I'm currently working on a bot that helps a user fill in a form in a conversational way. I'm wondering if I should pick LUIS or azure text analytics for this. Or maybe both? Since I'll need to respond to certain intents but answering a question obviously has no intent. Or can I count answering a question as an intent. Either way I'll have to deal with various types of answers ranging from well ranges to names, dates and sentiments. And to add to this: it'll need to support dutch.
Simple example:
Bot asks:
On a scale of 1 - 10 how do you feel about some subject?
User responds:
Well I would give it a 10
Bot extracts:
feelingScore = 10
More complex example
Bot asks:
How do you feel about some subject?
User responds:
Well I would give it a 10
Bot extracts:
feeling = "10"
User responds:
I honestly didn't feel that good about it.
Bot extracts:
feeling = "not that good" (or possibly a sentiment score)
Since there's no action based on user's input, I don't see a reason for using LUIS. You can achieve what you want using Text Analytics.
PS: Please notice that even tough users reply with a 10, the sentiment analysis may not be 100%.
Example:
"I will give it a 10" -> sentiment score is 95
"Well I would give it a 10" -> sentiment score is 85
But at the same time, you can check the NAMED ENTITIES and get the extracted number from it (when provided in the answer)
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
Related
I have created a pizza bot in dialogflow. The scenario is like..
Bot says: Hi What do you want.
User says : I want pizza.
If the user says I want watermelon or I love pizza then dialogflow should respond with error message and ask the same question again. After getting a valid response from the user the bot should prompt the second like
Bot says: What kind of pizza do you want.
User says: I want mushroom(any) pizza.
If the user gives some garbage data like I want icecream or I want good pizza then again bot has to respond with an error and should ask the same question. I have trained the bot with the intents but the problem is validating the user input.
How can I make it possible in dialogflow?
A glimpse of training data & output
If you have already created different training phrases, then invalid phrases will typically trigger the Fallback Intent. If you're just using #sys.any as a parameter type, then it will fill it with anything, so you should define more narrow Entity Types.
In the example Intent you provided, you have a number of training phrases, but Dialogflow uses these training phrases as guidance, not as absolute strings that must be matched. From what you've trained it, it appears that phrases such as "I want .+ pizza" should be matched, so the NLU model might read it that way.
To narrow exactly what you're looking for, you might wish to create an Entity Type to handle pizza flavors. This will help narrow how the NLU model will interpret what the user will say. It also makes it easier for you to understand what type of pizza they're asking for, since you can examine just the parameters, and not have to parse the entire string again.
How you handle this in the Fallback Intent depends on how the rest of your system works. The most straightforward is to use your Fulfillment webhook to determine what state of your questioning you're in and either repeat the question or provide additional guidance.
Remember, also, that the conversation could go something like this:
Bot says: Hi What do you want.
User says : I want a mushroom pizza.
They've skipped over one of your questions (which wasn't necessary in this case). This is normal for a conversational UI, so you need to be prepared for it.
The type of pizzas (eg mushroom, chicken etc) should be a custom entity.
Then at your intent you should define the training phrases as you have but make sure that the entity is marked and that you also add a template for the user's response:
There are 3 main things you need to note here:
The entities are marked
A template is used. To create a template click on the quote symbol in the training phrases as the image below shows. Make sure that again your entity is used here
Make your pizza type a required parameter. That way it won't advance to the next question unless a valid answer is provided.
One final advice is to put some more effort in designing the interaction and the responses. Greeting your users with "what do you want" isn't the best experience. Also, with your approach you're trying to force them into one specific path but this is not how a conversational app should be. You can find more about this here.
A better experience would be to greet the users, explain what they can do with your app and let them know about their options. Example:
- Hi, welcome to the Pizza App! I'm here to help you find the perfect pizza for you [note: here you need to add any other actions your bot can perform, like track an order for instance]! Our most popular pizzas are mushroom, chicken and margarita? Do you know what you want already or do you need help?
Not able to identify simple phrases like "my name is not Harry, it's Sam".
It is giving me name as harry and company name as Sam, Since name and company name was required in the same sentence.
It should have taken name as Sam and prompted the user again for company name OR should have given complete fallback.
Hi and welcome to Stackoverflow.
Dude. This is not a simple phrase.
Negative questions are always very difficult to catch by Dialogflow.
Suppose I have a question like,
I want to check *google* revenue for the year *2017*
As you can see, google and 2017 are the entities.
But now in the same way if you say,
I don't want to check *google* revenue for the year *2017*
The chances of hitting that old intent is very high as dialogflow matches almost 90% of this sentence with your old sentence. So it might fail.
Hope you are trying to ask something similar to this.
