Richard Socher (Salesforce)

Richard is Chief Scientist at Salesforce, which he joined through acquisition of his startup Metamind. Previously, Richard was a professor in the Stanford CS department.

Why Unified Multi-Task Models for NLP?

  • Multi-task learning is a blocker for general NLLP systems.

  • Unified models can decide how to transfer knowledge (domain adaptation, weight sharing, transfer learning, and zero-shot learning).

  • Unified AND multi-task models can:

    • More easily adapt to new tasks.

    • Make deploying to production X times simpler.

    • Lower the bar for more people to solve new tasks.

    • Potentially move towards continual learning.

The 3 Major NLP Task Categories

  1. Sequence tagging: named entity recognition, aspect specific sentiment.

  2. Text classification: dialogue state tracking, sentiment classification.

  3. Sequence-to-sequence: machine translation, summarization, question answering.

⇒ They correspond to the 3 equivalent super-tasks of NLP: Language Modeling, Question Answering, and Dialogue.

A Multi-Task Question Answering Network for decaNLP


  • Start with a context.

  • Ask a question.

  • Generate the answer one word at a time by:

    • Pointing to context.

    • Pointing to question.

    • Or choosing a word from an external vocabulary.

  • Pointer Switch is choosing between those three options for each output word.

Architecture Design

  • Train a single question answering model for multiple NLP tasks (aka questions).

  • Framework for tackling:

    • More general language understanding.

    • Multi-task learning.

    • Domain adaptation.

    • Transfer learning.

    • Weight-sharing, pre-training, fine-tuning.

    • Zero-shot learning.

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