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.

研究神经网络句子编码器是否能够学习利用语言特征预测实用推理的强度，发现 LSTM 句子编码器能够高精度地预测人类推理强度评分，并学会利用语言特征预测实用推理。