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D17-1195.pdf
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🔗 Source: aclanthology.org
📊 Size: 201 KB
👤 Author: Yangfeng Ji ; Chenhao Tan ; Sebastian Martschat ; Yejin Choi ; Noah A. Smith
⬇️ Downloads: 362

Summary

Researchers presented a new language model, ENTITYNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. The model is generative and flexible, able to model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. Experimental results demonstrate that ENTITYNLM consistently outperforms strong baselines and prior work on tasks such as language modeling, coreference resolution, and entity prediction. The model can be used for various tasks, including language modeling, coreference resolution, and entity cloze tasks, and achieves state-of-the-art performance on the InScript corpus. ENTITYNLM builds on recent advances in representation learning, creating local probability distributions from neural networks, and can be understood as a recurrent neural network language model augmented with random variables for entity mentions that capture coreference and dynamic representations of entities.

Description

Researchers presented a new language model, ENTITYNLM, that can explicitly model entities, dynamically update their representations, and contextually generate...

Technical Information

  • File Format: PDF
  • File Size: 201 KB
  • Pages: 10
  • Language: EN
  • Author: Yangfeng Ji ; Chenhao Tan ; Sebastian Martschat ; Yejin Choi ; Noah A. Smith
  • Total Downloads: 362
  • Last Updated: 5 hours ago

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