17 research outputs found
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate how well these representations can predict perceptual and
conceptual features of concrete concepts, drawing on two semantic norm datasets
sourced from human participants. We find that several standard word
representations fail to encode many salient perceptual features of concepts,
and show that these deficits correlate with word-word similarity prediction
errors. Our analyses provide motivation for grounded and embodied language
learning approaches, which may help to remedy these deficits.Comment: Accepted at RoboNLP 201
Emergence of Grounded Compositional Language in Multi-Agent Populations
By capturing statistical patterns in large corpora, machine learning has
enabled significant advances in natural language processing, including in
machine translation, question answering, and sentiment analysis. However, for
agents to intelligently interact with humans, simply capturing the statistical
patterns is insufficient. In this paper we investigate if, and how, grounded
compositional language can emerge as a means to achieve goals in multi-agent
populations. Towards this end, we propose a multi-agent learning environment
and learning methods that bring about emergence of a basic compositional
language. This language is represented as streams of abstract discrete symbols
uttered by agents over time, but nonetheless has a coherent structure that
possesses a defined vocabulary and syntax. We also observe emergence of
non-verbal communication such as pointing and guiding when language
communication is unavailable
Translating Neuralese
Several approaches have recently been proposed for learning decentralized
deep multiagent policies that coordinate via a differentiable communication
channel. While these policies are effective for many tasks, interpretation of
their induced communication strategies has remained a challenge. Here we
propose to interpret agents' messages by translating them. Unlike in typical
machine translation problems, we have no parallel data to learn from. Instead
we develop a translation model based on the insight that agent messages and
natural language strings mean the same thing if they induce the same belief
about the world in a listener. We present theoretical guarantees and empirical
evidence that our approach preserves both the semantics and pragmatics of
messages by ensuring that players communicating through a translation layer do
not suffer a substantial loss in reward relative to players with a common
language.Comment: Fixes typos and cleans ups some model presentation detail
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
Computational models of emergent communication in agent populations are
currently gaining interest in the machine learning community due to recent
advances in Multi-Agent Reinforcement Learning (MARL). Current contributions
are however still relatively disconnected from the earlier theoretical and
computational literature aiming at understanding how language might have
emerged from a prelinguistic substance. The goal of this paper is to position
recent MARL contributions within the historical context of language evolution
research, as well as to extract from this theoretical and computational
background a few challenges for future research