---
_id: '61234'
abstract:
- lang: eng
text: "The ability to generate explanations that are understood by explainees is
the\r\nquintessence of explainable artificial intelligence. Since understanding\r\ndepends
on the explainee's background and needs, recent research focused on\r\nco-constructive
explanation dialogues, where an explainer continuously monitors\r\nthe explainee's
understanding and adapts their explanations dynamically. We\r\ninvestigate the
ability of large language models (LLMs) to engage as explainers\r\nin co-constructive
explanation dialogues. In particular, we present a user\r\nstudy in which explainees
interact with an LLM in two settings, one of which\r\ninvolves the LLM being instructed
to explain a topic co-constructively. We\r\nevaluate the explainees' understanding
before and after the dialogue, as well\r\nas their perception of the LLMs' co-constructive
behavior. Our results suggest\r\nthat LLMs show some co-constructive behaviors,
such as asking verification\r\nquestions, that foster the explainees' engagement
and can improve understanding\r\nof a topic. However, their ability to effectively
monitor the current\r\nunderstanding and scaffold the explanations accordingly
remains limited."
author:
- first_name: Leandra
full_name: Fichtel, Leandra
last_name: Fichtel
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Patricia
full_name: Jimenez, Patricia
id: '103339'
last_name: Jimenez
- first_name: Nils
full_name: Klowait, Nils
id: '98454'
last_name: Klowait
orcid: 0000-0002-7347-099X
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Amelie
full_name: Robrecht, Amelie
id: '91982'
last_name: Robrecht
orcid: 0000-0001-5622-8248
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Lutz
full_name: Terfloth, Lutz
id: '37320'
last_name: Terfloth
- first_name: Anna-Lisa
full_name: Vollmer, Anna-Lisa
id: '86589'
last_name: Vollmer
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Fichtel L, Spliethöver M, Hüllermeier E, et al. Investigating Co-Constructive
Behavior of Large Language Models in Explanation Dialogues. In: Proceedings
of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Association for Computational Linguistics.'
apa: Fichtel, L., Spliethöver, M., Hüllermeier, E., Jimenez, P., Klowait, N., Kopp,
S., Ngonga Ngomo, A.-C., Robrecht, A., Scharlau, I., Terfloth, L., Vollmer, A.-L.,
& Wachsmuth, H. (n.d.). Investigating Co-Constructive Behavior of Large Language
Models in Explanation Dialogues. Proceedings of the 26th Annual Meeting of
the Special Interest Group on Discourse and Dialogue. Annual Meeting of the
Special Interest Group on Discourse and Dialogue.
bibtex: '@inproceedings{Fichtel_Spliethöver_Hüllermeier_Jimenez_Klowait_Kopp_Ngonga
Ngomo_Robrecht_Scharlau_Terfloth_et al., place={Avignon, France}, title={Investigating
Co-Constructive Behavior of Large Language Models in Explanation Dialogues},
booktitle={Proceedings of the 26th Annual Meeting of the Special Interest Group
on Discourse and Dialogue}, publisher={Association for Computational Linguistics},
author={Fichtel, Leandra and Spliethöver, Maximilian and Hüllermeier, Eyke and
Jimenez, Patricia and Klowait, Nils and Kopp, Stefan and Ngonga Ngomo, Axel-Cyrille
and Robrecht, Amelie and Scharlau, Ingrid and Terfloth, Lutz and et al.} }'
chicago: 'Fichtel, Leandra, Maximilian Spliethöver, Eyke Hüllermeier, Patricia Jimenez,
Nils Klowait, Stefan Kopp, Axel-Cyrille Ngonga Ngomo, et al. “Investigating Co-Constructive
Behavior of Large Language Models in Explanation Dialogues.” In Proceedings
of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Avignon, France: Association for Computational Linguistics, n.d.'
ieee: L. Fichtel et al., “Investigating Co-Constructive Behavior of Large
Language Models in Explanation Dialogues,” presented at the Annual Meeting of
the Special Interest Group on Discourse and Dialogue.
mla: Fichtel, Leandra, et al. “Investigating Co-Constructive Behavior of Large Language
Models in Explanation Dialogues.” Proceedings of the 26th Annual Meeting of
the Special Interest Group on Discourse and Dialogue, Association for Computational
Linguistics.
short: 'L. Fichtel, M. Spliethöver, E. Hüllermeier, P. Jimenez, N. Klowait, S. Kopp,
A.-C. Ngonga Ngomo, A. Robrecht, I. Scharlau, L. Terfloth, A.-L. Vollmer, H. Wachsmuth,
in: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse
and Dialogue, Association for Computational Linguistics, Avignon, France, n.d.'
