PolEval 2022/23 Challenge Tasks and Results
Łukasz Kobyliński, Maciej Ogrodniczuk, Piotr Rybak, Piotr Przybyła, Piotr Pęzik, Agnieszka Mikołajczyk, Wojciech Janowski, Michał Marcińczuk, Aleksander Smywiński-Pohl
DOI: http://dx.doi.org/10.15439/2023F5627
Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1243–1250 (2023)
Abstract. This paper summarizes the 2022/2023 edition of PolEval --- an evaluation campaign for natural language processing tools for Polish. We describe the tasks organized in this edition, which are: Punctuation prediction from conversational language, Abbreviation disambiguation and Passage Retrieval. We also discuss the datasets prepared for each of the tasks, evaluation metrics chosen to rank the submissions and also sum up the approaches chosen by the participants to tackle the tasks.
References
- Asai, A., Yu, X., Kasai, J., Hajishirzi, H.: One question answering model for many languages with cross-lingual dense passage retrieval. In: NeurIPS (2021)
- Chrabrowa, A., Dragan, Ł., Grzegorczyk, K., Kajtoch, D., Koszowski, M., Mroczkowski, R., Rybak, P.: Evaluation of transfer learning for Polish with a text-to-text model. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. pp. 4374–4394. European Language Resources Association, Marseille, France (Jun 2022), https://aclanthology.org/2022.lrec-1.466
- Dadas, S., Perełkiewicz, M., Poświata, R.: Pre-training polish transformer-based language models at scale. In: Artificial Intelligence and Soft Computing. pp. 301–314. Springer International Publishing (2020)
- Feng, F., Yang, Y., Cer, D., Arivazhagan, N., Wang, W.: Language-agnostic BERT sentence embedding. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 878–891. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl- long.62, https://aclanthology.org/2022.acl-long.62
- Hlubík, P., Španěl, M., Boháč, M., Weingartová, L.: Inserting Punctuation to ASR Output in a Real-Time Production Environment. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds.) Text, Speech, and Dialogue. pp. 418–425. Springer International Publishing, Cham (2020)
- Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., Grave, E.: Unsupervised dense information retrieval with contrastive learning (2021). https://doi.org/10.48550/ARXIV.2112.09118, https://arxiv.org/abs/2112.09118
- Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20, 422–446 (10 2002). https://doi.org/10.1145/582415.582418
- Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., Yih, W.t.: Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 6769–6781. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.emnlp-main.550, https://aclanthology.org/2020.emnlp-main.550
- Kieraś, W., Woliński, M.: Morfeusz 2 – analizator i generator fleksyjny dla języka polskiego. J ̨ezyk Polski XCVII(1), 75–83 (2017)
- Mikołajczyk, A., Wawrzyński, A., Pęzik, P., Adamczyk, M., Kaczmarek, A., Janowski, W.: PolEval 2021 Task 1: Punctuation Restoration from Read Text. In: Ogrodniczuk, M., Kobyliński, Ł. (eds.) Proceedings of the PolEval 2021 Workshop. pp. 21–31. Institute of Computer Science, Polish Academy of Sciences, Warsaw (2021)
- Mroczkowski, R., Rybak, P., Wróblewska, A., Gawlik, I.: HerBERT: Efficiently pretrained transformer-based language model for Polish. In: Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing. pp. 1–10. Association for Computational Linguistics, Kiyv, Ukraine (Apr 2021), https://www.aclweb.org/anthology/2021.bsnlp-1.1
- Nguyen, T.B., Nguyen, Q.M., Nguyen, T.T.H., Do, Q.T., Luong, C.M.: Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models. In: Proceedings of Interspeech 2020. pp. 4263–4267 (2020). https://doi.org/10.21437/Interspeech.2020-1896, http://dx.doi.org/10.21437/Interspeech.2020-1896
- Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., Deng, L.: MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (November 2016), https://www.microsoft.com/en-us/research/publication/ms-marco-human-generated-machine-reading-comprehension-dataset/
- Ogrodniczuk, M., Kobyliński, Ł. (eds.): Proceedings of the PolEval 2021 Workshop. Institute of Computer Science, Polish Academy of Sciences, Warsaw (2021)
- Pappagari, R., Żelasko, P., Mikołajczyk, A., Pęzik, P., Dehak, N.: Joint Prediction of Truecasing and Punctuation for Conversational Speech in Low-Resource Scenarios. In: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). pp. 1185–1191 (2021). https://doi.org/10.1109/ASRU51503.2021.9687976
- Pęzik, P.: Spokes – a Search and Exploration Service for Conversational Corpus Data. In: Linköping Electronic Conference Proceedings. Selected Papers from CLARIN 2014. pp. 99–109. Linköping University Electronic Press (2015)
- Pęzik, P., Krawentek, G., Karasińska, S., Wilk, P., Rybińska, P., Cichosz, A., Peljak-Łapińska, A., Deckert, M., Adamczyk, M.: DiaBiz – an Annotated Corpus of Polish Call Center Dialogs. In: Proceedings of the Language Resources and Evaluation Conference. pp. 723–726. European Language Resources Association, Marseille, France (Jun 2022), https://aclanthology.org/2022.lrec-1.76
- Robertson, S.E., Zaragoza, H.: The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr. 3, 333–389 (2009)
- Rybak, P., Przybyła, P., Ogrodniczuk, M.: Improving question answering performance through manual annotation: Costs, benefits and strategies (2022). https://doi.org/10.48550/ARXIV.2212.08897, https://arxiv.org/abs/2212.08897
- S., K., Cichosz, A., M., A., P., P.: Evaluating Punctuation Prediction in Conversational Language (2023), Forthcoming
- Sirts, K., Peekman, K.: Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts. In: Utka, A., Vaičenonienė, J., Kovalevskaitė, J., Kalinauskaitė, D. (eds.) Human Language Technologies – The Baltic Perspective. Frontiers in Artificial Intelligence and Applications, vol. 328, pp. 174–181 (2020). https://doi.org/10.3233/FAIA200620
- Sunkara, M., Ronanki, S., Bekal, D., Bodapati, S., Kirchhoff, K.: Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech. In: Proceedings of Interspeech 2020. pp. 4911–4915 (2020). https://doi.org/10.21437/Interspeech.2020-3074, http://dx.doi.org/10.21437/Interspeech.2020-3074
- Wang, X.: Analysis of Sentence Boundary of the Host’s Spoken Language Based on Semantic Orientation Pointwise Mutual Information Algorithm. In: Proceedings of the 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). pp. 501–506 (2020). https://doi.org/10.1109/ICMTMA50254.2020.00114
- Yi, J., Tao, J., Bai, Y., Tian, Z., Fan, C.: Adversarial Transfer Learning for Punctuation Restoration (2020)