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Research in Brief: LIGHT, a multi-player crowdsourced text adventure game for dialogue research

Research in Brief: LIGHT, a multi-player crowdsourced text adventure game for dialogue research


>>Jack: Hi. I’m Jack Urbaneck. I’m
an engineer on Facebook’s AI team and I work on natural language
understanding and dialogue. I worked with my team to build a
multiplayer crowdsourced text adventure game called LIGHT. We’ve made the complete
setup open source and available to other researchers. Much current research today
focuses on statistical regularities of fixed language data without an
explicit understanding of the world that that language is
trying to describe. So for this game we wanted to build
it entirely on natural language and as such, all of the
locations, objects and characters within were written by people. Agents in LIGHT are trained on data
produced by people playing the game as well which means that all of
the environment communication and actions are natural and rich. For example, the language present
inherits all of the complex properties of natural language such as
ambiguity and co-reference which can be challenging for machine
learning models to understand but are an important part
of the language that we use. I’ll talk a little bit about how we
made the AI-controlled agents work. First we devised an AI model that could
produce separate representations for all of the contexts including the
setting, a character’s persona and objects present in the room. And then we create a context embedding to score the most promising
text candidates. We used our PyTorch machine
learning framework and ParlAI to build this model, then we use the
Bidirectional Encoder Representations from Transformers, otherwise
known as BERT model, that’s able to access context from
both past and future directions to build two systems, a bi-ranker
model which is fast and practical and a cross-ranker which is a slower
model that allows more cross-correlation between the contexts we’ve calculated
and the response and lastly, we used another set of AI models
to encode the context and dialogue into features that let
us generate game actions. Combining these models we
have characters in the game that can communicate with players
and take relevant game actions. Before LIGHT we didn’t have a
platform for studying language and actions jointly in
a rich game world. Now that we’ve built that
and made it openly available and showed its potential, we
hope to enable future research in improving the ability of
agents to think, speak and act, all while modeling a holistic world
including the other agents within it.


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