CoCoA Collaborative Communicating Agents
We build Collaborative Communicating Agents (CoCoA) that collaborate with humans to achieve a common goal through natural language communication. Each dialogue is centered around a scenario, which provides private knowledge to each agent and optional shared knowledge available to both agents. Two agents communicate using dialogue to achieve a common goal and complete a task based on information in the scenario.
Broadly, many dialogue tasks fall into this framework, for example,
- restaurant/movie searching where the scenario contains the user preference list and a database;
- visually grounded dialogue where the scenario contains a shared scene (image);
- negotiation where the scenario contains terms or issues to discuss.
Projects
He He, Derek Chen, Anusha Balakrishnan, Percy Liang. Decoupling Strategy and Generation in Negotiation Dialogues. EMNLP 2018.
[Download data] [Browse] [Github Code]
The CraigslistBargain task is designed to focus on more realistic scenarios that invites richer language but at the same time requires strategic decision-making. Here, we have two agents negotiate the price of an item for sale on Craigslist.
JVC HD-ILA 1080P 70 Inch TV ($275)
Tv is approximately 10 years old. Just installed new lamp. There are 2 HDMI inputs. Works and looks like new.
A: Hello
B: Hello there
A: So, are you interested in this great TV? Honestly, I barely used it and decided to sell it because I don’t really watch much TV these days. I’m selling it for $275
B: I am definitely interested in the TV, but it being 10 years old has me a bit skeptical. How does the TV look running movies and games, if you don’t mind me asking.
A: It’s full HD at 1080p and it looks great. The TV works like it is brand new. I’ll throw in a DVD player that was hooked up to it for the same price of $275
B: The DVD player sounds nice, but unfortunately I’m on somewhat of a budget. Would you be willing to drop the price a tad, maybe $230?
A: $230 is kind of low. I’ll tell ya what, if you come pick it up where it is located I’ll sell it for $260
B: Throw in a couple of movies with that DVD player,and you have yourself a deal.
A: Deal.
B: OFFER $260.00
A: ACCEPT
He He, Anusha Balakrishnan, Mihail Eric, Percy Liang. Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. ACL 2017.
[Download data] [Browse] [Codalab worksheet] [Github Code]
Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend.
| Name | School | Major | Company |
|---|---|---|---|
| Jessica | Columbia | Computer Science | |
| Josh | Columbia | Linguistics |
A: Hi! Most of my friends work for Google
B: do you have anyone who went to columbia?
A: Hello?
A: I have Jessica a friend of mine
A: and Josh, both went to columbia
B: or anyone working at apple?
B: SELECT (Jessica, Columbia, Computer Science, Google)
A: SELECT (Jessica, Columbia, Computer Science, Google)