International audienceIt has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human reading time, which is an approximation of human sentence comprehension difficulty. Nevertheless, parsers usually take as input sentences that have already been processed or even manually annotated. We propose to study a more realistic scenario, where the various processing levels (tokenization, PoS and morphology tagging, lemmatization, syntactic parsing and sentence segmentation) are predicted incrementally from raw text. To this end, we propose a versatile modeling framework, we call the Reading Machine, that performs all such linguistic tasks and allows to incorporate cognitive constrains such as incrementality. We il...
Zarrieß S, Loth S, Schlangen D. Reading Times Predict the Quality of Generated Text Above and Beyond...
We examine the ability of several mod-els of computation and storage to explain reading time data. S...
In this paper we develop novel algorithmic ideas for building a natural language parser grounded upo...
It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human r...
Information theoretic measures of incremental parser load were generated from a phrase structure par...
How well can we predict reading times and thus cognitive processing load? This study first assesses ...
Reading speed is commonly used as an index of reading fluency. However, reading speed is not a consi...
What are the effects of word-by-word predictability on sentence processing times during the natural ...
International audienceWe present in this paper a robust method for predicting reading times. Robustn...
The aim of this thesis is to design and implement a cognitively plausible theory of sentence process...
We analyze if large language models are able to predict patterns of human reading behavior. We compa...
We analyze if large language models are able to predict patterns of human reading behavior. We compa...
We examine the ability of several mod-els of computation and storage to explain reading time data. S...
We present a targeted, scaled-up comparison of incremental processing in humans and neural language ...
This paper investigates the relationship between two complementary perspectives in the human assessm...
Zarrieß S, Loth S, Schlangen D. Reading Times Predict the Quality of Generated Text Above and Beyond...
We examine the ability of several mod-els of computation and storage to explain reading time data. S...
In this paper we develop novel algorithmic ideas for building a natural language parser grounded upo...
It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human r...
Information theoretic measures of incremental parser load were generated from a phrase structure par...
How well can we predict reading times and thus cognitive processing load? This study first assesses ...
Reading speed is commonly used as an index of reading fluency. However, reading speed is not a consi...
What are the effects of word-by-word predictability on sentence processing times during the natural ...
International audienceWe present in this paper a robust method for predicting reading times. Robustn...
The aim of this thesis is to design and implement a cognitively plausible theory of sentence process...
We analyze if large language models are able to predict patterns of human reading behavior. We compa...
We analyze if large language models are able to predict patterns of human reading behavior. We compa...
We examine the ability of several mod-els of computation and storage to explain reading time data. S...
We present a targeted, scaled-up comparison of incremental processing in humans and neural language ...
This paper investigates the relationship between two complementary perspectives in the human assessm...
Zarrieß S, Loth S, Schlangen D. Reading Times Predict the Quality of Generated Text Above and Beyond...
We examine the ability of several mod-els of computation and storage to explain reading time data. S...
In this paper we develop novel algorithmic ideas for building a natural language parser grounded upo...