# An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling

## TL;DR

This paper introduces a novel ensemble model for semantic proto-role labeling that leverages self-attention and predicate-argument markers, outperforming previous methods without relying on gold argument heads.

## Contribution

The paper presents a new ensemble model with self-attention for SPRL that is competitive with state-of-the-art methods and does not depend on gold argument heads.

## Key findings

- Outperforms previous SPRL models in multi-label and Likert scale tasks
- Does not require gold argument heads from tree banks
- Achieves competitive results with state-of-the-art methods

## Abstract

Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty's feature-based view of proto-roles. This theory determines agenthood vs. patienthood based on a participant's instantiation of more or less typical agent vs. patient properties, such as, for example, volition in an event. To perform SPRL, we develop an ensemble of hierarchical models with self-attention and concurrently learned predicate-argument-markers. Our method is competitive with the state-of-the art, overall outperforming previous work in two formulations of the task (multi-label and multi-variate Likert scale prediction). In contrast to previous work, our results do not depend on gold argument heads derived from supplementary gold tree banks.