# Target Based Speech Act Classification in Political Campaign Text

## TL;DR

This paper introduces a new schema and annotated corpus for classifying speech acts in political campaign texts, focusing on domain-specific acts and their targets, and evaluates various modeling techniques.

## Contribution

It presents a novel annotation schema and corpus for political speech acts, along with modeling approaches that incorporate context, semi-supervised learning, and speaker metadata.

## Key findings

- Effective modeling of speech acts using contextualized embeddings.
- Semi-supervised learning improves classification accuracy.
- Incorporating speaker meta-data enhances model performance.

## Abstract

We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific speech acts, such as commissive-action, and present a novel annotated corpus of media releases and speech transcripts from the 2016 Australian election cycle. We show how speech acts and target referents can be modeled as sequential classification, and evaluate several techniques, exploiting contextualized word representations, semi-supervised learning, task dependencies and speaker meta-data.