Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples 9 Conclusion In this work, we examined the dialogs of six datasets from various types, including MultiWoZ, SGD, BABI, PersonaChat, Wizards-of-Wikipedia, and the human-bot split from the Self-Feeding Chatbot, for errors in system utterances and the types of subsequent user responses to assess their extendibility with annotations for learning from free-text human feedback. Our results show that this largely depends on whether the dialogs are human-human or human-bot, and whether they are task-oriented, open-domain, or knowledgegrounded. We found that human-bot dialogs, contain more errors in system utterances that are addressed with free-text human feedback in subsequent user responses, especially in the case of opendomain and knowledge-grounded dialogs. Therefore, it might be feasible to extend these datasets with the needed annotations to support research into methods for learning from free-text human feedback, e.g., by taking advantage of the recent developments in synthetic data generation. We also used the insights gained during this process to propose a new user response type taxonomy and a modified Integrated Error Taxonomy for the annotation of free-text human feedback. Our experiments show that including errors from system utterances and subsequent user responses has a positive impact in response generation. 10 Limitations The majority of our evaluation was done manually. Therefore, with respect to the original dataset sizes, we only consider a small fraction of the data in our study. It might be possible that our results would have been clearer when we would have considered more dialogs for the collection of error-indicating sentences. However, our analysis shows that errors found in the randomly selected dialogs are mostly ignored by the user, i.e., the user does not provide free-text human feedback that could be used for learning. Thus, as far as we are concerned, this does not limit the meaningfulness of our results. Regarding dataset selection, our corpus study (and its results) have only limited expressiveness for knowledge-grounded dialog datasets, since we only consider one of such datasets in our study, Wizards-of-Wikipedia (Dinan et al., 2019). However, this does not affect the relevance of our work, as there are already free-text human feedback annotated datasets available, e.g., FITS (Xu et al., 2023), and we considered a representative number of datasets from other dialog types for which there is a lack of publicly available feedback-annotated datasets, such as task-oriented dialogs. The taxonomies used in this work are also subject to limitations. In the case of the modified Integrated Error Taxonomy, our results show that it improves agreement across different dialog types. However, its abstract error types might limit application for specific use cases, e.g., for a more fine-grained consideration of different types of social errors. 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