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One of the interesting idea is to try our AI model on Taiwan official regulation . It will be the same as Talk to Book from Google. Since @jonathanbgn implemented the same model as the Talk to Book, it is probably possible to have the same results?
An other tricky part is that we might not want the "normal" bot just quoting the law . Maybe this can be a separate bot called "Talk To Taiwan Law"?
It will also be a good PR stunt for us if it ends up to be accurate enough
@fifieldt mentioned those links to get some law texts in english :
https://law.moj.gov.tw/Eng/LawClass/LawAll.aspx?PCode=D0080132
https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?PCode=A0030295
https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?PCode=D0080204
https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?PCode=G0340150
We have too many copy/paste of the same answer in the Google Spreadsheet. It starts becoming hard to maintain, and keep all answer updated. It will make it easier for us to add many variations of a question
The parser should be able to take multiple variations of the same question, from a single line. We can strip it with a new line like :
Hello everyone, @erickhun told me about the FAQ bot project which I found quite interesting.
I've been building many side-projects using NLP these past years and I think the easiest solution would be using sentence embeddings to find the most relevant answer to the user's question. Google recently released a light-weight, easy to implement model that you can use directly in Google Sheets to map questions to the best answers.
Here is an overview of how that works:
You can experiment yourself by going to the shared spreadsheet (you'll need to install Semantic Reactor first).
To implement that into a website we could run the model directly in Javascript on the client side using TensorFlow.js (no server needed), or build a small backend to be able to interact with it through Line for example. I could help with that part if needed!
The problem is that now we need to redeploy the bot to repull data from the spreadsheet.
Some ideas:
We know that the LINE group is spammed with questions already answered within the group. So we could scrape the LINE group for valuable questions (and answers). This improves the bot for questions commonly asked in that LINE group; we could then add the bot the the LINE group (or direct others to that bot).
We can start by exporting the chat log (from someone's account who's used it the longest without LINE deleting previous chats) and scraping that. Then we can consider API integration in the future.
Other channels we can consider for scraping:
So if anytime our bot gives a bad answer to a user (we know with the score), we can leave 2 buttons for user to click:
For example, when developing something for slack (replying to thread), i can't use the local emulator.
Today, I literally test in production by going to the server to check out my branch code and reload. ๐ข
We should be able to see (logs?) what questions people asks to the bot , also the answers given and the score (similarity) associated to it.
Just a log file or even writing it in the spreadsheet would be good, so we could adjust the dataset
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