Reinforced Multiple Instance Selection for Speaker Attribute Prediction (2024)

Abstract: Language usage is related to speaker age, gender, moral concerns, political ideology, and other attributes. Current state-of-the-art methods for predicting these attributes take a speaker’s utterances as input and provide a prediction per speaker attribute. Most of these approaches struggle to handle a large number of utterances per speaker. This difficulty is primarily due to the computational constraints of the models. Additionally, only a subset of speaker utterances may be relevant to specific attributes. In this paper, we formulate speaker attribute prediction as a Multiple Instance Learning (MIL) problem and propose RL-MIL, a novel approach based on Reinforcement Learning (RL) that effectively addresses both of these challenges. Our experiments demonstrate that our RL-based methodology consistently outperforms previous approaches across a range of related tasks: predicting speakers’ psychographics and demographics from social media posts, and political ideologies from transcribed speeches. We create synthetic datasets and investigate the behavior of RL-MIL systematically. Our results show the success of RL-MIL in improving speaker attribute prediction by learning to select relevant speaker utterances.

## introduction examining and quantifying the connection between individuals' language usage and their psychological and demographic attributes has emerged as a focal area of research, often referred to as 'speaker attribute prediction'. previous studies have found relationships between language usage and age in blogs @xcite ; personality dimensions and moral concerns in facebook posts @xcite ; and political ideology in twitter posts @xcite among other attributes (e.g., @xcite @xcite . in datasets for predicting speaker attributes, observations typically consist of multiple instances of figure 1 : illustration of the relationship between speaker attributes and social media posts, where (1) not all posts are related to a speaker attribute and (2) some are related to more than one attribute. to overcome the limitations posed by bags of many instances, we propose a novel mil approach based on reinforcement learning (rl) that learns to select relevant instances for the downstream model. the rl component, responsible for the instance selection, not only reduces the number of instances fed into the models but also ensures that relevant instances to the target speaker attribute are present in the down-sampled bags. we first formalize speaker attribute prediction as a mil task (section 3.1) and introduce rl-mil, our rl-based approach for handling large bags in mil (section 3.4). we then show that rl-mil consistently outperforms other mil and non-mil variants on a wide range of real-world datasets for speaker attribute prediction (section 5.1). finally, in section 5.2, we construct a synthetic dataset where we have full control over the number of instances relevant to the speaker attribute and utilize it to understand and analyze the efficacy of our rl-mil approach. overall, this work establishes mil as an important framework for predicting speaker attributes and develops an rl methodology that addresses the main limitations of mil in this context. the work in this paper can be widely applied in speaker attribute prediction across domains, and our rl method can be expanded to provide exemplar-based explanations foot_0 . ## methods first, we formalize speaker attribute prediction as a mil problem and discuss our baselines including four core mil methods. then, we detail our proposed rl method for improved data selection, which is combined with each of the mil methods in a model-agnostic way. ## conclusion & discussion in conclusion, we formulate speaker attribute prediction as a mil problem and identify two key challenges faced by mil models for this task due to the sparsity of signal. speakers typically produce a large number of utterances, but only a few of these utterances are indicative of each speaker's attribute. however, mil models are limited in their input capacity and do not readily have the ability to choose which utterances to use for prediction. to address these challenges, we proposed rl-mil, a novel rl-based data selection framework for instance selection. we designed our framework to learn an rl model for data selection in conjunction with the mil model responsible for speaker attribute prediction. to enable rl-mil to discern relevant instances for each speaker attribute from a large pool of speaker utterances, we designed our data selection loss to reward the selection of bags that yield more accurate predictions. we put our method to a real-world test and experimented with predicting eight different speaker attributes, namely, gender, age, political ideology, and moral concerns from political speeches and facebook posts. furthermore, we created a synthetic dataset to study the success of our proposed framework and investigate the impact of signal sparsity on various rl approaches. our experimental results demonstrate the efficacy of rl-mil, especially in scenarios with sparse signals. our approach can be easily applied to other mil problems characterized by signal sparsity, such as clinical reports that consist of multiple text reviews per patient, where not all information is pertinent to a given condition. our code base and experimental setup provide the ground for future work to explore the potential application of our approach in such contexts. while our work highlights the potential of rl for instance selection in mil, due to space constraints, we did not exhaustively explore all variations of rl. subsequent research could delve into alternative rl designs, including the rl model and loss function, for this setup. particularly, optimizing rl in conjunction with mil can be challenging, and future work can focus on refining this integration.