@inbook{32179,
  abstract     = {{This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.}},
  author       = {{Kersting, Joschka and Ahmed, Mobeen and Geierhos, Michaela}},
  booktitle    = {{HCI International 2022 Posters}},
  editor       = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula}},
  isbn         = {{9783031064166}},
  issn         = {{1865-0929}},
  keywords     = {{On-The-Fly Computing, Chatbot, Knowledge Base}},
  location     = {{Virtual}},
  pages        = {{419----426}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Chatbot-Enhanced Requirements Resolution for Automated Service Compositions}}},
  doi          = {{10.1007/978-3-031-06417-3_56}},
  volume       = {{1580}},
  year         = {{2022}},
}

@inproceedings{26049,
  abstract     = {{Content is the new oil. Users consume billions of terabytes a day while surfing on news sites or blogs, posting on social media sites, and sending chat messages around the globe. While content is heterogeneous, the dominant form of web content is text. There are situations where more diversity needs to be introduced into text content, for example, to reuse it on websites or to allow a chatbot to base its models on the information conveyed rather than of the language used. In order to achieve this, paraphrasing techniques have been developed: One example is Text spinning, a technique that automatically paraphrases text while leaving the intent intact. This makes it easier to reuse content, or to change the language generated by the bot more human. One method for modifying texts is a combination of translation and back-translation. This paper presents NATTS, a naive approach that uses transformer-based translation models to create diversified text, combining translation steps in one model. An advantage of this approach is that it can be fine-tuned and handle technical language.}},
  author       = {{Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos, Michaela}},
  booktitle    = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}},
  keywords     = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}},
  location     = {{Lisbon, Portugal}},
  pages        = {{221----225}},
  publisher    = {{IADIS}},
  title        = {{{IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING}}},
  year         = {{2021}},
}

@inproceedings{18686,
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon}},
  booktitle    = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020}},
  keywords     = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}},
  location     = {{Lisbon, Portugal}},
  pages        = {{119----123}},
  publisher    = {{IADIS}},
  title        = {{{SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH}}},
  year         = {{2020}},
}