Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation (2021)

Abstract: We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.

## introduction text classifiers play an important role in filtering inappropriate products on e-commerce platforms. product moderators are dependent on classifiers that have been trained on up-to-date labeled data in order to keep pace with policy changes and new instances of inappropriate products. for example, amazon had to take fast action to remove offensive t-shirts during the 2020 us election @xcite and overpriced items and fake cures during the covid-19 pandemic @xcite . in this paper, we carry out a case study at a large e-commerce platform. we investigate an approach that allows moderators to rapidly steer the creation of labeled training data, thereby enabling close control of moderation classifiers. our approach makes use of a recently-introduced technique called data programming @xcite , which generates classifier training data on the basis of rules that have been specified by domain experts (platform moderators). data programming eliminates the need to individually hand-label training data points. we propose a feedback loop that selects subsets of data, called inspiration sets, that are used by moderators as the basis for updating an initial or existing set of rules. we investigate whether inspiration sets can be selected in an unsupervised manner, i.e., without ground truth. the contribution of our case study is insight into how to support moderators in updating the rules used by a data programming pipeline in a realworld use scenario requiring fast control (i.e., imposing time constraints). our study is carried out in collaboration with professional moderators at bol.com, a large european e-commerce company. in contrast to our work, most papers on product moderation, such as @xcite , do not obviously take an inside perspective. most previous studies of data programming, such as ehrenberg et al. @xcite , have looked at user control, but not at fast control, i.e., the ability to update rules quickly in order to steer the training data. because of the sensitive nature of the work of the platform moderators, our case study is written with a relatively high level of abstraction. we cannot reveal the exact statistics of inappropriate items on the platform. the rules formulated by the moderators are largely based on keywords occurring in the text of product descriptions, but it is not possible to state them exactly. nonetheless, we find that we are able to report enough information to reveal the potential of inspiration sets for fast control of inappropriate products on e-commerce platforms. this paper is based on a collaborative project with bol.com. further analysis and experimental results are available in the resulting thesis @xcite . ## related work most work on product moderation @xcite @xcite focuses on products sold on social media. in contrast, we study an e-commerce platform from the inside. like social media moderation, we face the challenge of lexical variation of keywords, cf. @xcite . our study is related to work investigating applications of data programming to a specific problem. such work includes examples from the medical domain @xcite @xcite @xcite , multi-task learning @xcite , information extraction @xcite , and learning discourse structure @xcite . like our work, such work often adjusts the snorkel framework @xcite for the task at hand. previous work has proposed a variety of methods for giving users (who are in our case the product moderators) control over classifiers by making use of a pipeline that allows them to provide feedback about training data labels and classification results. in wesal @xcite user feedback improves the labels that sets of rules assign to data points. in contrast, our focus is on feedback that allows moderators to improve the rules directly. in this respect, our work is related to @xcite , which was, to our knowledge, the first to discuss how rules in a data programming pipeline can be improved using sampled data as feedback. socratic learning @xcite considered the issue of users implicitly focusing on subsets of data when they formulate rules, limiting the ability of the data programming pipeline to generalize to data outside of these subsets. we are working under time-constrained conditions. there are two constraints. first, our moderators are given a limited amount of time to formulate the initial rules. they formulate the rules themselves based solely on their domain expertise and experience, which allows them to work quickly. in contrast, in work such as @xcite and @xcite , users consult labeled data to formulate the initial rules. second, our moderators have limited time to revise the initial rules. in this step, they consult data in the form of inspiration sets. wu et al. (2018 ## approach in this section, we describe the data programming pipeline and also our experiment with inspiration sets, which investigates the potential for fast control of training data for moderation classifiers. ## results and discussion we analyze how the inspiration sets impact the quality of our data. our results suggest two findings that have, to our knowledge, not been previously documented. first, professional content moderators do not necessarily need labeled sample data to write rules for a data programming pipeline, but instead come quite far relying only on domain knowledge and experience (cf. "initial" in we also found that for most categories, a considerable amount of training data (31-56%) received only abstains (see appendix b for more details). this observation is consistent with previous work, e.g., that of @xcite , which has noted that lf sets rarely reach complete coverage. in general, a small number of rules tend to cover a large portion of the data. the majority of rules had a low precision, and a small number of rules had high recall. possible reasons are that product moderators tried not to miss out on inappropriate products, or that they had set of specific data points in mind during lf definition, as suggested by @xcite . we also noticed that moderators added and changed, but did not delete rules. in fact, we only observed a single case of a rule being deleted. more research is necessary to understand if this reflects high confidence in the initial choices, or a default thinking pattern, as studied by @xcite . finally, we observe it is important not to assume that each newly added rule yields improvement: rule interactions are also important. a more detailed analysis of the changes brought about by the inspiration sets for two representative cases is included in appendix c. ## conclusion and outlook our case study has shown our data programming pipeline can generate labeled data for moderation classifiers in a fraction of the time needed for hand labeling (90 min. vs. a week or more of effort). we have seen that moderators can create effective rules based on their domain knowledge and experience, plus a short exposure to an unsupervised inspiration set. labeling data by hand in order to create supervised inspiration sets may not be worth the effort. our observations suggest that it is important that moderators not only write rules, but also continue moderating so that they can gain expertise and also be able to update rules quickly in response to changes in the domain, i.e., a new type of offensive clothing items, as in @xcite . we hope that our work will inspire research on data programming in domains in which fast response to inappropriate products or content is needed. future research could seek to understand the ability of moderators to predict the interaction of rules and why they seem hesitant to discard rules once they have created them.