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Evaluating the quality of arguments is a crucial aspect of any system leveraging argument mining.
Tindale, C.W.: Fallacies and Argument Appraisal. Cambridge University Press, 1 edn. (Jan 2007)
2007
Earlier work this paper cites.
Park, J., Cardie, C.: Identifying Appropriate Support for Propositions in Online User Comments. In: Proceedings of the First Workshop on Argument Mining, hosted by the 52nd Annual Meeting of the Association for Computational Linguistics, ArgMining@ACL 2014, June 26, 2014, Baltimore, Maryland, USA. pp. 29–38. The Association for Computer Linguistics (2014)
2014
Earlier work this paper cites.
Persing, I., Ng, V.: Modeling Argument Strength in Student Essays. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers. pp. 543–552. The Association for Computer Linguistics (2015)
2015
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Habernal, I., Gurevych, I.: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics (2016)
2016
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Stab, C., Gurevych, I.: Parsing Argumentation Structures in Persuasive Essays. Comput. Linguistics 43
2017
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Wachsmuth, H., Naderi, N., Habernal, I., Hou, Y., Hirst, G., Gurevych, I., Stein, B.: Argumentation Quality Assessment: Theory vs. Practice. In: Barzilay, R., Kan, M.Y. (eds.) 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017). pp. 250–255. Association for Computational Linguistics (Aug 2017)
2017
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Wachsmuth, H., Naderi, N., Hou, Y., Bilu, Y., Prabhakaran, V., Thijm, T.A., Hirst, G., Stein, B.: Computational Argumentation Quality Assessment in Natural Language. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. pp. 176–187 (2017)
2017
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Carlile, W., Gurrapadi, N., Ke, Z., Ng, V.: Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers. pp. 621–631. Association for Computational Linguistics (2018)
2018
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Yin, P., Deng, B., Chen, E., Vasilescu, B., Neubig, G.: Learning to mine aligned code and natural language pairs from stack overflow. In: Zaidman, A., Kamei, Y., Hill, E. (eds.) Proceedings of the 15th International Conference on Mining Software Repositories, MSR 2018, Gothenburg, Sweden, May 28-29, 2018. pp. 476–486. ACM (2018)
2018
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Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics (2019)
2019
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2019
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Toledo, A., Gretz, S., Cohen-Karlik, E., Friedman, R., et al.: Automatic Argument Quality Assessment - New Datasets and Methods. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019. pp. 5624–5634. Association for Computational Linguistics (2019)
2019
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Brown, T.B., Mann, B., Ryder, N., Subbiah, M., et al.: Language Models are Few-Shot Learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual (2020)
2020
Cited alongside, same era.
2022
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Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large Language Models are Zero-Shot Reasoners. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Guo, J., Cheng, L., Zhang, W., Kok, S., Li, X., Bing, L.: AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach. In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023. pp. 932–946. Association for Computational Linguistics (2023)
2023
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Huo, S., Arabzadeh, N., Clarke, C.L.A.: Retrieving Supporting Evidence for Generative Question Answering. In: Ai, Q., Liu, Y., Moffat, A., Huang, X., Sakai, T., Zobel, J. (eds.) Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2023, Beijing, China, November 26-28, 2023. pp. 11–20. ACM (2023)
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2022
Cited alongside, same era.
OpenAI: ChatGPT (2022)
2022
Cited alongside, same era.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)
2022
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Anil, R., Dai, A.M., Firat, O., Johnson, M., et al.: PaLM 2 Technical Report. CoRR abs/2305.10403
2023
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2023
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Chiang, D.C., Lee, H.: Can Large Language Models Be an Alternative to Human Evaluations? In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. pp. 15607–15631. Association for Computational Linguistics (2023)
2023
Cited alongside, same era.
Ding, B., Qin, C., Liu, L., Chia, Y.K., Li, B., Joty, S., Bing, L.: Is GPT-3 a Good Data Annotator? In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. pp. 11173–11195. Association for Computational Linguistics (2023)
2023
Cited alongside, same era.
Faggioli, G., Dietz, L., Clarke, C.L.A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., Wachsmuth, H.: Perspectives on Large Language Models for Relevance Judgment. In: Yoshioka, M., Kiseleva, J., Aliannejadi, M. (eds.) Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2023, Taipei, Taiwan, 23 July 2023. pp. 39–50. ACM (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Kamalloo, E., Dziri, N., Clarke, C.L.A., Rafiei, D.: Evaluating Open-Domain Question Answering in the Era of Large Language Models. In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. pp. 5591–5606. Association for Computational Linguistics (2023)
2023
Later among the works it cites.
2023
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OpenAI: GPT-4 Technical Report. CoRR abs/2303.08774
2023
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2023
Later among the works it cites.
Wadhwa, S., Amir, S., Wallace, B.C.: Revisiting Relation Extraction in the era of Large Language Models. In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. pp. 15566–15589. Association for Computational Linguistics (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Rønningstad, E., Velldal, E., Øvrelid, L.: A GPT among Annotators: LLM-based Entity-Level Sentiment Annotation. In: Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII). pp. 133–139 (2024)
2024
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