Did they answer? Subjective acts and intents in conversational discourse

TL;DR

This paper introduces a novel discourse dataset capturing multiple subjective interpretations of conversation acts and intents, emphasizing the social context's role in understanding discourse and improving computational models.

Contribution

It presents the first dataset with multiple subjective discourse interpretations and demonstrates that considering interpreter biases enhances prediction accuracy.

Findings

Interpreter bias improves interpretation predictions.

Disagreements are nuanced and context-dependent.

Dataset and code are publicly available.

Abstract

Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences. At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations. However, current discourse data and frameworks ignore the social aspect, expecting only a single ground truth. We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. We carefully analyze our dataset and create computational models to (1) confirm our hypothesis that taking into account the bias of the interpreters leads to better predictions of the interpretations, (2) and show disagreements are nuanced and require a deeper understanding of the different contextual factors. We share our dataset and code at http://github.com/elisaF/subjective_discourse.