5 accepted papers
Lexical ambiguity—where a single wordform takes on distinct, context-dependent meanings–serves as a useful tool to compare across different language models’ (LMs’) ability to form distinct, contextualized representations of the same stimulus. Few studies have systematically compared LMs’ contextuali…
Recent studies show that deep vision-only and language-only models—trained on disjoint modalities—nonetheless project their inputs into a partially aligned representational space. Yet we still lack a clear picture of _where_ in each network this convergence emerges, _what_ visual or linguistic cues
Multimodal large language models (MLLMs) are gaining popularity as partial solutions to the “symbol grounding problem” faced by language models trained on text alone. However, little is known about whether and how these multiple modalities are integrated. We draw inspiration from analogous work in h…
Most words are ambiguous—-i.e., they convey distinct meanings in different contexts—-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambi…
We are developing a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction…