---
_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: <i>Proceedings of the 16th International Natural Language Generation
    Conference</i>.'
  apa: Stahl, M., &#38; Wachsmuth, H. (n.d.). Identifying Feedback Types to Augment
    Feedback Comment Generation. <i>Proceedings of the 16th International Natural
    Language Generation Conference</i>. 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 <i>Proceedings of the 16th International Natural
    Language Generation Conference</i>, 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.” <i>Proceedings of the 16th International Natural
    Language Generation Conference</i>.
  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: <i>Proceedings of the 9th Workshop on Argument Mining</i>.
    International Conference on Computational Linguistics; 2022:111–114.'
  apa: Alshomary, M., &#38; Stahl, M. (2022). Argument Novelty and Validity Assessment
    via Multitask and Transfer Learning. <i>Proceedings of the 9th Workshop on Argument
    Mining</i>, 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 <i>Proceedings of the 9th Workshop on
    Argument Mining</i>, 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 <i>Proceedings of the 9th Workshop on Argument Mining</i>,
    2022, pp. 111–114.
  mla: Alshomary, Milad, and Maja Stahl. “Argument Novelty and Validity Assessment
    via Multitask and Transfer Learning.” <i>Proceedings of the 9th Workshop on Argument
    Mining</i>, 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: <i>Proceedings
    of the Fifth Workshop on Natural Language Processing and Computational Social
    Science</i>.'
  apa: Stahl, M., Spliethöver, M., &#38; Wachsmuth, H. (n.d.). To Prefer or to Choose?
    Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation.
    <i>Proceedings of the Fifth Workshop on Natural Language Processing and Computational
    Social Science</i>. 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 <i>Proceedings of the Fifth Workshop on Natural Language Processing
    and Computational Social Science</i>. 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.” <i>Proceedings of the Fifth Workshop on Natural
    Language Processing and Computational Social Science</i>.
  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'
...