--- _id: '34083' abstract: - lang: eng text: In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only. author: - first_name: Maja full_name: Stahl, Maja id: '77647' last_name: Stahl - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth citation: ama: 'Stahl M, Wachsmuth H. Identifying Feedback Types to Augment Feedback Comment Generation. In: Proceedings of the 16th International Natural Language Generation Conference.' apa: Stahl, M., & Wachsmuth, H. (n.d.). Identifying Feedback Types to Augment Feedback Comment Generation. Proceedings of the 16th International Natural Language Generation Conference. 16th International Natural Language Generation Conference. bibtex: '@inproceedings{Stahl_Wachsmuth, title={Identifying Feedback Types to Augment Feedback Comment Generation}, booktitle={Proceedings of the 16th International Natural Language Generation Conference}, author={Stahl, Maja and Wachsmuth, Henning} }' chicago: Stahl, Maja, and Henning Wachsmuth. “Identifying Feedback Types to Augment Feedback Comment Generation.” In Proceedings of the 16th International Natural Language Generation Conference, n.d. ieee: M. Stahl and H. Wachsmuth, “Identifying Feedback Types to Augment Feedback Comment Generation,” presented at the 16th International Natural Language Generation Conference. mla: Stahl, Maja, and Henning Wachsmuth. “Identifying Feedback Types to Augment Feedback Comment Generation.” Proceedings of the 16th International Natural Language Generation Conference. short: 'M. Stahl, H. Wachsmuth, in: Proceedings of the 16th International Natural Language Generation Conference, n.d.' conference: name: 16th International Natural Language Generation Conference date_created: 2022-11-15T08:47:57Z date_updated: 2022-11-15T08:48:01Z extern: '1' language: - iso: eng publication: Proceedings of the 16th International Natural Language Generation Conference publication_status: accepted status: public title: Identifying Feedback Types to Augment Feedback Comment Generation type: conference user_id: '77647' year: '2023' ... --- _id: '34051' abstract: - lang: eng text: An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new task to assess the novelty and validity of a conclusion given a set of premises. In this paper, we present a multitask learning approach that transfers the knowledge learned from the natural language inference task to the tasks at hand. Evaluation results indicate the importance of both knowledge transfer and joint learning, placing our approach in the fifth place with strong results compared to baselines. author: - first_name: Milad full_name: Alshomary, Milad id: '73059' last_name: Alshomary - first_name: Maja full_name: Stahl, Maja id: '77647' last_name: Stahl citation: ama: 'Alshomary M, Stahl M. Argument Novelty and Validity Assessment via Multitask and Transfer Learning. In: Proceedings of the 9th Workshop on Argument Mining. International Conference on Computational Linguistics; 2022:111–114.' apa: Alshomary, M., & Stahl, M. (2022). Argument Novelty and Validity Assessment via Multitask and Transfer Learning. Proceedings of the 9th Workshop on Argument Mining, 111–114. bibtex: '@inproceedings{Alshomary_Stahl_2022, place={Online and in Gyeongju, Republic of Korea}, title={Argument Novelty and Validity Assessment via Multitask and Transfer Learning}, booktitle={Proceedings of the 9th Workshop on Argument Mining}, publisher={International Conference on Computational Linguistics}, author={Alshomary, Milad and Stahl, Maja}, year={2022}, pages={111–114} }' chicago: 'Alshomary, Milad, and Maja Stahl. “Argument Novelty and Validity Assessment via Multitask and Transfer Learning.” In Proceedings of the 9th Workshop on Argument Mining, 111–114. Online and in Gyeongju, Republic of Korea: International Conference on Computational Linguistics, 2022.' ieee: M. Alshomary and M. Stahl, “Argument Novelty and Validity Assessment via Multitask and Transfer Learning,” in Proceedings of the 9th Workshop on Argument Mining, 2022, pp. 111–114. mla: Alshomary, Milad, and Maja Stahl. “Argument Novelty and Validity Assessment via Multitask and Transfer Learning.” Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, 2022, pp. 111–114. short: 'M. Alshomary, M. Stahl, in: Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, Online and in Gyeongju, Republic of Korea, 2022, pp. 111–114.' date_created: 2022-11-10T09:51:45Z date_updated: 2022-11-15T08:49:10Z language: - iso: eng page: 111–114 place: Online and in Gyeongju, Republic of Korea publication: Proceedings of the 9th Workshop on Argument Mining publication_status: published publisher: International Conference on Computational Linguistics status: public title: Argument Novelty and Validity Assessment via Multitask and Transfer Learning type: conference user_id: '77647' year: '2022' ... --- _id: '34082' abstract: - lang: eng text: Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better. author: - first_name: Maja full_name: Stahl, Maja id: '77647' last_name: Stahl - first_name: Maximilian full_name: Spliethöver, Maximilian id: '84035' last_name: Spliethöver orcid: 0000-0003-4364-1409 - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth citation: ama: 'Stahl M, Spliethöver M, Wachsmuth H. To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.' apa: Stahl, M., Spliethöver, M., & Wachsmuth, H. (n.d.). To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science. Fifth Workshop on NLP and Computational Social Science (NLP+CSS)  At EMNLP 2022, Abu Dhabi, United Arab Emirates. bibtex: '@inproceedings{Stahl_Spliethöver_Wachsmuth, place={Abu Dhabi, United Arab Emirates}, title={To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation}, booktitle={Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science}, author={Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning} }' chicago: Stahl, Maja, Maximilian Spliethöver, and Henning Wachsmuth. “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation.” In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science. Abu Dhabi, United Arab Emirates, n.d. ieee: M. Stahl, M. Spliethöver, and H. Wachsmuth, “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation,” presented at the Fifth Workshop on NLP and Computational Social Science (NLP+CSS)  At EMNLP 2022, Abu Dhabi, United Arab Emirates. mla: Stahl, Maja, et al. “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation.” Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science. short: 'M. Stahl, M. Spliethöver, H. Wachsmuth, in: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science, Abu Dhabi, United Arab Emirates, n.d.' conference: location: Abu Dhabi, United Arab Emirates name: Fifth Workshop on NLP and Computational Social Science (NLP+CSS) At EMNLP 2022 date_created: 2022-11-15T08:29:26Z date_updated: 2022-11-18T08:22:56Z extern: '1' language: - iso: eng place: Abu Dhabi, United Arab Emirates publication: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science publication_status: accepted quality_controlled: '1' status: public title: To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation type: conference user_id: '77647' year: '2022' ...