Learning attention functions requires large volumes of data, but many NLP tasks simulate
human behavior, and in this paper, we show that human attention really does provide a …

Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal
resolution which closely follows the sequential structure of the text. It is highly correlated with …

Attempts to find a single technique for general-purpose intrinsic evaluation of word
embeddings have so far not been successful. We present a new approach based on scaled …

In this study, we introduce and evaluate a novel extractive text summarization methodology,“
SummarEyes,” based on the visual interaction of the user with the text, using eye-tracking …

Gaze behaviour has been used as a way to gather cognitive information for a number of
years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural …

Sarcasm is a complex linguistic construct with incongruity at its very core. Detecting sarcasm
depends on the actual content spoken and tonality, facial expressions, the context of an …

Predicting a reader's rating of text quality is a challenging task that involves estimating
different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are …

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification
method with respect to two NLP tasks: semantic role labelling (SRL) and information …

We present the first work on predicting reading mistakes in children with reading difficulties
based on eye-tracking data from real-world reading teaching. Our approach employs …

Automatic text summarization has been a hot research topic for years. Though most of the
existing studies only use the content itself to generate the summaries, researchers believe …