conference:
name: Annual Meeting of the Special Interest Group on Discourse and Dialogue
date_created: 2025-09-11T16:11:17Z
date_updated: 2025-09-12T09:50:48Z
department:
- _id: '660'
external_id:
arxiv:
- '2504.18483'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2504.18483
oa: '1'
place: Avignon, France
project:
- _id: '118'
name: 'TRR 318: Project Area INF'
- _id: '121'
name: 'TRR 318; TP B01: Ein dialogbasierter Ansatz zur Erklärung von Modellen des
maschinellen Lernens'
- _id: '127'
name: 'TRR 318; TP C04: Metaphern als Werkzeug des Erklärens'
- _id: '122'
name: TRR 318 - Subproject B3
- _id: '119'
name: TRR 318 - Project Area Ö
- _id: '114'
name: 'TRR 318; TP A04: Integration des technischen Modells in das Partnermodell
bei der Erklärung von digitalen Artefakten'
publication: Proceedings of the 26th Annual Meeting of the Special Interest Group
on Discourse and Dialogue
publication_status: accepted
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/sigdial25-co-constructive-llms
- relation: research_data
url: https://github.com/webis-de/sigdial25-co-constructive-llms-data
status: public
title: Investigating Co-Constructive Behavior of Large Language Models in Explanation
Dialogues
type: conference
user_id: '84035'
year: '2025'
...
---
_id: '59856'
abstract:
- lang: eng
text: Recent advances on instruction fine-tuning have led to the development of
various prompting techniques for large language models, such as explicit reasoning
steps. However, the success of techniques depends on various parameters, such
as the task, language model, and context provided. Finding an effective prompt
is, therefore, often a trial-and-error process. Most existing approaches to automatic
prompting aim to optimize individual techniques instead of compositions of techniques
and their dependence on the input. To fill this gap, we propose an adaptive prompting
approach that predicts the optimal prompt composition ad-hoc for a given input.
We apply our approach to social bias detection, a highly context-dependent task
that requires semantic understanding. We evaluate it with three large language
models on three datasets, comparing compositions to individual techniques and
other baselines. The results underline the importance of finding an effective
prompt composition. Our approach robustly ensures high detection performance,
and is best in several settings. Moreover, first experiments on other tasks support
its generalizability.
author:
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Tim
full_name: Knebler, Tim
last_name: Knebler
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Spliethöver M, Knebler T, Fumagalli F, et al. Adaptive Prompting: Ad-hoc Prompt
Composition for Social Bias Detection. In: Chiruzzo L, Ritter A, Wang L, eds.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of
the Association for Computational Linguistics: Human Language Technologies (Volume
1: Long Papers). Association for Computational Linguistics; 2025:2421–2449.'
apa: 'Spliethöver, M., Knebler, T., Fumagalli, F., Muschalik, M., Hammer, B., Hüllermeier,
E., & Wachsmuth, H. (2025). Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Proceedings
of the 2025 Conference of the Nations of the Americas Chapter of the Association
for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
(pp. 2421–2449). Association for Computational Linguistics.'
bibtex: '@inproceedings{Spliethöver_Knebler_Fumagalli_Muschalik_Hammer_Hüllermeier_Wachsmuth_2025,
place={Albuquerque, New Mexico}, title={Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection}, booktitle={Proceedings of the 2025 Conference of the
Nations of the Americas Chapter of the Association for Computational Linguistics:
Human Language Technologies (Volume 1: Long Papers)}, publisher={Association for
Computational Linguistics}, author={Spliethöver, Maximilian and Knebler, Tim and
Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and Hüllermeier,
Eyke and Wachsmuth, Henning}, editor={Chiruzzo, Luis and Ritter, Alan and Wang,
Lu}, year={2025}, pages={2421–2449} }'
chicago: 'Spliethöver, Maximilian, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik,
Barbara Hammer, Eyke Hüllermeier, and Henning Wachsmuth. “Adaptive Prompting:
Ad-Hoc Prompt Composition for Social Bias Detection.” In Proceedings of the
2025 Conference of the Nations of the Americas Chapter of the Association for
Computational Linguistics: Human Language Technologies (Volume 1: Long Papers),
edited by Luis Chiruzzo, Alan Ritter, and Lu Wang, 2421–2449. Albuquerque, New
Mexico: Association for Computational Linguistics, 2025.'
ieee: 'M. Spliethöver et al., “Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection,” in Proceedings of the 2025 Conference of the Nations
of the Americas Chapter of the Association for Computational Linguistics: Human
Language Technologies (Volume 1: Long Papers), 2025, pp. 2421–2449.'
mla: 'Spliethöver, Maximilian, et al. “Adaptive Prompting: Ad-Hoc Prompt Composition
for Social Bias Detection.” Proceedings of the 2025 Conference of the Nations
of the Americas Chapter of the Association for Computational Linguistics: Human
Language Technologies (Volume 1: Long Papers), edited by Luis Chiruzzo et
al., Association for Computational Linguistics, 2025, pp. 2421–2449.'
