Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues (2024)

Abstract: Implicit user feedback, user emotions and demographic information have shown to be promising sources for improving the accuracy and user engagement of responses generated by dialogue systems. However, the influence of such information on task completion and factual consistency, which are important criteria for task-oriented and document-grounded dialogues, is not yet known. To address this, we introduce FEDI, the first English task-oriented and document-grounded dialogue dataset annotated with this information. Our experiments with Flan-T5, GPT-2 and Llama 2 show a particularly positive impact on task completion and factual consistency. Participants in our human evaluation reported that the responses generated by the feedback-trained models were more informative (Flan-T5 and GPT-2), relevant and factual consistent (Llama 2).

## introduction implicit user feedback @xcite @xcite , such as clarification questions, user emotions @xcite @xcite and demographic information @xcite , such as age or language style, are promising sources for improving the accuracy and user engagement of responses generated by dialogue systems. for example, in the second utterance of figure 1 , the system generates a response unrelated to the user's question, which affects her emotional state. she asks the system for clarification, getting a more satisfying response. this makes her happy and she continues the conversation. however, we do not know to what extent the generated response contributes to achieving the user's goal and reflects the underlying knowledge source. this is commonly referred to as task completion and factual consistency. both are important criteria for task-oriented and document-grounded dialogue systems @xcite @xcite , but the impact of implicit user feedback, emotions and demographic information on them is an open research question. to address this gap, we introduce the fedi dataset. following recent research that includes information-seeking in task-oriented dialogues @xcite @xcite , e.g., for handling multi-domain scenarios, fedi provides annotations for required knowledge documents and is the first english task-oriented and document-grounded dialogue dataset annotated with implicit user feedback, emotions and demographic information. fedi allows us to investigate the impact of this information on task ## related work learning from user emotions and demographic information can improve generation accuracy and user engagement in dialogue systems @xcite @xcite . the same applies to implicit user feedback, which usually requires user interaction for data collection and continual learning @xcite @xcite . to investigate the impact of implicit user feedback, emotions and demographic information in task-oriented and document-grounded dialogues, we create fedi by combining the best of both worlds. we use gpt-3.5 to generate training and validation data and recruit human annotators for dialogue quality assessment, annotation curation, and collection of a separate set of test dialogues. ## fedi self-service terminals are increasingly common in the service sector, including postal services, access controls (e.g., to security-critical areas or in the hospitality industry), or customer service @xcite @xcite . they implement specific workflows to serve customers and support employees. fedi covers four use cases from these domains. for postal sevices, we include customer support for parcel shipping and topping up a prepaid sim card. for receptionist and insurance services, we include one use case each, i.e., access control (the reception and registration of new visitors in office buildings) and question answering (in the context of financial topics and pet, health and heritage insurance). the question answering dialogues are additionally annotated with knowledge documents. appendix a describes the tasks in more detail, including slots (information required for task completion), intents and document sources. implicit user feedback (ge, f ) we use the taxonomies proposed by @xcite to generate and annotate generation errors (ge) and subsequent implicit user feedback (f ). they distinguish ten types of generation errors. nine of which are relevant for fedi, such as attribute error, factually incorrect or lack of sociality. for implicit user feedback, they distinguish five types, e.g., ask for clarification, ignore and continue and repeat or rephrase. definitions, further details and examples can be found in appendix b. ## framework for generating and annotating dialogues figure 2 gives an overview of our framework for generating and annotating dialogues. we distinguish feedback-free and feedback dialogues, i.e., dialogues that provide annotations for generation errors and implicit user feedback. for each step, we require gpt-3.5 to return the results in a predefined json scheme. if in one step the generation does not match this requirement, the whole dialogue is discarded. we provide more details, including the instructions used in this procedure, in appendix c. ## fedi analysis fedi consists of 8,852 dialogues, divided into 1,988 feedback-free dialogues, including 326 for testing, and 6,864 feedback dialogues (1,716 in four versions). the test dialogues were collected human-human by eight computer science students. in the following, we focus on the completeness of generated slot and intent annotations, the distribution of user emotions and the feedback scenarios represented in the dialogues. we provide additional statistical analysis in appendix e, including split sizes and the distribution of demographic information. details on recruitment, salary, procedure, and our experiences and findings from collecting and annotating dialogue data with humans vs. llms can be found in appendix d. ## quality control for fedi we asked two participants from our test data collection to assess and curate the intent, slot and emotion annotations in 480 feedback-free dialogues and the generation error and implicit user feedback type annotations in 380 feedback dialogues (see appendix d.3 for the procedure). the dialogues were randomly sampled from the train and dev splits of fedi. we used inception @xcite as a platform for this study. we calculate the interannotator agreement (iaa) using krippendorff's alpha @xcite with a nominal weighting function (as provided in the platform). overall, the ratio of dialogues with at least one missing annotation is rather low, except for slot annotations. we found that most of them are parcel shipping dialogues, which has a comparatively complex annotation scheme (see appendix a). a detailed analysis revealed that an average of 1.8 annotations were added to these dialogues. for the dialogues with at least one changed annotation, annotators reported that in many of these cases placeholders, e.g., the slot name put in brackets ([shipping_box_name]), were used instead of the slot values from the dialogues. we attribute this to our observation from section 5 (gpt-3.5 strictly follows the slot annotation scheme, even if the values are not in the dialogue). emotions, whose perception is very subjective, are the most frequently changed annotation type (on average 2.09 times per affected dialogue), whereby the originally annotated emotion was sometimes not part of our taxonomy (hallucination). we provide further analysis of the impact of human curation on data quality in appendix e.1. ## experiments and results we conduct experiments using three models of different architecture and pretraining approaches, including flan-t5 (chung et al., 2022) (780m), gpt-2 @xcite (780m) and llama 2 @xcite (7b, plain pretrained version) 10 . we first finetune the pretrained models using the feedback-free dialogues (baselines) and include the gold user emotions, demographic information and documents as part of the input sequences. we use the gold annotations of these signals to avoid bias from external components, such as emotion classifiers or document retrievers. for llama 2, we only finetune the lora @xcite weights. we also provide in-context results for this model. we use the best feedback-free models for experiments with the feedback dialogues, in which we include the generation error and the user feedback utterance in the input sequence. we provide additional details in the appendix, including hyperparameters and data configuration for feedback training (f.1) and input sequences (f.2). we also provide results for experiments using llama 3 (dubey et al., 2024) (f.5), which was published shortly after we had completed our main experiments, and continual learning from feedback data (h). ## conclusion we introduce fedi, the first english task-oriented and document-grounded dialogue dataset annotated with implicit user feedback, user emotions and demographic information. our analysis shows the usefulness of our framework for generating feedback-annotated dialogues from various domains, and that fedi is comparable to other related datasets. our experiments show that learning from implicit user feedback improves task completion and factual consistency. humans perceive the responses generated by feedback models as more informative (flan-t5 and gpt-2), more relevant and more factually consistent (llama 2). however, our results also show room for improvements in future work, e.g., the varying impact of learning from errors and feedback data on the generated responses and how they are perceived by humans.