9 citations · 14 across the 10 of their papers we have counts for
12 papers
Estimating Surprisal from the Speech Signal
Zachary Hopton, Samuel Kiegeland, Francesco Ignazio Re +4
Although language is predominantly spoken, empirical tests of surprisal theory rely almost entirely on written text. In practice, surprisal is estimated using language models (LMs)…
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland +3
Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language mo…
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Stochastic Estimation of Transduced Language Models
Vésteinn Snæbjarnarson, Samuel Kiegeland, Manuel de Prada Corral +2
Transduced language models (TLMs) compose a pretrained \emph{source} language model with a functional finite-state transducer to induce a language model over \emph{target} strings.…
On the Proper Treatment of Units in Surprisal Theory
Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira +1
Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a unit underspecified. In practic…
Probing for Reading Times
Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re +4
Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive signals about human processing.…
Ensembling Language Models with Sequential Monte Carlo
Robin Shing Moon Chan, Tianyu Liu, Samuel Kiegeland +5
Practitioners have access to an abundance of language models and prompting strategies for solving many language modeling tasks; yet prior work shows that modeling performance is hi…
Transducing Language Models
Vésteinn Snæbjarnarson, Samuel Kiegeland, Tianyu Liu +3
Modern language models define distributions over strings, but downstream tasks often require different output formats. For instance, a model that generates byte-pair strings does n…
The Harmonic Structure of Information Contours
Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak +5
The uniform information density (UID) hypothesis proposes that speakers aim to distribute information evenly throughout a text, balancing production effort and listener comprehensi…
Reverse-Engineering the Reader
Samuel Kiegeland, Ethan Gotlieb Wilcox, Afra Amini +2
Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper…
Reading Task Classification Using EEG and Eye-Tracking Data
Nora Hollenstein, Marius Tröndle, Martyna Plomecka +4
The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading. We analyze whethe…
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
Samuel Kiegeland, Julia Kreutzer
Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multip…