short: 'M. Spliethöver, T. Knebler, F. Fumagalli, M. Muschalik, B. Hammer, E. Hüllermeier,
H. Wachsmuth, in: L. Chiruzzo, A. Ritter, L. Wang (Eds.), Proceedings of the 2025
Conference of the Nations of the Americas Chapter of the Association for Computational
Linguistics: Human Language Technologies (Volume 1: Long Papers), Association
for Computational Linguistics, Albuquerque, New Mexico, 2025, pp. 2421–2449.'
date_created: 2025-05-10T12:37:45Z
date_updated: 2025-09-12T09:51:30Z
department:
- _id: '660'
editor:
- first_name: Luis
full_name: Chiruzzo, Luis
last_name: Chiruzzo
- first_name: Alan
full_name: Ritter, Alan
last_name: Ritter
- first_name: Lu
full_name: Wang, Lu
last_name: Wang
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2025.naacl-long.122/
oa: '1'
page: 2421–2449
place: Albuquerque, New Mexico
project:
- _id: '118'
name: 'TRR 318: Project Area INF'
- _id: '126'
name: TRR 318 - Subproject C3
publication: 'Proceedings of the 2025 Conference of the Nations of the Americas Chapter
of the Association for Computational Linguistics: Human Language Technologies (Volume
1: Long Papers)'
publication_identifier:
isbn:
- 979-8-89176-189-6
publication_status: published
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/naacl25-prompt-compositions
status: public
title: 'Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection'
type: conference
user_id: '84035'
year: '2025'
...
---
_id: '58722'
abstract:
- lang: eng
text: Dialects introduce syntactic and lexical variations in language that occur
in regional or social groups. Most NLP methods are not sensitive to such variations.
This may lead to unfair behavior of the methods, conveying negative bias towards
dialect speakers. While previous work has studied dialect-related fairness for
aspects like hate speech, other aspects of biased language, such as lewdness,
remain fully unexplored. To fill this gap, we investigate performance disparities
between dialects in the detection of five aspects of biased language and how to
mitigate them. To alleviate bias, we present a multitask learning approach that
models dialect language as an auxiliary task to incorporate syntactic and lexical
variations. In our experiments with African-American English dialect, we provide
empirical evidence that complementing common learning approaches with dialect
modeling improves their fairness. Furthermore, the results suggest that multitask
learning achieves state-of-the-art performance and helps to detect properties
of biased language more reliably.
author:
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Sai Nikhil
full_name: Menon, Sai Nikhil
last_name: Menon
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Spliethöver M, Menon SN, Wachsmuth H. Disentangling Dialect from Social Bias
via Multitask Learning to Improve Fairness. In: Ku L-W, Martins A, Srikumar V,
eds. Findings of the Association for Computational Linguistics: ACL 2024.
Association for Computational Linguistics; 2024:9294–9313. doi:10.18653/v1/2024.findings-acl.553'
apa: 'Spliethöver, M., Menon, S. N., & Wachsmuth, H. (2024). Disentangling Dialect
from Social Bias via Multitask Learning to Improve Fairness. In L.-W. Ku, A. Martins,
& V. Srikumar (Eds.), Findings of the Association for Computational Linguistics:
ACL 2024 (pp. 9294–9313). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.553'
bibtex: '@inproceedings{Spliethöver_Menon_Wachsmuth_2024, place={Bangkok, Thailand},
title={Disentangling Dialect from Social Bias via Multitask Learning to Improve
Fairness}, DOI={10.18653/v1/2024.findings-acl.553},
booktitle={Findings of the Association for Computational Linguistics: ACL 2024},
publisher={Association for Computational Linguistics}, author={Spliethöver, Maximilian
and Menon, Sai Nikhil and Wachsmuth, Henning}, editor={Ku, Lun-Wei and Martins,
Andre and Srikumar, Vivek}, year={2024}, pages={9294–9313} }'
chicago: 'Spliethöver, Maximilian, Sai Nikhil Menon, and Henning Wachsmuth. “Disentangling
Dialect from Social Bias via Multitask Learning to Improve Fairness.” In Findings
of the Association for Computational Linguistics: ACL 2024, edited by Lun-Wei
Ku, Andre Martins, and Vivek Srikumar, 9294–9313. Bangkok, Thailand: Association
for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-acl.553.'
ieee: 'M. Spliethöver, S. N. Menon, and H. Wachsmuth, “Disentangling Dialect from
Social Bias via Multitask Learning to Improve Fairness,” in Findings of the
Association for Computational Linguistics: ACL 2024, 2024, pp. 9294–9313,
doi: 10.18653/v1/2024.findings-acl.553.'
