Revisiting the Uniform Information Density Hypothesis

The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal -- or lack thereof -- should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document -- a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel

Testing the Predictions of Surprisal Theory in 11 Languages

A fundamental result in psycholinguistics is that less predictable words take a longer time to process. One theoretical explanation for this finding is Surprisal Theory (Hale, 2001; Levy, 2008), which quantifies a word's predictability as its surprisal, i.e. its negative log-probability given a context. While evidence supporting the predictions of Surprisal Theory have been replicated widely, most have focused on a very narrow slice of data: native English speakers reading English texts. Indeed, no comprehensive multilingual analysis exists. We address this gap in the current literature by investigating the relationship between surprisal and reading times in eleven different languages, distributed across five language families. Deriving estimates from language models trained on monolingual and multilingual corpora, we test three predictions associated with surprisal theory: (i) whether surprisal is predictive of reading times; (ii) whether expected surprisal, i.e. contextual entropy, is predictive of reading times; (iii) and whether the linking function between surprisal and reading times is linear. We find that all three predictions are borne out crosslinguistically. By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.Comment: This is a pre-MIT Press publication version of the pape

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens

Recent psycholinguistic studies have drawn conflicting conclusions about the relationship between the quality of a language model and the ability of its surprisal estimates to predict human reading times, which has been speculated to be due to the large gap in both the amount of training data and model capacity across studies. The current work aims to consolidate these findings by evaluating surprisal estimates from Transformer-based language model variants that vary systematically in the amount of training data and model capacity on their ability to predict human reading times. The results show that surprisal estimates from most variants with contemporary model capacities provide the best fit after seeing about two billion training tokens, after which they begin to diverge from humanlike expectations. Additionally, newly-trained smaller model variants reveal a 'tipping point' at convergence, after which the decrease in language model perplexity begins to result in poorer fits to human reading times. These results suggest that the massive amount of training data is mainly responsible for the poorer fit achieved by surprisal from larger pre-trained language models, and that a certain degree of model capacity is necessary for Transformer-based language models to capture humanlike expectations.Comment: Findings of the Association for Computational Linguistics: EMNLP 202

Comparing Transformers and RNNs on predicting human sentence processing data

Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The more recently introduced Transformer architecture has been shown to outperform recurrent neural networks on many natural language processing tasks but little is known about their ability to model human language processing. It has long been thought that human sentence reading involves something akin to recurrence and so RNNs may still have an advantage over the Transformer as a cognitive model. In this paper we train both Transformer and RNN based language models and compare their performance as a model of human sentence processing. We use the trained language models to compute surprisal values for the stimuli used in several reading experiments and use mixed linear modelling to measure how well the surprisal explains measures of human reading effort. Our analysis shows that the Transformers outperform the RNNs as cognitive models in explaining self-paced reading times and N400 strength but not gaze durations from an eye-tracking experiment

On the Effect of Anticipation on Reading Times

Over the past two decades, numerous studies have demonstrated how less predictable (i.e., higher surprisal) words take more time to read. In general, these studies have implicitly assumed the reading process is purely responsive: Readers observe a new word and allocate time to process it as required. We argue that prior results are also compatible with a reading process that is at least partially anticipatory: Readers could make predictions about a future word and allocate time to process it based on their expectation. In this work, we operationalize this anticipation as a word's contextual entropy. We assess the effect of anticipation on reading by comparing how well surprisal and contextual entropy predict reading times on four naturalistic reading datasets: two self-paced and two eye-tracking. Experimentally, across datasets and analyses, we find substantial evidence for effects of contextual entropy over surprisal on a word's reading time (RT): in fact, entropy is sometimes better than surprisal in predicting a word's RT. Spillover effects, however, are generally not captured by entropy, but only by surprisal. Further, we hypothesize four cognitive mechanisms through which contextual entropy could impact RTs -- three of which we are able to design experiments to analyze. Overall, our results support a view of reading that is not just responsive, but also anticipatory.Comment: This is a pre-MIT Press publication version of the paper. Code is available in https://github.com/rycolab/anticipation-on-reading-time

Neural models of language use:Studies of language comprehension and production in context

Artificial neural network models of language are mostly known and appreciated today for providing a backbone for formidable AI technologies. This thesis takes a different perspective. Through a series of studies on language comprehension and production, it investigates whether artificial neural networks—beyond being useful in countless AI applications—can serve as accurate computational simulations of human language use, and thus as a new core methodology for the language sciences

Is the mind inherently predicting? Exploring forward and backward looking in language processing

Prediction is one characteristic of the human mind. But what does it mean to say the mind is a ’prediction machine’ and inherently forward looking as is frequently claimed? In natural languages, many contexts are not easily predictable in a forward fashion. In English for example many frequent verbs do not carry unique meaning on their own, but instead rely on another word or words that follow them to become meaningful. Upon reading take a the processor often cannot easily predict walk as the next word. But the system can ‘look back’ and integrate walk more easily when it follows take a (e.g., as opposed to make|get|have a walk). In the present paper we provide further evidence for the importance of both forward and backward looking in language processing. In two self-paced reading tasks and an eye-tracking reading task, we found evidence that adult English native speakers’ sensitivity to word forward and backward conditional probability significantly explained variance in reading times over and above psycholinguistic predictors of reading latencies. We conclude that both forward and backward-looking (prediction and integration) appear to be important characteristics of language processing. Our results thus suggest that it makes just as much sense to call the mind an ’integration machine’ which is inherently backward looking

Lower Perplexity is Not Always Human-Like

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization -- the lower perplexity a language model has, the more human-like the language model is -- in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.Comment: Accepted by ACL 202