Siamese neural networks on the trail of similarity in bugs in 5G mobile network base stations

• Autorzy: Zarębski Sebastian , Kuzmich Aliaksandr , Sitko Sebastian , Rusek Krzysztof , Chołda Piotr

Identyfikatory

DOI

10.15199/59.2022.4.21

Warianty tytułu

PL

Syjamskie sieci neuronowena tropie podobieństwa błędów w stacjach bazowych sieci komórkowych 5G

• Konferencja: Multikonferencja Krajowego Środowiska Tele- i Radiokomunikacyjnego (7-9.09.2022 ; Warszawa, Polska)
• Języki publikacji: EN

Abstrakty

EN

To improve the R&D process, by reducing duplicated bug tickets, we used an idea of composing BERT encoder as Siamese network to create a system for finding similar existing tickets. We proposed several different methods of generating artificial ticket pairs, to augment the training set. Two phases of training were conducted. The first showed that only and approximate 9% pairs were correctly identified as certainly similar. Only 48% of the test samples are found to be pairs of similar tickets. With the fine-tuning we improved that result up to 81%, proving the concept to be viable for further improvements.

Słowa kluczowe

EN

fault detection; machine learning; natural language processing

PL

wykrywanie uszkodzeń; nauczanie maszynowe; przetwarzanie języka naturalnego

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne
• Rocznik: 2022
• Tom: nr 4
• Strony: 198--201
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Zarębski Sebastian

• NOKIA, Kraków
• AGH University of Science and Technology, Institute of Telecommunications, Kraków

autor

Kuzmich Aliaksandr

• NOKIA, Kraków

autor

Sitko Sebastian

• NOKIA, Kraków

autor

Rusek Krzysztof

• AGH University of Science and Technology, Institute of Telecommunications, Kraków

autor

Chołda Piotr

• AGH University of Science and Technology, Institute of Telecommunications, Kraków

Bibliografia

• [1] 3GPP. 2021. version 17.1.0. " Vocabulary for 3GPP Specifications". 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 21.905 [Online]. Available: http://www.3gpp.org/DynaReport/21905.htm
• [2] Bojanowski Piotr et al., 2017."Enriching Word Vectors with Subword Information". Transactions of the Association for Computational Linguistics. ( 5): 135-146
• [3] Elastic. 2022. "Elasticsearch: The Officia Distributed Search & Analytics Engine". [Online]. Available: http://www.elastic.co/elasticsearch/
• [4] Devlin Jaoob et al. June 2019. "BERT: Pre-training of deep bidirectional,transformers for language understanding". Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. ( 1 ): 4171-4186. Association for Computational Linguistics.
• [5] Reimers Nils, Gurevych Iryna 2019. "Sentence-bert: Sentence embeddings using siamese bert-networks". CoRR.
• [6] Lavi Dor, Medentsiy Volodymyr, Graus David. 2021. "conSultantBERT: Fine-tuned Siamese Sentence. BERT for Matching Jobs and Job Seekers." ArXiv abs/2109.06501
• [7] Chicco Davide. 2021. "Siamese Neural Networks: An Overview". Artificial Neural Networks. Methods in Molecular Biology. (2190): 73-94.
• [8] Merchant Amil et al. 2020. " What happens to BERT embeddings during fine-tuning?", Proceedings of The Third Blackbox NLP Workshop on Analyzing and Interpreting Neural Networks for NLP. 33-44. (Online.) Available: https://aclanthology.org/2020.blackbox.nlp-1. 4
• [9] Li Wen et al. 2020. "ApproximateNearest Neighbor Search on High Dimensional Data - Experiments, Analyses, and Improvement". IEEE Transactions on Knowledge and Data Engineering. 32 (8): 1475-1488.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).