mla: 'Spliethöver, Maximilian, et al. “Disentangling Dialect from Social Bias via
Multitask Learning to Improve Fairness.” Findings of the Association for Computational
Linguistics: ACL 2024, edited by Lun-Wei Ku et al., Association for Computational
Linguistics, 2024, pp. 9294–9313, doi:10.18653/v1/2024.findings-acl.553.'
short: 'M. Spliethöver, S.N. Menon, H. Wachsmuth, in: L.-W. Ku, A. Martins, V. Srikumar
(Eds.), Findings of the Association for Computational Linguistics: ACL 2024, Association
for Computational Linguistics, Bangkok, Thailand, 2024, pp. 9294–9313.'
date_created: 2025-02-20T08:18:01Z
date_updated: 2025-09-12T09:52:59Z
department:
- _id: '600'
- _id: '660'
doi: 10.18653/v1/2024.findings-acl.553
editor:
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
- first_name: Andre
full_name: Martins, Andre
last_name: Martins
- first_name: Vivek
full_name: Srikumar, Vivek
last_name: Srikumar
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2024.findings-acl.553/
oa: '1'
page: 9294–9313
place: Bangkok, Thailand
project:
- _id: '118'
name: 'TRR 318 - INF: TRR 318 - Project Area INF'
publication: 'Findings of the Association for Computational Linguistics: ACL 2024'
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/acl24-dialect-aware-bias-detection
status: public
title: Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
type: conference
user_id: '84035'
year: '2024'
...
---
_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'
...
---
_id: '22156'
abstract:
- lang: eng
text: Word embedding models reflect bias towards genders, ethnicities, and other
social groups present in the underlying training data. Metrics such as ECT, RNSB,
and WEAT quantify bias in these models based on predefined word lists representing
social groups and bias-conveying concepts. How suitable these lists actually are
to reveal bias - let alone the bias metrics in general - remains unclear, though.
In this paper, we study how to assess the quality of bias metrics for word embedding
models. In particular, we present a generic method, Bias Silhouette Analysis (BSA),
that quantifies the accuracy and robustness of such a metric and of the word lists
used. Given a biased and an unbiased reference embedding model, BSA applies the
metric systematically for several subsets of the lists to the models. The variance
and rate of convergence of the bias values of each model then entail the robustness
of the word lists, whereas the distance between the models' values gives indications
of the general accuracy of the metric with the word lists. We demonstrate the
behavior of BSA on two standard embedding models for the three mentioned metrics
with several word lists from existing research.
author:
- 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: 'Spliethöver M, Wachsmuth H. Bias Silhouette Analysis: Towards Assessing the
Quality of Bias Metrics for Word Embedding Models. In: Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence, IJCAI-21. ; 2021:552-559.
doi:10.24963/ijcai.2021/77'
apa: 'Spliethöver, M., & Wachsmuth, H. (2021). Bias Silhouette Analysis: Towards
Assessing the Quality of Bias Metrics for Word Embedding Models. Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21,
552–559. https://doi.org/10.24963/ijcai.2021/77'
bibtex: '@inproceedings{Spliethöver_Wachsmuth_2021, title={Bias Silhouette Analysis:
Towards Assessing the Quality of Bias Metrics for Word Embedding Models}, DOI={10.24963/ijcai.2021/77}, booktitle={Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21},
author={Spliethöver, Maximilian and Wachsmuth, Henning}, year={2021}, pages={552–559}
}'
chicago: 'Spliethöver, Maximilian, and Henning Wachsmuth. “Bias Silhouette Analysis:
Towards Assessing the Quality of Bias Metrics for Word Embedding Models.” In Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21,
552–59, 2021. https://doi.org/10.24963/ijcai.2021/77.'
ieee: 'M. Spliethöver and H. Wachsmuth, “Bias Silhouette Analysis: Towards Assessing
the Quality of Bias Metrics for Word Embedding Models,” in Proceedings of the
Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21,
Online, 2021, pp. 552–559, doi: 10.24963/ijcai.2021/77.'
mla: 'Spliethöver, Maximilian, and Henning Wachsmuth. “Bias Silhouette Analysis:
Towards Assessing the Quality of Bias Metrics for Word Embedding Models.” Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21,
2021, pp. 552–59, doi:10.24963/ijcai.2021/77.'
short: 'M. Spliethöver, H. Wachsmuth, in: Proceedings of the Thirtieth International
Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 552–559.'
conference:
end_date: 2021-08-26
location: Online
name: 30th International Joint Conference on Artificial Intelligence (IJCAI-21)
start_date: 2021-08-19
date_created: 2021-05-11T23:13:26Z
date_updated: 2022-01-06T06:55:28Z
department:
- _id: '600'
doi: 10.24963/ijcai.2021/77
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ijcai.org/proceedings/2021/77
oa: '1'
page: 552-559
publication: Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence, IJCAI-21
quality_controlled: '1'
status: public
title: 'Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for
Word Embedding Models'
type: conference
user_id: '82920'
year: '2021'
...
---
_id: '25297'
author:
- first_name: Milad
full_name: Alshomary, Milad
id: '73059'
last_name: Alshomary
- first_name: Timon
full_name: Gurcke, Timon
id: '52174'
last_name: Gurcke
- first_name: Shahbaz
full_name: Syed, Shahbaz
last_name: Syed
- first_name: Philipp
full_name: Heinisch, Philipp
last_name: Heinisch
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Alshomary M, Gurcke T, Syed S, et al. Key Point Analysis via Contrastive Learning
and Extractive Argument Summarization. In: Proceedings of the 8th Workshop
on Argument Mining. ; 2021:184-189.'
apa: Alshomary, M., Gurcke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano,
P., Potthast, M., & Wachsmuth, H. (2021). Key Point Analysis via Contrastive
Learning and Extractive Argument Summarization. Proceedings of the 8th Workshop
on Argument Mining, 184–189.
bibtex: '@inproceedings{Alshomary_Gurcke_Syed_Heinisch_Spliethöver_Cimiano_Potthast_Wachsmuth_2021,
title={Key Point Analysis via Contrastive Learning and Extractive Argument Summarization},
booktitle={Proceedings of the 8th Workshop on Argument Mining}, author={Alshomary,
Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver,
Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning},
year={2021}, pages={184–189} }'
chicago: Alshomary, Milad, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian
Spliethöver, Philipp Cimiano, Martin Potthast, and Henning Wachsmuth. “Key Point
Analysis via Contrastive Learning and Extractive Argument Summarization.” In Proceedings
of the 8th Workshop on Argument Mining, 184–89, 2021.
ieee: M. Alshomary et al., “Key Point Analysis via Contrastive Learning and
Extractive Argument Summarization,” in Proceedings of the 8th Workshop on Argument
Mining, 2021, pp. 184–189.
mla: Alshomary, Milad, et al. “Key Point Analysis via Contrastive Learning and Extractive
Argument Summarization.” Proceedings of the 8th Workshop on Argument Mining,
2021, pp. 184–89.
short: 'M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano,
M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining,
2021, pp. 184–189.'
date_created: 2021-10-04T12:40:02Z
date_updated: 2022-03-08T12:47:33Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://aclanthology.org/2021.argmining-1.19.pdf
page: 184 - 189
publication: Proceedings of the 8th Workshop on Argument Mining
status: public
title: Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
type: conference
user_id: '82920'
year: '2021'
...
---
_id: '20139'
author:
- 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: 'Spliethöver M, Wachsmuth H. Argument from Old Man’s View: Assessing Social
Bias in Argumentation. In: Proceedings of the 7th Workshop on Argument Mining
(ArgMining 2020). ; 2020:76-87.'
apa: 'Spliethöver, M., & Wachsmuth, H. (2020). Argument from Old Man’s View:
Assessing Social Bias in Argumentation. In Proceedings of the 7th Workshop
on Argument Mining (ArgMining 2020) (pp. 76–87).'
bibtex: '@inproceedings{Spliethöver_Wachsmuth_2020, title={Argument from Old Man’s
View: Assessing Social Bias in Argumentation}, booktitle={Proceedings of the 7th
Workshop on Argument Mining (ArgMining 2020)}, author={Spliethöver, Maximilian
and Wachsmuth, Henning}, year={2020}, pages={76–87} }'
chicago: 'Spliethöver, Maximilian, and Henning Wachsmuth. “Argument from Old Man’s
View: Assessing Social Bias in Argumentation.” In Proceedings of the 7th Workshop
on Argument Mining (ArgMining 2020), 76–87, 2020.'
ieee: 'M. Spliethöver and H. Wachsmuth, “Argument from Old Man’s View: Assessing
Social Bias in Argumentation,” in Proceedings of the 7th Workshop on Argument
Mining (ArgMining 2020), 2020, pp. 76–87.'
mla: 'Spliethöver, Maximilian, and Henning Wachsmuth. “Argument from Old Man’s View:
Assessing Social Bias in Argumentation.” Proceedings of the 7th Workshop on
Argument Mining (ArgMining 2020), 2020, pp. 76–87.'
short: 'M. Spliethöver, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument
Mining (ArgMining 2020), 2020, pp. 76–87.'
date_created: 2020-10-20T13:03:08Z
date_updated: 2022-01-06T06:54:20Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.aclweb.org/anthology/2020.argmining-1.9
oa: '1'
page: 76-87
publication: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)
status: public
title: 'Argument from Old Man''s View: Assessing Social Bias in Argumentation'
type: conference
user_id: '84035'
year: '2020'
...
---
_id: '21174'
abstract:
- lang: eng
text: Overcoming a range of challenges that traditional therapy faces, VRET yields
great potential for the treatment of phobias such as acrophobia, the fear of heights.
