This team will first undertake an open-ended and substantive deliberation of meaning representations for linguistic processing, and then focus on a pragmatic problem in semantic processing by machines.
Goal 1: Deliberate upon representations of linguistic meaning. “Deep” natural-language understanding will eventually need more sophisticated semantic representations. What representations should we be using in 10 years? How will they relate to non-linguistic processing? How can we start to recover them from text or other linguistic resources?
Linguists currently rely on modal logic as the foundation of semantics. However, semantics and knowledge representation must connect to reasoning and pragmatics, which are increasingly regarded by the AI and cognitive science communities as involving probabilistic inference and not just logical inference. Can we find a probabilistic foundation to integrate the whole enterprise? What is the role of probability distributions over semantic representations and within semantic representations?
The team includes leaders from multiple communities — linguistics,natural language processing, machine learning, and computational cognitive science. We hope to make progress toward an acceptable theory by integrating the constraints and formal techniques contributed by all of these communities.
This week-long immersive exercise, which will take a broad perspective on meaning and its representation, is expected to inform the long-term thinking of all workshop participants, even as they pursue near-term practical uses of meaning representations.
Goal 2: Explore semantic/proto-roles, from both a theoretical and an empirical perspective.
This research is motivated by linguists such as David Dowty, who have considered the meta-question of which (if any) of the semantic role theories espoused in the literature are well founded. Instead of the traditional coarse labels, they create a binary feature structure representation by collecting human responses to questions on proto-roles (e.g., “does the subject of this verb have a causal role in the event?” or “does the object of this verb change location as a result of the event?”).
From a computational perspective, the team will build a classifier for automatic prediction of these binary features, adapting recent work led by Van Durme on models for PropBank semantic role classification. They will also perform corpus-based studies on how these feature structures correlate with existing resources such as Framenet and PropBank. PropBank is a precursor to the current work in AMR, which will lead to interesting discussions with the CLAMR team.
|Jason Eisner||Johns Hopkins University|
|Benjamin Van Durme||Johns Hopkins University|
|Oren Etzioni||University of Washington, Allen Institute|
|Craig Harman||Johns Hopkins University|
|Shalom Lappin||King's College London|
|Staffan Larsson||University of Gothenburg|
|Dan Lassiter||Stanford University|
|Percy Liang||Stanford University|
|David McAllester||Toyota Technical Institute|
|James Pustejovsky||Brandeis University|
|Kyle Rawlins||Johns Hopkins University|
|Nicholas Andrews||Johns Hopkins University|
|Frank Ferraro||Johns Hopkins University|
|Drew Reisinger||Johns Hopkins University|
|Darcey Riley||Johns Hopkins University|
|Rachel Rudinger||Johns Hopkins University|