The Efficiency of Question-Asking Strategies in a Real-World Visual Search Task.

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets.

She adapts to her student: An expert pragmatic speaker tailoring her referring expressions to the Layman listener.

Communication is a dynamic process through which interlocutors adapt to each other. In the development of conversational agents, this core aspect has been put aside for several years since the main challenge was to obtain conversational neural models able to produce utterances and dialogues that at least at the surface level are human-like. Now that this milestone has been achieved, the importance of paying attention to the dynamic and adaptive interactive aspects of language has been advocated in several position papers.

Artificial Intelligence Models Do Not Ground Negation, Humans Do. GuessWhat?! Dialogues as a Case Study.

Negation is widely present in human communication, yet it is largely neglected in the research on conversational agents based on neural network architectures. Cognitive studies show that a supportive visual context makes the processing of negation easier. We take GuessWhat?!, a referential visually grounded guessing game, as test-bed and evaluate to which extent guessers based on pre-trained language models profit from negatively answered polar questions.

Linguistic issues behind visual question answering.

Answering a question that is in an image is a crucial ability that requires understanding the question, the visual context, and their interaction at many linguistic levels: among others, semantics, syntax and pragmatics. As such, visually-grounded questions have long been of interest to theoretical linguists and cognitive scientists. Moreover, they have inspired the first attempts to computationally model natural language understanding, where pioneering systems were faced with the highly challenging task-still unsolved-of jointly dealing with syntax, semantics and inference whilst understanding a visual context.

Probing the mental representation of quantifiers.

In this study, we investigate the mental representation of non-numerical quantifiers ("some", "many", "all", etc.) by comparing their use in abstract and in grounded perceptual contexts. Using an approach similar to that used in the number domain, we test whether (and to what extent) such representation is constrained by the way we perceive the world through our senses.