The document discusses the development of skill-based conversational agents, including various skills necessary for text-based interactions such as question generation, chit-chat, and summarization. It outlines a competition (ConvAI) evaluating conversational agents and presents their progress in classifier development and future research directions. The authors emphasize a focus on skill implementation rather than traditional finite state machines for managing these conversational skills.

1. Skill-based Conversational
Agent
Idris Yusupov, Yury Kuratov
i.yusupov@phystech.edu, yurii.kuratov@phystech.edu
Moscow Institute of Physics and Technology

2. Plan
1. Intro to Conversational Agents
2. The Conversational Intelligence Challenge @ NIPS (convai.io)
3. Skill-based Conversational Agent

3. What is Conversational Agent?
● Siri (Apple)
● Cortana (Microsoft)
● Alexa (Amazon)
● Chat-Bots (Telegram, Facebook messenger)

4. Two dimensions of Conversational Agents
Artificial Intelligence
Pizza Bot
Siri
ConvAI Bot

5. ConvAI (The Conversational Intelligence Challenge)

6. ConvAI
- 6 Teams (McGill, MIPT, University of Wroclaw, …)
- Human evaluation qualification round (July, 2017)
- 1st place: 2.386 of 5 (overall dialog quality)
- 2nd (Ours): 2.318 of 5
- Released dataset: http://convai.io/data/
- about 2k dialogs
- NIPS Final (December, 2017)
- Talk with bots and help to collect the data:
http://t.me/ConvaiBot

8. Conversation about the text

9. Conversation about the text
- Skill - narrow model.
- What skills are required to discuss the text?
- Question generation (factoid, common, …)
- Question answering (factoid, common, …)
- Chit-chat skill
- Summarization skill
- Personality skill (Name, birthday …)
- …
- Models for skills:
- Seq2Seq
- Retrieval models
- Templates
- Rules
- ...

10. Conversation about the text
- Finite state machine (FSM) to model the conversation
- Hard to maintain

11. Our approach
- Focus on skills implementation

12. What is done. Skills
- Seq2Seq, OpenNMT
- Question generation (SQuAD)
- Chit-chat (Facebook news)
- Chit-chat (Open Subtitles)
- Question Answering (BiDAF)
- Greeting skill
- Common questions asking skill
- Checking user answer correctness skill

13. What is done. Skill classifier

14. Evolution of skill classifier
● Baseline (done)
○ no conversational data
○ use classifier to select skills
● Model with scorer (in progress)
○ we have some data after human evaluation round
○ 2k dialogs (all bots)
○ we are mostly interested in our bot scores
● Model with improved scorer (to be done)
○ data from Mechanical Turk

15. What is done. Dialog evaluation scorer
- 2 evaluation scorers were built by using ConvAI human evaluation dataset
- Current utterance quality scorer: [context, utterance] => (poor, good)
- Word level GRU, sequence length is 50
- Overall dialog quality scorer: [overall dialog] => (poor, neutral, good)
- Word level GRU, sequence length is a whole dialog

16. Future work
● Improve classifier by using current utterance quality scorer
● Setup for human dialog evaluation (Amazon Mechanical Turk, Telegram)
● Bot with bot conversation using dialog scorer
● Improve skills
● New skills (summarization, retrieval based models)

17. Summary
- Skill: it is a narrow model (question generation/answerer, chit-chat, …)
- Conversational agent requires management of such skills
- Management can be done using FSM, but it is hard to maintain them
- Our approach helps to get rid of FSM and focus on skill implementation
- Main idea: use classifier which decides what skill to use
- Future work may lead to interesting results
- Talk with our Telegram bot here: https://t.me/IdrisConvaioTestBot

18. References
1. The Conversational Intelligence Challenge: http://convai.io/
2. Our bot: https://t.me/IdrisConvaioTestBot
3. Seo M. et al. Bidirectional attention flow for machine comprehension //arXiv preprint
arXiv:1611.01603. – 2016.
4. Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2017). Neural Question Generation from
Text: A Preliminary Study. arXiv preprint arXiv:1704.01792.
5. Bordes, A., & Weston, J. (2016). Learning end-to-end goal-oriented dialog. arXiv preprint
arXiv:1605.07683.
6. Serban I. V. et al. A Deep Reinforcement Learning Chatbot //arXiv preprint arXiv:1709.02349. –
2017.
7. Lewis M. et al. Deal or No Deal? End-to-End Learning for Negotiation Dialogues //arXiv preprint
arXiv:1706.05125. – 2017.