AIConfPaper
← Search

Matthew Matero

5 accepted papers

2023

Discourse-Level Representations can Improve Prediction of Degree of Anxiety

Swanie Juhng, Matthew Matero, Vasudha Varadarajan, Johannes Eichstaedt, Adithya V Ganesan, H. Andrew Schwartz
ACL 2023short

Anxiety disorders are the most common of mental illnesses, but relatively little is known about how to detect them from language. The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. Here, we investig…

Cited by 11Source ↗PDF ↗Scholar ↗
2021

Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality

Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula, Huy Vu, H. Andrew Schwartz
NAACL 2021long

In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. He…

Cited by 47Source ↗PDF ↗code ↗Scholar ↗
2021

MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection

Matthew Matero, Nikita Soni, Niranjan Balasubramanian, H. Andrew Schwartz
EMNLP 2021finding

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequences of messages) is under-explored. In stance de…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2020

Autoregressive Affective Language Forecasting: A Self-Supervised Task

Matthew Matero, H. Andrew Schwartz
COLING 2020main

Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of affective language fo…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