We investigate this potential and present playful user-generated treatment (PUT),
a novel game-based approach for VRET. Based on a requirement analysis consisting
of a literature review and semi-structured interviews with professional therapists,
we designed and implemented the PUT concept as a two-step VR game design. To validate
our approach, we conducted two studies. (1) In a study with 31 non-acrophobic
subjects, we investigated the effect of content creation on player experience,
motivation and height perception, and (2) in an online survey, we collected feedback
from professional therapists. Both studies reveal that the PUT approach is well
applicable. In particular, the analysis of the user study shows that the design
phase leads to increased interest and enjoyment without notably influencing affective
measures during the exposure session. Our work can help guiding researchers and
practitioners at the intersection of game design and exposure therapy.
author:
- first_name: Dmitry
full_name: Alexandrovsky, Dmitry
last_name: Alexandrovsky
- first_name: Georg
full_name: Volkmar, Georg
last_name: Volkmar
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Stefan
full_name: Finke, Stefan
last_name: Finke
- first_name: Marc
full_name: Herrlich, Marc
last_name: Herrlich
- first_name: Tanja
full_name: Döring, Tanja
last_name: Döring
- first_name: Jan David
full_name: Smeddinck, Jan David
last_name: Smeddinck
- first_name: Rainer
full_name: Malaka, Rainer
last_name: Malaka
citation:
ama: 'Alexandrovsky D, Volkmar G, Spliethöver M, et al. Playful User-Generated Treatment:
A Novel Game Design Approach for VR Exposure Therapy. In: Proceedings of the
Annual Symposium on Computer-Human Interaction in Play. CHI PLAY’20. New York,
NY, USA: Association for Computing Machinery; 2020:32–45. doi:10.1145/3410404.3414222'
apa: 'Alexandrovsky, D., Volkmar, G., Spliethöver, M., Finke, S., Herrlich, M.,
Döring, T., … Malaka, R. (2020). Playful User-Generated Treatment: A Novel Game
Design Approach for VR Exposure Therapy. In Proceedings of the Annual Symposium
on Computer-Human Interaction in Play (pp. 32–45). New York, NY, USA: Association
for Computing Machinery. https://doi.org/10.1145/3410404.3414222'
bibtex: '@inproceedings{Alexandrovsky_Volkmar_Spliethöver_Finke_Herrlich_Döring_Smeddinck_Malaka_2020,
place={New York, NY, USA}, series={CHI PLAY’20}, title={Playful User-Generated
Treatment: A Novel Game Design Approach for VR Exposure Therapy}, DOI={10.1145/3410404.3414222},
booktitle={Proceedings of the Annual Symposium on Computer-Human Interaction in
Play}, publisher={Association for Computing Machinery}, author={Alexandrovsky,
Dmitry and Volkmar, Georg and Spliethöver, Maximilian and Finke, Stefan and Herrlich,
Marc and Döring, Tanja and Smeddinck, Jan David and Malaka, Rainer}, year={2020},
pages={32–45}, collection={CHI PLAY’20} }'
chicago: 'Alexandrovsky, Dmitry, Georg Volkmar, Maximilian Spliethöver, Stefan Finke,
Marc Herrlich, Tanja Döring, Jan David Smeddinck, and Rainer Malaka. “Playful
User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy.”
In Proceedings of the Annual Symposium on Computer-Human Interaction in Play,
32–45. CHI PLAY’20. New York, NY, USA: Association for Computing Machinery, 2020.
https://doi.org/10.1145/3410404.3414222.'
ieee: 'D. Alexandrovsky et al., “Playful User-Generated Treatment: A Novel
Game Design Approach for VR Exposure Therapy,” in Proceedings of the Annual
Symposium on Computer-Human Interaction in Play, 2020, pp. 32–45.'
mla: 'Alexandrovsky, Dmitry, et al. “Playful User-Generated Treatment: A Novel Game
Design Approach for VR Exposure Therapy.” Proceedings of the Annual Symposium
on Computer-Human Interaction in Play, Association for Computing Machinery,
2020, pp. 32–45, doi:10.1145/3410404.3414222.'
short: 'D. Alexandrovsky, G. Volkmar, M. Spliethöver, S. Finke, M. Herrlich, T.
Döring, J.D. Smeddinck, R. Malaka, in: Proceedings of the Annual Symposium on
Computer-Human Interaction in Play, Association for Computing Machinery, New York,
NY, USA, 2020, pp. 32–45.'
date_created: 2021-02-04T09:45:38Z
date_updated: 2022-01-06T06:54:48Z
doi: 10.1145/3410404.3414222
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/abs/10.1145/3410404.3414222
page: 32–45
place: New York, NY, USA
publication: Proceedings of the Annual Symposium on Computer-Human Interaction in
Play
publication_identifier:
isbn:
- '9781450380744'
publication_status: published
publisher: Association for Computing Machinery
series_title: CHI PLAY'20
status: public
title: 'Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure
Therapy'
type: conference
user_id: '84035'
year: '2020'
...
