# Harnessing the linguistic signal to predict scalar inferences

arXiv: https://arxiv.org/abs/1910.14254 · HTML: https://mlsanity.com/paper/1910.14254/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 17 of 17)

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- [Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions](https://mlsanity.com/paper/2405.05776/) (2024)
- [Pragmatic inference of scalar implicature by LLMs](https://mlsanity.com/paper/2408.06673/) (2024)
- [Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics](https://mlsanity.com/paper/2404.03301/) (2024)
- [Understand the Implication: Learning to Think for Pragmatic Understanding](https://mlsanity.com/paper/2506.13559/) (2025)
- [DRInQ: Evaluating Conversational Implicature with Controlled Context Variation](https://mlsanity.com/paper/2605.24267/) (2026)
- [Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions](https://mlsanity.com/paper/2605.31586/) (2026)

## Abstract (arXiv)

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.