United Arab Emirates

In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task.  The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.

EMNLP 2022

Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media

mental health

machine learning

social media

workshop paper

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Using language models created from large data sources has improved the performance of several deep learning-based architectures, obtaining state-of-the-art results in several NLP extrinsic tasks. However, little research is related to creating intrinsic tests that allow us to compare the quality of different language models when obtaining contextualized embeddings. This gap increases even more when working on specific domains in languages other than English. This paper proposes a novel graph-based intrinsic test that allows us to measure the quality of different language models in clinical and biomedical domains in Spanish. Our results show that our intrinsic test performs better for clinical and biomedical language models than a general one. Also, it correlates with better outcomes for a NER task using a probing model over contextualized embeddings. We hope our work will help the clinical NLP research community to evaluate and compare new language models in other languages and find the most suitable models for solving downstream tasks.

A Knowledge-Graph-Based Intrinsic Test for Benchmarking Medical Concept Embeddings and Pretrained Language Models

Empathy is a vital component of health care and plays  a key role in the training of future doctors. Paying attention to medical students' self-reflective stories of their interactions with patients can encourage empathy and the formation of professional identities that embody desirable values such as integrity and respect. We present a computational approach and linguistic analysis of empathic language in a large corpus of 440 essays written by pre-med students as narrated simulated patient -- doctor interactions. We analyze the discourse of three kinds of empathy: cognitive, affective, and prosocial as highlighted by expert annotators. We also present various experiments with state-of-the-art recurrent neural networks and transformer models for classifying these forms of empathy. To further improve over these results, we develop a novel system architecture that makes use of frame semantics to enrich our state-of-the-art models. We show that this novel framework leads to significant improvement on the empathy classification task for this dataset.

Enriching Deep Learning with Frame Semantics for Empathy Classification in Medical Narrative Essays

Social media has become a popular platform where people share information about personal healthcare conditions, diagnostic histories, and medical plans. Analyzing posts on social media depicting such realistic information can help improve quality and clinical decision-making; however, the lack of structured resources in this genre limits us to build robust NLP models for meaningful analysis. This paper presents a new corpus annotating relations among many types of conditions, treatments, and their attributes illustrated in social media posts by patients and caregivers. For experiments, a transformer encoder is pretrained on 1M raw posts and used to train several document-level relation extraction models using our corpus. Our best-performing model achieves the F1 scores of 70.9 and 51.7 for Entity Recognition and Relation Extraction, respectively. These results are encouraging as it is the first neural model extracting complex relations of this kind on social media data.

Condition-Treatment Relation Extraction on Disease-related Social Media Data

Recently, research into bringing outside knowledge sources into current neural NLP models has been increasing. Most approaches that leverage external knowledge sources require laborious and non-trivial designs, as well as tailoring the system through intensive ablation of different knowledge sources, an effort that discourages users to use quality ontological resources. In this paper, we show that multiple large heterogeneous KSs can be easily integrated using a decoupled approach, allowing for an automatic ablation of irrelevant KSs, while keeping the overall parameter space tractable. We experiment with BERT and pre-trained graph embeddings, and show that they interoperate well without performance degradation, even when some do not contribute to the task.

Integration of Heterogeneous Knowledge Sources for Biomedical Text Processing

Recent literature has demonstrated the potential of multilingual Neural Machine Translation (mNMT) models. However, the most efficient models are not well suited to specialized industries. In these cases, internal data is scarce and expensive to find in all language pairs. Therefore, fine-tuning a mNMT model on a specialized domain is hard. In this context, we decided to focus on a new task: Domain Adaptation of a pre-trained mNMT model on a single pair of language while trying to maintain model quality on generic domain data for all language pairs. The risk of loss on generic domain and on other pairs is high. This task is key for mNMT model adoption in the industry and is at the border of many others. We propose a fine-tuning procedure for the generic mNMT that combines embeddings freezing and adversarial loss. Our experiments demonstrated that the procedure improves performances on specialized data with a minimal loss in initial performances on generic domain for all languages pairs, compared to a naive standard approach (+10.0 BLEU score on specialized data,  -0.01 to -0.5 BLEU on WMT and Tatoeba datasets on the other pairs with M2M100).

Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models

Encoding both language-specific and language-agnostic information into a single high-dimensional space is a common practice of pre-trained Multi-lingual Language Models (pMLM). Such encoding has been shown to perform effectively on natural language tasks requiring semantics of the whole sentence (e.g., translation). However, its effectiveness appears to be limited on tasks requiring partial information of the utterance (e.g., multi-lingual entity retrieval, template retrieval, and semantic alignment). In this work, a novel Fine-grained Multilingual Disentangled Autoencoder (FMDA) is proposed to disentangle fine-grained semantic information from language-specific information in a multi-lingual setting. FMDA is capable of successfully extracting the disentangled template semantic and residual semantic representations. Experiments conducted on the MASSIVE dataset demonstrate that the disentangled encoding can boost each other during the training, thus consistently outperforming the original pMLM and the strong language disentanglement baseline on monolingual template retrieval and cross-lingual semantic retrieval tasks across multiple languages.

Fine-grained Multi-lingual Disentangled Autoencoder for Language-agnostic Representation Learning

Token free approaches have been successfully applied to a series of word and span level tasks. In this work, we evaluate a byte-level sequence to sequence model (ByT5) on the 51 languages in the MASSIVE multilingual semantic parsing dataset. We examine multiple experimental settings: (i) zero-shot, (ii) full gold data and (iii) zero-shot with synthetic data. By leveraging a state-of-the-art label projection method for machine translated examples, we are able to reduce the gap in exact match to only 5 points with respect to a model trained on gold data from all the languages. We additionally provide insights on the cross-lingual transfer of ByT5 and show how the model compares with respect to mT5 across all parameter sizes. 

Byte-Level Massively Multilingual Semantic Parsing

Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks. Our system ranked 1st in the MMNLU-22 competition under the full-dataset setting.

HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding

Cross-lingual phenomena are quite common in informal contexts like social media, where users are likely to mix their native language with English or other languages. However, few studies have focused so far on analyzing cross-lingual interactions in voice-assistant data, which present peculiar features in terms of sentence length, named entities, and use of spoken language. Also, little attention has been posed to European countries, where English is frequently used as a second language. In this paper, we present a large-scale empirical analysis of cross-lingual phenomena (code-mixing, linguistic borrowing, foreign named entities) in the interactions with a large-scale voice assistant in European countries. To do this, we first introduce a general, highly-scalable technique to generate synthetic mixed training data annotated with token-level language labels and we train two neural network models to predict them.  We evaluate the models both on the synthetic dataset and on a real dataset of code-switched utterances, showing that the best performance is obtained by a character convolution based model. The results of the analysis highlight different behaviors between countries, having Italy with the highest ratio of cross-lingual utterances and Spain with a marked preference in keeping Spanish words. Our research, paired to the increase of the cross-lingual phenomena in time, motivates further research in developing multilingual Natural Language Understanding (NLU) models, which can naturally deal with cross-lingual interactions.

Play música alegre: A Large-Scale Empirical Analysis of Cross-Lingual Phenomena in Voice Assistant Interactions

The joint intent classification and slot filling task seeks to detect the intent of an utterance and extract its semantic concepts. In the zero-shot cross-lingual setting, a model is trained on a source language and then transferred to other target languages through multi-lingual representations without additional training data. While prior studies show that pre-trained multilingual sequence-to-sequence (Seq2Seq) models can facilitate zero-shot transfer, there is little understanding on how to design the output template for the joint prediction tasks. In this paper, we examine three aspects of the output template -- (1) label mapping, (2) task dependency, and (3) word order. Experiments on the MASSIVE dataset consisting of 51 languages show that our output template significantly improves the performance of pre-trained cross-lingual language models.

Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media

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A Knowledge-Graph-Based Intrinsic Test for Benchmarking Medical Concept Embeddings and Pretrained Language Models

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Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media

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Next from EMNLP 2022

A Knowledge-Graph-Based Intrinsic Test for Benchmarking Medical Concept Embeddings and Pretrained Language Models

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads