Body: <p>I was reading about the <a href="https://en.wikipedia.org/wiki/Symbol_grounding_problem" rel="nofollow noreferrer">grounding problem</a> after seeing it mentioned in another answer today. The article states that, in order to avoid the "infinite regress" of defining all words with other words, we must ground the meaning of some words in the "sensorimotor." </p>
  
  <blockquote>
    <p>To be grounded, the symbol system would have to be augmented with nonsymbolic, sensorimotor capacities—the capacity to interact autonomously with that world of objects, events, actions, properties and states that its symbols are systematically interpretable (by us) as referring to.</p>
  </blockquote>
  
  <p>Obviously, this made me think of Reinforcement Learning. But I'm not exactly sure what counts as "interaction." Would this necessarily imply an MDP-like formulation with rewards, state transitions, etc? Or could some form of grounding be accomplished with supervised learning?</p>
  
  <p>This seems like a pretty fundamental problem of AI. Does anyone know of research being done on grounding words/symbols within an RL agent?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-03-17T10:08:06.093Z
Comments.Text: I think you need to reduce the number of questions at the end. In fact I would remove the "On a related note" paragraph, because it although it is related, answering it could double the length of any answer here. By stating the question, you are setting expectation that someone should attempt to answer it. If you want an answer to that, and it is not already covered by answers to your first question, you can always ask a second, separate question here. That would use the site better, IMO
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-14T15:21:40.897Z
Comments.Text: Have a look at https://github.com/adityathakker/awesome-rl-nlp.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><natural-language-processing>
Title: Is Reinforcement Learning the future of Natural Language Processing?
UserDisplayName: Philip Raeisghasem
ThreadText: # Is Reinforcement Learning the future of Natural Language Processing?
  
  (Asked by: username_0 on 2019-03-16)
  
  I was reading about the [grounding problem](https://en.wikipedia.org/wiki/Symbol_grounding_problem) after seeing it mentioned in another answer today. The article states that, in order to avoid the "infinite regress" of defining all words with other words, we must ground the meaning of some words in the "sensorimotor."
  
  > To be grounded, the symbol system would have to be augmented with nonsymbolic, sensorimotor capacities—the capacity to interact autonomously with that world of objects, events, actions, properties and states that its symbols are systematically interpretable (by us) as referring to.
  
  Obviously, this made me think of Reinforcement Learning. But I'm not exactly sure what counts as "interaction." Would this necessarily imply an MDP-like formulation with rewards, state transitions, etc? Or could some form of grounding be accomplished with supervised learning?
  
  This seems like a pretty fundamental problem of AI. Does anyone know of research being done on grounding words/symbols within an RL agent?
  
  **Tags:** reinforcement-learning, natural-language-processing
  > **Comments:**
  > **username_1**: I think you need to reduce the number of questions at the end. In fact I would remove the "On a related note" paragraph, because it although it is related, answering it could double the length of any answer here. By stating the question, you are setting expectation that someone should attempt to answer it. If you want an answer to that, and it is not already covered by answers to your first question, you can always ask a second, separate question here. That would use the site better, IMO  
  > **username_2**: Have a look at https://github.com/adityathakker/awesome-rl-nlp.  
  
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  ## 0 answers
  
  