Deeper Conversational AI - Part 3

dangdang2222·2022년 12월 8일

Deeper Conversational AI

  1. Reinforcement Learning / Self-Chat ⇒ Human In the Loop
    hard to learn directly from human interaction

    • learning a reward signal directly from human responses and transforming into meaningful update ⇒ but only toy datasets
  2. Few-Shot / Zero-Shot Learning ⇒ Better Strategy
    collecting dataets is hard
    less data-intensive is crucial

    how do we learn to converse in new domain by learning from another domain

    • Zero-Shot learning
      no train data for this domain⇒need to learn from another domain and adapt to this
    • Few-shots learning
      • use pre-trained language model and fine-tune with few examples ex) ToD-BERT, SC-GPT
      • apply Meta-Learning solve new learning tasks using only a small number of training examples Model Agnostic Meata-Learning
      • without persona description, just by using previous dialogue by the same user
  3. Lifelong Learning ⇒ User experience
    relatively unexplored area

    • extract attributes from the previous conversatoin and being able to remeber it and use it
    • add new domain or dialogue skills without retraining all data
  4. Mitigating Inappropriate Response ⇒ On the Model

    • misleading response
    • toxic response
    • gender bias response

    build classifiers to filter out inappropritate examples(preprocessing)
    block n-gram from sensitive word list(decoding)

  5. Multimodal ⇒ still challenge
    talk & able to see
    conversations grounded on images, VR environment

  6. Evaluation ⇒ Better Automatic evalution
    N-gram based ⇒ fail to capture semantic meaning
    turn-level ⇒ fail to capture repetition, consistency between turns
    perplexity vs human evaluation

  7. Shared Tasks&Datasets ⇒ need more

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