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Dialogue Evaluation of DialGLM

Automatic Evaluation

1 Data Processing

To prepare the data, You need to create a directory under the path data/, which contains train.txt, valid.txt, and test.txt.

The functions that process datasets are located in the file data/data_process.py. After the processing, each line in the files should be a dialogue sample. Different utterances are separated with \t and the last utterance is the response that the model needs to generate.

你好,知道北京市规划展览馆在什么地方吗?	嗯呢,地址在北京市东城区前门东大街20号(老北京火车站东侧),电话你要不?	不用了,我已经知道了,电话是010-67017074,帮我查一下这里可以玩多久好了?	大概可以我1小时 - 2小时,知道这里啥时间开放吗?	周一闭馆,周二-周日9:00-17:00,16:00停止入馆,门票贵不?	免费开放,凭有效证件领票入场。	挺好呀,该景点周边有没有其他好玩的地方啊?	好多呢,比如故宫,天安门广场,恭王府等,都是历史遗留的产物。	我想去恭王府看看,能把详细地址发给我吗?	可以,地址在北京市西城区柳荫街甲14号,电话你有没?	有的,电话是010-83288149,这里能够玩多久呀?	差不多能玩2小时 - 4小时吧,门票贵不?	

2 RUN

All the python scripts to run is in src/.

  • generate.py: Invoke the API of the model and generate the response.
  • metric.py: Calculate metrics according to the generated responses.
  • generation_metrics.py: Include utility functions used to calculate metrics.

Before running the code, please change model in the script according to the URL. The results can be found in results/{{model}}/{{testset}}.

Human Evaluation

1. Create database schema

The code for creating database is located in human evaluation/database.

  • init.py: Create the mongodb database and collections.

You need to first modify line 4 and input your own ip address.

Then, run init.py to initialize mongodb.

2. Self-Chat Generation

The code for generating self-chat dialogue is located in human evaluation/self-chat generation.

  • db_op.py: Methods for manipulating mongodb.
  • self_chat_creator.py: Generate self-chat dialogue.

Run self_chat_creator to generate self-chat dialogue.

3. Deploy backend

The code for deploying backend is located in human evaluation/backend.

  • db_op.py: Methods for manipulating mongodb.
  • chatbot_api: Getting response from available chatbot.
  • app.py: Deploy the flask backend.

You should first modify line 4 in db_op.py and input your own mongodb information.

Then, run app.py to deploy the backend.

4. Deploy frontend

The code for deploying frontend is located in human evaluation/frontend

run npm install to setup the project.

run npm run dev to compile and hot-reload for development

run npm run build to compile and minify for production

Implicit Human Evaluation

1. Create database schema

The code for creating database is located in implicit human evaluation/database.

  • init.py: Create the mongodb database and collections.

You need to first modify line 4 and input your own ip address.

Then, run init.py to initialize mongodb.

2. Deploy backend

The code for deploying backend is located in implicit human evaluation/backend.

  • db_op.py: Methods for manipulating mongodb.
  • chatbot_api: Getting response from available chatbot.
  • app.py: Deploy the flask backend.

You should first modify line 4 in db_op.py and input your own mongodb information.

Then, run app.py to deploy the backend.

3. Deploy frontend

The code for deploying frontend is located in implicit human evaluation/frontend

run npm install to setup the project.

run npm run dev to compile and hot-reload for development

run npm run build to compile and minify for production

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