Discover hidden insights in customer feedback and complex qualitative data. Summetix uses proprietary Argument Mining and large language models to discover patterns and trends that may change your business.
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Journal of Service Research
Using Information-Seeking Argument Mining to Improve Service
arXiv preprint arXiv:2205.11472
On the Effect of Sample and Topic Sizes for Argument Mining Datasets
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue.
From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
KI – Künstliche Intelligenz.
Stance Detection Benchmark: How Robust Is Your Stance Detection?
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Aspect-Controlled Neural Argument Generation
AI for Social Good – AAAI Fall Symposium 2020.
Arguments as Social Good: Good Arguments in Times of Crisis
Datenbank-Spektrum 20:115–121 (2020).
ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
arxiv preprint: arXiv:2005.00084
Aspect-Controlled Neural Argument Generation. Arxiv Preprint.
arxiv preprint: arXiv:2001.01565
Stance Detection Benchmark: How Robust Is Your Stance Detection? Arxiv Preprint.
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA
Fine-Grained Argument Unit Recognition and Classification. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA.
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems. In: Proceedings of Language Resources and Evaluation Conference (LREC 2020), Marseille, France.
Association for Computational Linguistics, Florence, Italy
Classification and Clustering of Arguments with Contextualized Word Embeddings. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
arxiv preprint: arXiv:1904.09688, 10 pages
Robust Argument Unit Recognition and Classification. Arxiv Preprint.
Association for Computational Linguistics, Brussels, Belgium, pages 3664–3674
Cross-topic Argument Mining from Heterogeneous Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Association for Computational Linguistics, Brussels, Belgium, pages 131–143
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection. In 5th Workshop on Argument Mining at the 2018 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics, Brussels, Belgium, pages 144–154
Cross-Lingual Argumentative Relation Identification: from English to Portuguese. In 5th Workshop on Argument Mining at the 2018 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics, Santa Fe, NM, USA, pages 831-844
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018).
Association for Computational Linguistics, New Orleans, LA, USA, pages 21–25
ArgumenText: Searching for Arguments in Heterogeneous Sources. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Demo).
Association for Computational Linguistics, New Orleans, LA, USA, pages 35–41
Multi-Task Learning for Argumentation Mining in Low-Resource Settings. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
arxiv preprint: arXiv:1802.05758
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks. Arxiv Preprint.
Association for Computational Linguistics, Copenhagen, Denmark, pages 2045–2056.
What is the essence of a claim? Cross-domain claim identification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics, Vancouver, Canada, pages 11–22.
Neural end-to-end learning for computational argumentation mining. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Computational Linguistics 43(3):619–659.
Parsing argumentation structures in persuasive essays.
