lingual question answering using common semantic space. In: Proceedings of TextGraphs-10: the Workshop on Graph-based Methods for Natural Language Processing, pp 15–19.
Vinyals, O., Fortunato, M., Jaitly, N. (2015) Pointer networks. In: Advances in Neural Information Processing Systems, pp 2692–2700.
Vulić, I., Moens, M. F. (2015) Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 363–372.
Wang, S., Jiang, J. (2016) Machine comprehension using match-lstm and answer pointer. arXiv preprint arXiv:160807905.
Wang, Z., Mi, H., Hamza, W., Florian, R. (2016) Multi-perspective context matching for machine comprehension. arXiv preprint arXiv:161204211.
Weissenborn, D., Wiese, G., Seiffe, L. (2017) Making neural qa as simple as possible but not simpler. arXiv preprint arXiv:170304816.
Xiong, C., Zhong, V., Socher, R. (2016) Dynamic coattention networks for question answering. arXiv preprint arXiv:161101604.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E. (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1480–1489.
Zhang, Y., Riesa, J., Gillick, D., Bakalov, A., Baldridge, J., Weiss, D. (2018) A fast, compact, accurate model for language identification of codemixed text. arXiv preprint arXiv:181004142.
Zhou, G., Xie, Z., He, T., Zhao, J., & Hu, X. T. (2016). Learning the multilingual translation representations for question retrieval in community question answering via non-negative matrix factorization. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(7), 1305–1314.CrossRef
Metadaten
Titel
Towards End-to-End Multilingual Question Answering
verfasst von
Ekaterina Loginova
Stalin Varanasi
Günter Neumann
Publikationsdatum
28.02.2020
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 1/2021
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-020-09996-1

Weitere Artikel der Ausgabe 1/2021

Information Systems Frontiers 1/2021 Zur Ausgabe

OriginalPaper

NetDER: An Architecture for Reasoning About Malicious Behavior

OriginalPaper

Atypical Sample Regularizer Autoencoder for Cross-Domain Human Activity Recognition

OriginalPaper

TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

OriginalPaper

Multi-view Latent Learning Applied to Fashion Industry

OriginalPaper

A Twitter-Based Study of the European Internet of Things

OriginalPaper

An Approach to Extracting Topic-guided Views from the Sources of a Data Lake

Premium Partner

    Bildnachweise