Multimodal Contextual Dialogue Breakdown Detection for Conversational AI Models (2024)

Abstract: Detecting dialogue breakdown in real time is critical for conversational AI systems, because it enables taking corrective action to successfully complete a task. In spoken dialog systems, this breakdown can be caused by a variety of unexpected situations including high levels of background noise, causing STT mistranscriptions, or unexpected user flows.In particular, industry settings like healthcare, require high precision and high flexibility to navigate differently based on the conversation history and dialogue states. This makes it both more challenging and more critical to accurately detect dialog breakdown. To accurately detect breakdown, we found it requires processing audio inputs along with downstream NLP model inferences on transcribed text in real time. In this paper, we introduce a Multimodal Contextual Dialogue Breakdown (MultConDB) model. This model significantly outperforms other known best models by achieving an F1 of 69.27.

## introduction dialogue breakdown detection is important in industry settings, because it allows the system to correct for mistakes in real time. while it is even better to avoid dialogue breakdown to begin with, in many industry settings there are components of the pipeline that have noise in real world settings. for example, the vendor or system making a voice call could drop some audio packets. the asr vendor or model could miss some transcripts or have very noisy transcripts, especially when the user is in a setting with a lot of background noise or using a phone line with a poor network. with dialogue breakdown, we can detect that there was likely some missing context and say something like "sorry i missed that, could you repeat yourself?" to get the conversation back on track. while dialogue breakdown is a challenging problem in general, there are some unique challenges in industry settings. in professional settings, users do not use as much explicit language or profanities. instead of detecting this strong language, we often need to rely more on tone or cadence to detect user frustration. additionally, there is low tolerance for incorrect responses. for example, in the healthcare domain a failed conversation could affect the time it takes for a patient to receive treatment. finally, some industry use cases, including ours, have very complex and varied flows. for example, the average conversation in our domain consists of about 100 turns and context from early in the conversation can affect the flow even at the end of the conversation. there are additionally unique challenges for detecting dialogue breakdown in phone call settings. over the phone, there are strict latency requirements (e.g. delayed or repeatedly incorrect responses can cause frustration or even hang ups from users interacting with the system). in contrast, text-based chatbot systems often have visual feedback to indicate processing time and often target users and use cases with more leniency around latency and potential hallucinations. thus, detecting dialogue breakdowns in a timely manner is crucial for real-time conversational speech ai systems. it is an extremely challenging task because there are multimodal factors in different components of the system pipeline which can appear as diverse downstream issues. we found that prior state of the art models were not able to accurately capture dialogue breakdown in our industry setting. in this paper, we propose a new model which uses audio and text signals to predict dialogue breakdown generalizable to various industry use cases. ## related work the dialogue breakdown detection challenge (dbdc) has been a pivotal platform for advancing research in this area @xcite . @xcite defines the task description, datasets, and evaluation metrics for dbdc and provides insights into the design and methods used in these challenges. these challenges involve detecting inappropriate system utterances that lead to dialogue breakdowns in chat, utilizing datasets composed of chat dialogues with annotated breakdown instances. the methodologies employed range from traditional machine learning techniques to advanced neural network models. @xcite explore different variants of lstm for dialogue breakdown detection. this work highlights the exploration of different model types and word embeddings, adding depth to the understanding of how various machine learning models and linguistic features can be utilized for breakdown detection. @xcite demonstrate a novel approach on dialogue breakdown detection by integrating bert's powerful language understanding capabilities with traditional dialogue features like dialogue acts. this hybrid approach aims to capture the nuances of conversational flow and detect potential breakdowns more effectively. another significant contribution in this field is the exploration of semi-supervised learning methods to improve dialogue breakdown detection, as discussed in @xcite . their research demonstrates the use of continued pre-training on the reddit dataset and a manifold-based data augmentation method, showing a substantial improvement in detecting dialogue breakdowns. the findings across these papers consistently indicate that the integration of advanced language models with contextual and conversational features significantly enhances the detection of dialogue breakdowns. there were a few approaches using acoustic signals or multimodality in previous related challenges @xcite @xcite . for more related work to our approach, @xcite used the output from automatic speech recognition system (asr) systems as features to detect dialogue breakdowns from spoken dialogues but they used only surface forms of stt texts or extracted text features from them rather than latent vectors of acoustic signals directly. also, @xcite utilized acoustic features and found that they can classify nonbreakdown dialogues and awkward conversation flows better than traditional text features but they used manually designed feature vectors extracted from emotion challenge dataset @xcite . in this paper, we explore novel multimodal architectures and the most recent state-of-the-art approaches for dialogue breakdown detection using both text and audio signals of real-time conversations in industry settings. we propose a model which uses deeper contextual signals across both audio and text inputs than prior works. this system is able to capture dialogue breakdowns in phone conversations in industry settings. ## results and analysis we conducted dialogue breakdown classification tasks using the previous state-of-the-art models and multcondb and analyzed multcondb to investigate its inference process and capability with qualitative visualization analysis. ## conclusion as the first work of multimodal dialogue breakdown detection in healthcare industry settings, we explored various approaches leveraging both audio and text and developed a high-performance multimodal model (@xmath0.27) which generalized well to various types of context of conversations driven by conversational ai models across multiple version releases in industry settings. additionally, we conducted thorough qualitative model analysis which provided insights with its output patterns which can cluster dialogue breakdown samples and their categories in separate groups. we hope the strong results of our dialogue detection approach here leads to more reliable conversational ai model development in the future research.