---
_id: '21177'
abstract:
- lang: eng
text: Attention mechanisms have seen some success for natural language processing
downstream tasks in recent years and generated new state-of-the-art results. A
thorough evaluation of the attention mechanism for the task of Argumentation Mining
is missing. With this paper, we report a comparative evaluation of attention layers
in combination with a bidirectional long short-term memory network, which is the
current state-of-the-art approach for the unit segmentation task. We also compare
sentence-level contextualized word embeddings to pre-generated ones. Our findings
suggest that for this task, the additional attention layer does not improve the
performance. In most cases, contextualized embeddings do also not show an improvement
on the score achieved by pre-defined embeddings.
author:
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Jonas
full_name: Klaff, Jonas
last_name: Klaff
- first_name: Hendrik
full_name: Heuer, Hendrik
last_name: Heuer
citation:
ama: 'Spliethöver M, Klaff J, Heuer H. Is It Worth the Attention? A Comparative
Evaluation of Attention Layers for Argument Unit Segmentation. In: Proceedings
of the 6th Workshop on Argument Mining. Florence, Italy: Association for Computational
Linguistics; 2019:74-82. doi:10.18653/v1/W19-4509'
apa: 'Spliethöver, M., Klaff, J., & Heuer, H. (2019). Is It Worth the Attention?
A Comparative Evaluation of Attention Layers for Argument Unit Segmentation. In
Proceedings of the 6th Workshop on Argument Mining (pp. 74–82). Florence,
Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4509'
bibtex: '@inproceedings{Spliethöver_Klaff_Heuer_2019, place={Florence, Italy}, title={Is
It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument
Unit Segmentation}, DOI={10.18653/v1/W19-4509},
booktitle={Proceedings of the 6th Workshop on Argument Mining}, publisher={Association
for Computational Linguistics}, author={Spliethöver, Maximilian and Klaff, Jonas
and Heuer, Hendrik}, year={2019}, pages={74–82} }'
chicago: 'Spliethöver, Maximilian, Jonas Klaff, and Hendrik Heuer. “Is It Worth
the Attention? A Comparative Evaluation of Attention Layers for Argument Unit
Segmentation.” In Proceedings of the 6th Workshop on Argument Mining, 74–82.
Florence, Italy: Association for Computational Linguistics, 2019. https://doi.org/10.18653/v1/W19-4509.'
ieee: M. Spliethöver, J. Klaff, and H. Heuer, “Is It Worth the Attention? A Comparative
Evaluation of Attention Layers for Argument Unit Segmentation,” in Proceedings
of the 6th Workshop on Argument Mining, Florence, Italy, 2019, pp. 74–82.
mla: Spliethöver, Maximilian, et al. “Is It Worth the Attention? A Comparative Evaluation
of Attention Layers for Argument Unit Segmentation.” Proceedings of the 6th
Workshop on Argument Mining, Association for Computational Linguistics, 2019,
pp. 74–82, doi:10.18653/v1/W19-4509.
short: 'M. Spliethöver, J. Klaff, H. Heuer, in: Proceedings of the 6th Workshop
on Argument Mining, Association for Computational Linguistics, Florence, Italy,
2019, pp. 74–82.'
conference:
location: Florence, Italy
name: 6th Workshop on Argument Mining
date_created: 2021-02-04T14:41:58Z
date_updated: 2022-01-06T06:54:48Z
doi: 10.18653/v1/W19-4509
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.aclweb.org/anthology/W19-4509/
oa: '1'
page: 74-82
place: Florence, Italy
publication: Proceedings of the 6th Workshop on Argument Mining
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Is It Worth the Attention? A Comparative Evaluation of Attention Layers for
Argument Unit Segmentation
type: conference
user_id: '84035'
year: '2019'
...
---
_id: '16847'
abstract:
- lang: eng
text: In this work we describe our results achieved in the ProtestNews Lab at CLEF
2019. To tackle the problems of event sentence detection and event extraction
we decided to use contextualized string embeddings. The models were trained on
a data corpus collected from Indian news sources, but evaluated on data obtained
from news sources from other countries as well, such as China. Our models have
obtained competitive results and have scored 3rd in the event sentence detection
task and 1st in the event extraction task based on average F1-scores for different
test datasets.
author:
- first_name: Gabriella
full_name: Skitalinskaya, Gabriella
last_name: Skitalinskaya
- first_name: Jonas
full_name: Klaff, Jonas
last_name: Klaff
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
citation:
ama: 'Skitalinskaya G, Klaff J, Spliethöver M. CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction. Vol 2380.
