Harnessing the linguistic signal to predict scalar inferences

TL;DR

This paper investigates whether neural network sentence encoders can learn to predict the strength of scalar inferences based on linguistic features, demonstrating high accuracy and capturing established linguistic associations.

Contribution

It shows that LSTM-based sentence encoders can effectively predict scalar inference strength from linguistic cues, bridging neural models and pragmatic inference understanding.

Findings

LSTM encoder predicts inference strength with r=0.78 accuracy.

Model captures linguistic features influencing inference strength.

Neural network infers established linguistic associations.

Abstract

Pragmatic inferences often subtly depend on the presence or absence of linguistic features. For example, the presence of a partitive construction (of the) increases the strength of a so-called scalar inference: listeners perceive the inference that Chris did not eat all of the cookies to be stronger after hearing "Chris ate some of the cookies" than after hearing the same utterance without a partitive, "Chris ate some cookies." In this work, we explore to what extent neural network sentence encoders can learn to predict the strength of scalar inferences. We first show that an LSTM-based sentence encoder trained on an English dataset of human inference strength ratings is able to predict ratings with high accuracy (r=0.78). We then probe the model's behavior using manually constructed minimal sentence pairs and corpus data. We find that the model inferred previously established associations between linguistic features and inference strength, suggesting that the model learns to use linguistic features to predict pragmatic inferences.