Tianran Liu


2023

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The Linearity of the Effect of Surprisal on Reading Times across Languages
Weijie Xu | Jason Chon | Tianran Liu | Richard Futrell
Findings of the Association for Computational Linguistics: EMNLP 2023

In psycholinguistics, surprisal theory posits that the amount of online processing effort expended by a human comprehender per word positively correlates with the surprisal of that word given its preceding context. In addition to this overall correlation, more importantly, the specific quantitative form taken by the processing effort as a function of surprisal offers insights into the underlying cognitive mechanisms of language processing. Focusing on English, previous studies have looked into the linearity of surprisal on reading times. Here, we extend the investigation by examining eyetracking corpora of seven languages: Danish, Dutch, English, German, Japanese, Mandarin, and Russian. We find evidence for superlinearity in some languages, but the results are highly sensitive to which language model is used to estimate surprisal.

2022

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Testing Pre-trained Language Models’ Understanding of Distributivity via Causal Mediation Analysis
Pangbo Ban | Yifan Jiang | Tianran Liu | Shane Steinert-Threlkeld
Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

To what extent do pre-trained language models grasp semantic knowledge regarding the phenomenon of distributivity? In this paper, we introduce DistNLI, a new diagnostic dataset for natural language inference that targets the semantic difference arising from distributivity, and employ the causal mediation analysis framework to quantify the model behavior and explore the underlying mechanism in this semantically-related task. We find that the extent of models’ understanding is associated with model size and vocabulary size. We also provide insights into how models encode such high-level semantic knowledge.