NeurlPS 2020 - Deeper conversational AI

Part 1 : Conversational AI Overview

conversational system의 기능

• task oriented dialogues
  • information consumption(정보 소비)
  • task completion(임무 수행)
  • decision support(open edded, none specific answer)
• social chit-chat
  • turing test(just chat, no goals)

Modularized Task-Oriented Dialogue Systems

• help acheive pre-defined goals/tasks with least turns
• dealing with(다루다) APIs/databases
  
  user 음성 → NLU → DST → DP → NLG → (기계)음성

1. NLU(Natural Language Understanding)

try to map the utterance(말) into semantic frames(의미론적) = turn level task

input : user utterance (ex: For two people, thanks!)
output : semantic information
constraint : number of people is equal to two

• domain/intent detection ⇒ classification task
  - classify sentence into specific domain or intent
  - cnn, lstm, attention model
• slot tagging ⇒ sequence labelling(IOB)
  - extract value(=slot value)
  - IOB task, name entity recognition task
  - cnn, lstm, rnnem, joint pointer
• slot filling ⇒ compute the slot-F major as the evaluation metrics
  - calculate the frame accuracy
  

trends&challenges

• Joint Intent / Slot Prediction
• Better Scalability
• Better Robustness
  

2. DST(Dialogue State Tracking)

map partial dialogue into dialogue state

input : dialogue(turn with its previous states)
output : dialogue state

• Rule-based
  expert-designed rules(state-update by adding slot values)
  RNN based
• Classification DST(one clasfficier per slot)
  필요한 context마다 분류기를 걸어놓고 이 대화에 이 context가 포함되었나 아닌가, 값은 무엇인가 확인 ⇒ need ontology with predefined values = need to know how many labels will be needed
  - cnn, lstm, context att, hierachical lstm, bert
  

compute slot accuracy for evaluation metrics
joint Acc

trends&challenges
since the limitation of classification, move foward to generation based approach

• Generation DST
  generate state as a sequence or dialogue state updates
  have to multiple foward
• Scalability
  - Multi-Domain(dataset : MultiWoz)
  - Cross-Domain(dataset : SGD)
  - Cross-Lingual
• Other State Representations
  - Graph
  - Queries
  - data-flow
  

3. DP(Dialogue Policy(action) Learning)

design a system action for interacting with people’s based on dialogue states
input : dialogue state + KB results
output : system action

• Supervised Learning
  learn (dialogue state, assistant action) from collected corpus
• Reinforcement Learning : learn from interaction with the user(simulator) ⇒ task success rate / dialogue length (클수록 큰 보상)

trends&challenges

• RL for DP
• User Simulator
  Agenda-based, reward shaping
• Learning dialogue policy using few well-annotated dialogues

4. NLG(Natural Language Generation)

generate corresponding sentence and get back to user
input : system speech-act + slot-value(option)
output : natural language response

• Template-Based
• Generation-Based
  - SC-LSTM
  - Seq2Seq
  - Structural NLG
  - Hierarchical Decoding
  - NLG
  - Hierarchical Decoding
  - Controllable NLG
  논점 : how can we evaluate?
  • calculate the slot error rate
• Hybrid : Template + Generation
  - Rewriting Simple Templates
  

trends&challenges

• Scalability
  - Few-shot domain learning for NLG
  - Unsupervised NLG

Retrieval-Based Chit-Chat Dialogue Systems

• free-form, open-domain
• engaging users for long conversation
• rare API, knowledge

Retrieval-based(검색 기반)

learn a scoring function between dialogue history&response candidate

• PolyEncoder(pretrained)
• Blended-Skill-Task
• Pros(response is from the pool of the response candidates→ safer response)
  • fluent, correct
  • poor scalability : no suitable candidate in new domain

⇒ solution : generation-based model

Generation-based(next chapter)