Lugano, Switzerland; 2019.'
apa: 'Skitalinskaya, G., Klaff, J., & Spliethöver, M. (2019). CLEF ProtestNews
Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event
Extraction (Vol. 2380). Lugano, Switzerland.'
bibtex: '@book{Skitalinskaya_Klaff_Spliethöver_2019, place={Lugano, Switzerland},
series={CEUR Workshop Proceedings}, title={CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction}, volume={2380},
author={Skitalinskaya, Gabriella and Klaff, Jonas and Spliethöver, Maximilian},
year={2019}, collection={CEUR Workshop Proceedings} }'
chicago: 'Skitalinskaya, Gabriella, Jonas Klaff, and Maximilian Spliethöver. CLEF
ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection
and Event Extraction. Vol. 2380. CEUR Workshop Proceedings. Lugano, Switzerland,
2019.'
ieee: 'G. Skitalinskaya, J. Klaff, and M. Spliethöver, CLEF ProtestNews Lab 2019:
Contextualized Word Embeddings for Event Sentence Detection and Event Extraction,
vol. 2380. Lugano, Switzerland, 2019.'
mla: 'Skitalinskaya, Gabriella, et al. CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction. Vol. 2380,
2019.'
short: 'G. Skitalinskaya, J. Klaff, M. Spliethöver, CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction, Lugano, Switzerland,
2019.'
date_created: 2020-04-23T15:18:40Z
date_updated: 2022-01-06T06:52:57Z
extern: '1'
intvolume: ' 2380'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ceur-ws.org/Vol-2380/paper_118.pdf
oa: '1'
page: '7'
place: Lugano, Switzerland
report_number: '118'
series_title: CEUR Workshop Proceedings
status: public
title: 'CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence
Detection and Event Extraction'
type: report
user_id: '84035'
volume: 2380
year: '2019'
...
---
_id: '21173'
author:
- first_name: Michael
full_name: Bonfert, Michael
last_name: Bonfert
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Roman
full_name: Arzaroli, Roman
last_name: Arzaroli
- first_name: Marvin
full_name: Lange, Marvin
last_name: Lange
- first_name: Martin
full_name: Hanci, Martin
last_name: Hanci
- first_name: Robert
full_name: Porzel, Robert
last_name: Porzel
citation:
ama: 'Bonfert M, Spliethöver M, Arzaroli R, Lange M, Hanci M, Porzel R. If You Ask
Nicely: A Digital Assistant Rebuking Impolite Voice Commands. In: Proceedings
of the 20th ACM International Conference on Multimodal Interaction. ; 2018.
doi:10.1145/3242969.3242995'
apa: 'Bonfert, M., Spliethöver, M., Arzaroli, R., Lange, M., Hanci, M., & Porzel,
R. (2018). If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands.
In Proceedings of the 20th ACM International Conference on Multimodal Interaction.
https://doi.org/10.1145/3242969.3242995'
bibtex: '@inproceedings{Bonfert_Spliethöver_Arzaroli_Lange_Hanci_Porzel_2018, title={If
You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands}, DOI={10.1145/3242969.3242995}, booktitle={Proceedings
of the 20th ACM International Conference on Multimodal Interaction}, author={Bonfert,
Michael and Spliethöver, Maximilian and Arzaroli, Roman and Lange, Marvin and
Hanci, Martin and Porzel, Robert}, year={2018} }'
chicago: 'Bonfert, Michael, Maximilian Spliethöver, Roman Arzaroli, Marvin Lange,
Martin Hanci, and Robert Porzel. “If You Ask Nicely: A Digital Assistant Rebuking
Impolite Voice Commands.” In Proceedings of the 20th ACM International Conference
on Multimodal Interaction, 2018. https://doi.org/10.1145/3242969.3242995.'
ieee: 'M. Bonfert, M. Spliethöver, R. Arzaroli, M. Lange, M. Hanci, and R. Porzel,
“If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands,” in
Proceedings of the 20th ACM International Conference on Multimodal Interaction,
2018.'
mla: 'Bonfert, Michael, et al. “If You Ask Nicely: A Digital Assistant Rebuking
Impolite Voice Commands.” Proceedings of the 20th ACM International Conference
on Multimodal Interaction, 2018, doi:10.1145/3242969.3242995.'
short: 'M. Bonfert, M. Spliethöver, R. Arzaroli, M. Lange, M. Hanci, R. Porzel,
in: Proceedings of the 20th ACM International Conference on Multimodal Interaction,
2018.'
date_created: 2021-02-04T09:44:02Z
date_updated: 2022-01-06T06:54:48Z
doi: 10.1145/3242969.3242995
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/abs/10.1145/3242969.3242995
publication: Proceedings of the 20th ACM International Conference on Multimodal Interaction
publication_identifier:
isbn:
- '9781450356923'
publication_status: published
status: public
title: 'If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands'
type: conference
user_id: '84035'
year: '2018'
...