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Conversational Al

  • Language Empowering INtelligent Assistants

  • from 2011 (Apple)
  • Google Now (2012)
    • 被動把方式給你
    • Google Assistant (2016)
  • Microsoft Cortana (2014)
  • Amazon Alex/Echo (2014)
    • 倒數計時應用
    • 購物功能
    • echo 對話
      • 下單
  • Google Hmoe (2016)
  • Appple HomePod (2017)
  • Facebook Portal (2019)
    • 吃 amazon 的 data 當 backend

Why Natural Language?

  • Total Population
    • 7.59B
  • Internet Users
    • 4.02B
  • Active Social Media Users
    • 3.2B
  • Mobile Users
    • 5.14B
    • 用手機的人口,竟然比上網人口還多ㄚㄚㄚㄚ!!! 看看家裡的長輩~ XD
  • Active Mobile Social Users
    • 2.96B

最自然的操作方式,就是用講的!

Why and When We Need?

  • Social Chit-Chat
    • talk like a human
  • Task-Oriented Dialogues
    • information consumption
    • Task completion
    • Decsiion support

Intelligent Assistants

  • 主要幾乎都是,task-oriented dialogues

APP –> Bot

  • A Bot is responsible for a “single” domain, similar to an app.

User 可以自己主動開始對話,而不會依照開發者的 logic 進行!

Two Branches of Conversational AI

  • Chit-Chat
  • Task-Oriented
    • Single-domain, system-initiative
    • Multi-domain, contextural, mixed-initiative
    • End-to-end learning, massively multi-domsin

Task-Oriented

Task-Oriented Dialogue System (Young, 2000)

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  1. Domain Identification
  2. Intent Detection
  3. Slot Filling
    • slot tagging
      • Variations:
        • RNNs with LSTM cells
        • Input, sliding window of n-grams
        • Bi-directional LSTMs
      • encoder-decoder
      • attention-based encode-decoder
    • Multi-Task Slot Tagging
    • Semi-Supervised Slot Tagging

Joint Semantic Frame Parsing

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Contextual Language Understanding

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End-to-End Memory NetWorks

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Dialogue State Tracking (DST)

Multi-Domain Dialogue State Tracking

Dialog State Tracking Challenge (DSTC)

E2E Task-Completion Bot (TC-Bot)

Issues in NLG

  • Issue
    • NLG tends to generate shorter sentences
    • NlG may generate grammatically-incorrect sentences
  • Soluttion
    • Generate word patterns

Conversational AI

  • Issue 1: Blandness Problem
  • Issue 2: Response Inconsistency
  • Issue 3: Dialouge-Level Optimization via RL
  • Issue 4: No Grounding

MMI for Response Diversity

High level Intention learning

Conversational Question Answering

Understanding RCT

GPT (Generative Pre-Training)

Challenge Summary

  • The human-machine interfcae is a hot topic butseveral ocmponents must be integrated

  • Most state-of-the-aret techologies area based on Dnn
    • REquires huge amounts of labed data
    • Serveral frameworrks/models are avilable
  • Fast domain adaptiona with scares datra _re-use of reles/knoledfe
  • Handing resoning and presonalization
  • Data collection adn danaysis from un-sructured dat
  • Complec-casecde