Multi-Task Learning for Depression Detection in Dialogs (2022)

Abstract: Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC and DailyDialog corpora – both contain dialogs in English – and show important improvements over state-of-the-art on depression detection (at best 70.6% F1), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.

## introduction depression is a serious mental disorder that affects around 5% of adults worldwide. 1 it comes with multiple causes and symptoms, leading to major disability, but is often hard to diagnose, with about half the cases not detected by primary care physicians @xcite . automated detection of depression, sometimes associated to other mental health disorders, has been the topic of several studies recently, with a particular focus on social media data and online forums @xcite @xcite @xcite @xcite . the ultimate goal of such system would be to complement expert assessments, but such empirical studies are also valuable to better understand how communication is affected by health disorders. in this paper, we propose to investigate depression detection within dialogs, a scenario less studied but more similar to the interviews with clinicians, which allegedly involves dialog features and also allows to examine how interaction is affected. however, depression detection suffers from data sparsity. in fact, using social media data was a way to tackle this issue, including considering data generated by self-diagnosed users -a method that leads to potentially noisy data and comes with ethical issues @xcite . we rather examine a dataset of 189 clinical interviews, the daic-woz @xcite , collected by experts to support the diagnosis of distress conditions. participants are identified as depressive or not, and if so they receive a severity score. a line of work proposed to overcome data scarcity by leveraging varied modalities, e.g., using audio as in al @xcite . previous approaches were solely based on textual information relied on hierarchical contextual attention networks on word and sentence-level representations @xcite , or multi-task learning (mtl) but limited to combing identification and severity prediction @xcite , possibly with emotion @xcite . inspired by the latter approaches, we also propose relying on the mtl framework to help our model leverage information from different sources. we exploit three auxiliary tasks: emotion classification -naturally tied to mental health states -, and dialog act and topic classification, hoping the shallow information about the dialog structure could further enhance the performance. our architecture is classic, based on hard-parameter sharing @xcite , simpler than the shared-private architecture in @xcite but has shown effective. in order to take into account dialog organization, we advocate for a dialog-tailored hierarchical architecture with some tasks performed at the speech turn level and others at the document level. our contributions are: (i) an empirical study on depression detection in dialogs, leveraging the power of multi-task learning to deal with data sparsity; (ii) an extension of previous work in examining the effects of depression on dialog structure via shallow markers, i.e., dialog acts and topics, as a first step; (iii) state-of-the-art results on depression detection in daic test set with 70.6% in f 1 at best. ## related work within multi-task learning (mtl), a model has to learn shared representations to generalize the target task better. it improves the performance over single-task learning (stl) by leveraging commonalities or correlations between tasks. recent years have witnessed a series of successful applications in various nlp tasks, as in @xcite ; @xcite ; @xcite ; @xcite , which demonstrate the effectiveness of mtl in learning information from different but related sources. it also tackles the data sparsity issue and reduces the risk of overfitting @xcite @xcite . @xcite demonstrated the benefit of mtl for specific pairs of close health prediction tasks on tweets. @xcite used mtl on social media data and achieved important improvements in predicting several mental health signals, including suicide risks, depression, and anxiety, together with gender prediction. with a focus on depression detection, the shared task avec in 2016 @xcite has brought out a series of multi-modal studies using vocal and visual features on the daic-woz dataset @xcite . (2019, 2020 ) proposed mtl approaches in adding emotion intensity and depression severity (i.e., a regression problem) prediction to the main classification task. they, however, found that the emotionunaware model obtained the best result. they used a monologue corpus for the emotion task, a domain bias that possibly harms the performance. on the contrary, we hypothesize that emotional information would benefit depression detection. @xcite used a hierarchical contextual attention network with static word embeddings within a single-task setting and then combined representations at the word and sentence levels. they reported at best 63% in f 1 . recently, xezonaki et al. (2020) presented even better results, 70% in f 1 , by augmenting the attention network with a conditioning mechanism based on effective external lexicons and incorporating the summary associated with each interview. we instead rely on mtl in this work, where incorporating external sources is more direct. none of the previous studies investigated potential links between depression and dialog structure. we note that @xcite explored mtl with sentiment foot_1 and dialog act prediction on mastodon (a twitter-like dataset), where both annotations are available, and found a positive correlation. to the best of our knowledge, we are the first to tackle depression detection in dialog transcriptions with the mtl approach and explore joint learning techniques with tasks related to the dialog structure. ## model architecture one condition generally assumed for success within mtl, at least in nlp, is that the primary and auxiliary tasks should be related @xcite . the emotion-related task is thus a natural choice since it is linked to mental states. we hypothesize that depressive disorder can also affect how people interact with others during conversations. we thus take a first step toward linking dialog structure and depression by examining shallow signals: dialog acts and topics. in addition, since the information comes at different levels, we propose hierarchical modeling, from speech turns to documents. baseline model: our basic model is a two-level recurrent network, similar to the one in @xcite . the input words are mapped to vectors using word embeddings from scratch. the first level (turn-level) takes the embeddings into a bilstm network to obtain one vector for each turn. the second level (dialog-level) takes a sequence of turns into an rnn network, and the output is finally passed into a linear layer for depression prediction. ## conclusion in this paper, we demonstrate the correlation between depression and emotion and show the relevance of features linked to dialog structures via shallow markers: dialog acts and topics. in the near future, we intend to investigate more refined modeling of dialog structures, possibly relying on discourse parsing @xcite . we would also like to explore depression severity classification as an extension to binary classification, possibly through a cascading structure: first, detect depression and then classify the severity. we intend to refine our work and report on cross-validation splits of the data to test the stability of the model, an issue even more crucial when dealing with sparse data with possibly representativeness problem. a further step will be to investigate the generalization of our model to other mental health disorders. moreover, we carefully select the dialog corpora used in this paper to control for potential biases and personal information leakage. we only work with interview transcription, with no audio or visual information. for the text part, all the participant's name have been marked out with pseudo-id.