Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data (D18-1)
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| Challenge: | Existing work on grounded language learning does not capture the semantics of correspondences between structured world state representations and texts. |
| Approach: | They propose to learn explicit latent semantic annotations from paired structured tables and texts . they use an adapted semi-hidden Markov model to impose a soft constraint to further improve performance . |
| Outcome: | The proposed framework improves on a semi-hidden Markov model and extracts templates for language generation. |
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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
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| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
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| Challenge: | In this tutorial, we will discuss the challenges of applying neural variational inference to NLP problems. |
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