Reducing cohort bias in natural language understanding systems with targeted self-training scheme (2023)

Abstract: Bias in machine learning models can be an issue when the models are trained on particular types of data that do not generalize well, causing under performance in certain groups of users. In this work, we focus on reducing the bias related to new customers in a digital voice assistant system. It is observed that natural language understanding models often have lower performance when dealing with requests coming from new users rather than experienced users. To mitigate this problem, we propose a framework that consists of two phases (1) a fixing phase with four active learning strategies used to identify important samples coming from new users, and (2) a self training phase where a teacher model trained from the first phase is used to annotate semi-supervised samples to expand the training data with relevant cohort utterances. We explain practical strategies that involve an identification of representative cohort-based samples through density clustering as well as employing implicit customer feedbacks to improve new customers’ experience. We demonstrate the effectiveness of our approach in a real world large scale voice assistant system for two languages, German and French through both offline experiments as well as A/B testings.

## introduction deep machine learning models tend to inherit the bias existing in the datasets used for training @xcite . for example, gpt-3 a state of the art in contextual language model, showed bias regarding religion, race and gender @xcite . even though deep learning models are trained on large amounts of data, it is hard to capture all the variations of the language that different users can use. even within the same language people talk differently, depending on the age group, part of the country, background, etc @xcite @xcite . if the training data is skewed towards a certain demographic group, this can cause models to pick up on patterns that do not generalize and underperform on certain user groups. bias on predictive models is an issue that has been studied for some time. most of the related literature is focused on social bias, specially gender and race @xcite and centered on measuring an specific type of bias and providing contra measures for it, which usually do not generalize to other types of bias @xcite @xcite @xcite . for example, on digital assistants, we identify other types of group bias, like customer tenure. everyday, new customers join services like amazon alexa, siri or google home. these new customers experience digital assistants for the first time and interact with it differently than mature cohort. new customers tend to try out more different functionalities, while mature customers often use utterances that work for them and settle down in daily-related domains. the experience of new customers is a closer reflection of how natural communication looks like as they are not yet "taught" how to communicate with the devices. learning from new customers therefore might be one of the best ways to learn natural interactions with digital assistants. contrary to most studies that focus on using semi-supervised learning for general accuracy @xcite ) @xcite @xcite , we focus on improving the accuracy of the new customer (early cohort) natural language understanding task @xcite and show that our framework could target a strategic customer cohort to improve their experiences, thus improve the overall accuracy in all customers in an industry scale experiment. even so, our approach can be easily applied to any customer cohort to mitigate other types of bias. our proposal consists in a method to identify important utterances coming from the early cohort that need to be fixed, then em-ploy self training techniques to mitigate them. the main idea is to automatically expand the training data to increase the representativeness of utterances that characterize early cohort customers. ## related work detecting and mitigating bias in model predictions have attracted a lot of studies recently. for example, @xcite proposed a bias detection technique based on clustering. their approach focuses in local bias detection, which refers to bias exhibited in a neighborhood of instances rather than on the entire data. @xcite did a survey on bias in deep nlp, where they present a review of the state-of-the-art in bias detection, evaluation and correction, where they used vector space manipulation @xcite , data augmentation, data manipulation or attribute protection for dealing with the bias. @xcite proposed a predictive bias framework for nlp and identified four potential origins of biases: label bias, selection bias, model over-amplification, and semantic bias. to mitigate model bias, common methods such as adversarial learning @xcite , data augmentation with synthetic data generation using back translation @xcite , pretrained language model @xcite @xcite @xcite and semi-supervised learning @xcite @xcite have shown to be effective, especially when there is a lack of labeled data. while most of these studies focus on general biases in training models, we specifically aim at new customer cohort in a real world large scale voice assistant system. we employ both active and semisupervised learning approaches that take customer feedback into consideration to improve the model prediction on this specific cohort. early cohort is defined as a group of new customers who have started to use the voice assistant device within the last 7 days. in contrast, mature cohort refers to the group of customers that have used the device for at least more than 30 days. typically, a voice assistant consists of different components, starting from wakeword detection, to automatic speech recognition (asr) that con-verts voice signals to texts, which will be used by the natural language understand (nlu) component. in this work, we focus on how improving the nlu part could help to improve the end to end experience of new customers. in this study, we use devices' response results and weak signals as a way to improve the system over time. in particular, friction is defined as commands from customers that the system failed to provide an answer to (e.g., when the system gives responses such that "sorry i do not understand"). we also consider negative feedback from customers as a signal that the system did not response well to their previous requests. finally, in order to measure the impact of our approach, we carried out offline nlu experiments (testing on annotated data). the only change is the implemented early cohort self training scheme. ## our approach we propose an end to end framework (figure 1 ) to identify cohort representativeness and effective data selection and augmentation to improve the model performance on a specific cohort without degrading the overall performance. it is composed of two phases, with the first phase looks for utterances from early cohort that need to be fixed using active learning using different strategies. after these utterances are annotated with human annotators, they are included in the training data to train a new nlu teacher model. we then employ a self learning phase to further extend similar utterances using semi-supervised learning to have more representatives of samples coming from early cohort. ## results we report offline results testing on annotated test data. ## conclusions in this work, we provide an end to end framework for bias mitigation with a focus on early cohort. this framework is also general enough to apply to other customer cohorts and other types of bias. our approach uses a combination of active and semisupervised learning techniques in a self-learning scheme for effective data selection and augmentation. our main contribution is the identification