Grounded language acquisition is concerned with learning the meaning of language as it
applies to the physical world. As robots become more capable and ubiquitous, there is an …

Building computer systems that can converse about their visual environment is one of the
oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for …

Distributional semantic models have become a mainstay in NLP, providing useful features
for downstream tasks. However, assessing long-term progress requires explicit long-term …

Multi-task learning with Convolutional Neural Network (CNN) has shown great success in
many Natural Language Processing (NLP) tasks. This success can be largely attributed to …

Abstract Language models are trained only on text despite the fact that humans learn their
first language in a highly interactive and multimodal environment where the first set of …

Vision-language models (VLMs) have shown to be effective at image retrieval based on
simple text queries, but text-image retrieval based on conversational input remains a …

Crossmodal interaction in situated language comprehension is important for effective and
efficient communication. The relationship between linguistic and visual stimuli provides …

RNN-based sequence generation is now widely used in NLP and NLG (natural language
generation). Most work focusses on how to train RNNs, even though also decoding is not …

Natural language object retrieval is a highly useful yet challenging task for robots in human-
centric environments. Previous work has primarily focused on commands specifying the …

In relation to semantics,“grounding” has (at least) two relevant meanings.“Symbol
grounding” is the process of connecting symbols (eg, words) to perception and the …