Computer Science > Computation and Language
[Submitted on 16 Sep 2021 (v1), last revised 1 Nov 2021 (this version, v2)]
Title:MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
View PDFAbstract: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 detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship. Here, we introduce Message-Level Transformer (MeLT) -- a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss. We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.
Submission history
From: Matthew Matero [view email][v1] Thu, 16 Sep 2021 17:07:45 UTC (805 KB)
[v2] Mon, 1 Nov 2021 18:42:07 UTC (994 KB)
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