# knowledge-driven-dialogue

**Repository Path**: semantic_kg/knowledge-driven-dialogue

## Basic Information

- **Project Name**: knowledge-driven-dialogue
- **Description**: 机器根据知识图谱主动和人类对话，完成给定对话目标，让机器的对话更有目的性，更有逻辑。本开源项目受国家重点研发计划“云计算和大数据”专项支持（项目号  2018YFB1004300 ）。
- **Primary Language**: Python
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2019-09-23
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Proactive Conversation——Knowledge Driven Dialogue
=============================


# Motivation
Human-machine conversation is one of the most important topics in artificial intelligence (AI) and has received much attention across academia and industry in recent years. Currently dialogue system is still in its infancy, which usually converses passively and utters their words more as a matter of response rather than on their own initiatives, which is different from human-human conversation. Therefore, we set up a new conversation task, named proactive conversation which is a type of knowledge driven dialogue, where machines converse with humans based on a built knowledge graph. It aims at testing machines’ ability to conduct human-like conversations.

# Knowledge Graph
We crawled the related knowledge information from the website MTime.com, which records the information of most films, heroes, and heroines in China. We collect both structured knowledge (such as “Harry Potter” is “directed by” “Chris Columbus”) as well as unstructured knowledge including short comments and synopsis.
After the raw data collection, we construct a knowledge graph. Our knowledge graph is comprised of multiple SPO (Subject, Predicate, Object) knowledge triplets, where objects can be factoid facts and non-factoid sentences such as comments and synopsis.


# Paper
* [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://arxiv.org/abs/1906.05572), accepted by ACL 2019

# Task Description
Given a dialogue goal g and a set of topic-related background knowledge M = f<sub>1</sub> ,f<sub>2</sub> ,..., f<sub>n</sub> , the system is expected to output an utterance "u<sub>t</sub>" for the current conversation H = u<sub>1</sub>, u<sub>2</sub>, ..., u<sub>t-1</sub>, which keeps the conversation coherent and informative under the guidance of the given goal. During the dialogue, the system is required to proactively lead the conversation from one topic to another. The dialog goal g is given like this: "Start->Topic_A->TOPIC_B", which means the machine should lead the conversation from any start state to topic A and then to topic B. The given background knowledge includes knowledge related to topic A and topic B, and the relations between these two topics.<br>
![image](https://images.gitee.com/uploads/images/2019/0923/160804_f8cf35c8_5328501.png)
*Figure1.Proactive Conversation Case. Each utterance of "BOT" could be predicted by system, e.g., utterances with black words represent history H,and utterance with green words represent the response u<sub>t</sub> predicted by system.*

# DuConv
We collected around 30k conversations containing 270k utterances named DuConv. Each conversation was created by two random selected crowdsourced workers. One worker was provided with dialogue goal and the associated knowledge to play the role of leader who proactively leads the conversation by sequentially change the discussion topics following the given goal, meanwhile keeping the conversation as natural and engaging as possible. Another worker was provided with nothing but conversation history and only has to respond to the leader. <br>
　 We devide the collected conversations into training, development, test1 and test2 splits. The test1 part with reference response is used for local testing such as the automatic evaluation of our paper. The test2 part without reference response is used for online testing such as the [competition](http://lic2019.ccf.org.cn/talk) we had held and the ___Leader Board___ which is opened forever in https://ai.baidu.com/broad/leaderboard?dataset=duconv. The dataset is available at https://ai.baidu.com/broad/subordinate?dataset=duconv. 

# Baseline Performance
We provide retrieval-based and generation-based baseline systems. Both systems were implemented by [PaddlePaddle](http://paddlepaddle.org/) (the Baidu deeplearning framework). The performance of the two systems is as follows:

| baseline system | F1/BLEU1/BLEU2 | DISTINCT1/DISTINCT2 |
| ------------- | ------------ | ------------ |
| retrieval-based | 31.72/0.291/0.156 | 0.118/0.373 |
| generation-based | 32.65/0.300/0.168 | 0.062/0.128 |

# Competitions
* [Knowledge-driven Dialogue task](http://lic2019.ccf.org.cn/talk) in [2019 Language and Intelligence Challenge](http://lic2019.ccf.org.cn/), has been closed.
  * Teams number of registration：1536
  * Teams number of submission result: 178
  * The Top 3 results:
  
| Rank | F1/BLEU1/BLEU2 | DISTINCT1/DISTINCT2 |
| ------------- | ------------ | ------------ |
| 1 | 49.22/0.449/0.318 |	0.118/0.299 |
| 2 | 47.76/0.430/0.296	| 0.110/0.275 |
| 3 | 46.40/0.422/0.289	| 0.118/0.303 |

* [Leader Board](https://ai.baidu.com/broad/leaderboard?dataset=duconv), is opened forever <br>
  We maintain a leader board which provides the official automatic evaluation. You can submit your result to https://ai.baidu.com/broad/submission?dataset=duconv to get the official result. Please make sure submit the result of test2 part. e