Abstract
The goal of the Touché lab on argument retrieval is to foster and support the development of technologies for argument mining and argument analysis. In the third edition of Touché, we organize three shared tasks: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance. In this paper, we briefly summarize the results of two years of organizing Touché and describe the planned setup for the third edition at CLEF 2022.
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Notes
- 1.
‘touché’ is commonly “used to acknowledge a hit in fencing or the success or appropriateness of an argument” [https://merriam-webster.com/dictionary/touche].
- 2.
- 3.
Also available as a Python library: https://pypi.org/project/targer-api/
References
Ajjour, Y., Wachsmuth, H., Kiesel, J., Potthast, M., Hagen, M., Stein, B.: Data acquisition for argument search: the args.me corpus. In: Benzmüller, C., Stuckenschmidt, H. (eds.) KI 2019. LNCS (LNAI), vol. 11793, pp. 48–59. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30179-8_4
Akiki, C., Potthast, M.: Exploring argument retrieval with transformers. In: Working Notes Papers of the CLEF 2020 Evaluation Labs, vol. 2696 (2020), ISSN 1613–0073. http://ceur-ws.org/Vol-2696/
Alshomary, M., Düsterhus, N., Wachsmuth, H.: Extractive snippet generation for arguments. In: Proceedings of the 43nd International ACM Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 1969–1972, ACM (2020). https://doi.org/10.1145/3397271.3401186
Bevendorff, J., Stein, B., Hagen, M., Potthast, M.: Elastic ChatNoir: search engine for the ClueWeb and the common crawl. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 820–824. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_83
Bondarenko, A., Ajjour, Y., Dittmar, V., Homann, N., Braslavski, P., Hagen, M.: Towards understanding and answering comparative questions. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, WSDM 2022. ACM (2022). https://doi.org/10.1145/3488560.3498534
Bondarenko, A., et al.: Overview of touché 2020: argument retrieval. In: Working Notes Papers of the CLEF 2020 Evaluation Labs, CEUR Workshop Proceedings, vol. 2696 (2020). https://doi.org/10.1007/978-3-030-58219-7_26
Bondarenko, A., et al.: Overview of touché 2021: argument retrieval. In: Candan, K.S., Ionescu, B., Goeuriot, L., Larsen, B., Müller, H., Joly, A., Maistro, M., Piroi, F., Faggioli, G., Ferro, N. (eds.) CLEF 2021. LNCS, vol. 12880, pp. 450–467. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85251-1_28
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794, ACM (2016). https://doi.org/10.1145/2939672.2939785
Chernodub, A., et al.: TARGER: neural argument mining at your fingertips. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, pp. 195–200. ACL (2019). https://doi.org/10.18653/v1/p19-3031
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, pp. 4171–4186. ACL (2019). https://doi.org/10.18653/v1/n19-1423
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
Fröbe, M., Bevendorff, J., Gienapp, L., Völske, M., Stein, B., Potthast, M., Hagen, M.: CopyCat: near-duplicates within and between the ClueWeb and the common crawl. In: Proceedings of the 44th International ACM Conference on Research and Development in Information Retrieval, SIGIR 2021, pp. 2398–2404. ACM (2021). https://dl.acm.org/doi/10.1145/3404835.3463246
Fröbe, M., Bevendorff, J., Reimer, J., Potthast, M., Hagen, M.: Sampling Bias due to near-duplicates in learning to rank. In: Proceedings of the 43rd International ACM Conference on Research and Development in Information Retrieval, SIGIR 2020. ACM (2020). https://dl.acm.org/doi/10.1145/3397271.3401212
Fröbe, M., Bittner, J.P., Potthast, M., Hagen, M.: The effect of content-equivalent near-duplicates on the evaluation of search engines. In: Jose, J.M., Yilmaz, E., Magalhães, J., Castells, P., Ferro, N., Silva, M.J., Martins, F. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 12–19. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_2
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the Annual Conference on Neural Information Processing Systems, NeurIPS 2017, pp. 3146–3154 (2017). https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html
Kiesel, J., Reichenbach, N., Stein, B., Potthast, M.: Image retrieval for arguments using stance-aware query expansion. In: Proceedings of the 8th Workshop on Argument Mining, ArgMining 2021 at EMNLP, pp. 36–45. ACL (2021)
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. In: Proceedings of the 8th International Conference on Learning Representations, ICLR 2020, OpenReview.net (2020). https://openreview.net/forum?id=H1eA7AEtvS
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the 1st International Conference on Learning Representations, ICLR 2013 (2013). http://arxiv.org/abs/1301.3781
Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., Deng, L.: MS MARCO: a human generated machine reading comprehension dataset. In: Proceedings of the Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches 2016 at NIPS, CEUR Workshop Proceedings, vol. 1773, CEUR-WS.org (2016). http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper9.pdf
Potthast, M., Gollub, T., Wiegmann, M., Stein, B.: TIRA integrated research architecture. In: Information Retrieval Evaluation in a Changing World. TIRS, vol. 41, pp. 123–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22948-1_5
Trask, A., Michalak, P., Liu, J.: sense2vec - a fast and accurate method for word sense disambiguation in neural word embeddings. CoRR abs/1511.06388 (2015). http://arxiv.org/abs/1511.06388
Wachsmuth, H., et al.: Computational argumentation quality assessment in natural language. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, pp. 176–187 (2017). http://aclweb.org/anthology/E17-1017
Acknowledgments
This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG) through the projects “ACQuA” and “ACQuA 2.0” (Answering Comparative Questions with Arguments; grants HA 5851/2-1, HA 5851/2-2, BI 1544/7-1, BI 1544/7-2) and “OASiS: Objective Argument Summarization in Search” (grant WA 4591/3-1), all part of the priority program “RATIO: Robust Argumentation Machines” (SPP 1999), and the German Ministry for Science and Education (BMBF) through the project “Shared Tasks as an Innovative Approach to Implement AI and Big Data-based Applications within Universities (SharKI)” (grant FKZ 16DHB4021). We are also grateful to Jan Heinrich Reimer for developing the TARGER Python library.
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Bondarenko, A. et al. (2022). Overview of Touché 2022: Argument Retrieval. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_43
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