Anyhow coming to your point, If company name and name are different entities, then
Two things you can avoid:
As everyone mentioned,check your entities. The values should not be present in both the entities. This will fail because dialogflow will not know whether it should treat 'Sam' as your name or company name.
If you are not using the values from an entity, instead using '$ANY', then It has a very high chance of failing. And If you are using Dialogflow's system entity like, $given-name, then that is also not preferred as it does not catch all the names. So avoid these entities.
Things you can try:
Train Train And Train. As you would be aware, the training section in dialogflow is pretty good. Train it a few times and it will automatically learn and master it.
But , please note: Wrong training will result in wrong results. It should be 100% accurate. Always check before you approve a training.
And try using webHooks, actions, and/or events to figuring your way out from an external API.
Does dialogflow learn from user data? For example, I built a chatbot which answers some general-purpose queries. Whatever questions a user asks(relevant or irrelevant), does dialogflow uses that to train the agent?
Please let me know if the question is unclear.
DialogFlow has done a very basic NLP for you. What I mean by that is that if a user enters something very similar to what you have added in User Says section the specified Intent will be triggered. But it does not learn on its own. You will have to see the user queries from the training tab and add these queries by yourself in order for it to work.
See, there is basic difference between AI & ML, understanding & interpreting the context of what user wants to say & then performing tasks is AI, on the contrary, ML involves when you try to learn something from user responses & then try to answer. NLP here tries to match user-entered response to user says in dialogflow.com & then compute the score, based on which our bot pick up an intent & answer the user. That's it! In case of dialogflow.com, it will never be like it has stored 1000 user responses & then it has used it for learning & answering. So the answer to user question is No!
Now coming to second point, if you'd like to explicitly train a bot based on specific responses, there's provision to do so. You can move to Training tab & then choose a dialog where you can check matched & unmatched intents. You can choose to right intent & say approve to train your bot. Please check snaps for more understanding.
You can train the agent using the training tab & discard the irrelevant user queries. Is that what you were looking for?
My bot reads and replies in simple mail conversation. More like in chat manner, one or 2 sentences only done through email. My backend is taking care of reading emails, interpreting api.ai responses, storing locally useful data and sending next questions. Before sending to api.ai, messages are split in sentences.
What I've seen from example conversations already done by humans is that the end users are quite often sending several significant information in one sentence. That means that from e.g. 8 possible peaces of information I totally can have (mostly non required) I can get in one sentence any 2 of them.
How to organize that?
I started with one intent for each field I require. But to solve case with any two intents in one sentence, I am extending user says examples with other field too. At the end I will have 8 intents which are actually filled with similar examples.
Now I am thinking to have just one intent and cover all in it. That might work, but the real question is that really way to do it?
Here are example conversations to describe issue better
v1 - simple way like in api.ai examples
- u: Hi. I need notebook bellow $700.
- b: Great. What size should it be?
- u: 17'
- b: I have gaming one at $590 and one professional for $650.
- u: I more to gaming one.
v2 - what I can expect from real life examples
- u: Hi I would like to buy 15 inch gaming laptop.
- great, what price range?
- ...
Api.ai has a feature called slot filling that allows to collect parameter values within a single intent. It's great for building conversational interfaces. You can see if it's compatible with you use case.
Here's how such intent could look like for the examples you provided:
See the "book_notebook" intent:
and how it would work in conversation:
See a test for the "book_notebook" intent:
I'm working on a problem that at the very least seems to require named entity recognition, but I'm not sure how to go farther than the NER parse. What I'm trying to do is parse information (likely from tweets) regarding scheduling of events. So, for example, I'd like to be able to automatically resolve the yes/no answer to the question of "Are The Beatles playing tomorrow?" from short messages like:
"The Beatles cancelled their show tomorrow" or
"The Beatles' show is still on tomorrow"
I know NER will get me close as it will identify the band of interest and the time (if it's indicated), but there are many ways to express the concepts I'm interested in, for example:
"The Beatles are on for tomorrow" or
"The Beatles won't be playing tomorrow."
How can I go from an NER parsed representation to extracting the information of interest? Any suggestions would be much appreciated.
I guess you should search by event detection (optionally - in Twitter); maybe, also by question answering systems, if your example with yes/no questions wasn't just an illustration: if you know user needs in advance, this information may increase the quality of the system.
For start, there are some papers about event detection in Twitter: here and here.
As a baseline, you can create a list with positive verbs for your domain (to be, to schedule) and negative verbs (to cancel, to delay) - just start from manual list and expand it by synonyms from some dictionary, e.g. WordNet. Also check for negations - again, by presence of pre-specified words ('not' in different forms) in a tweet. Then, if there is a negation, you just invert the meaning.
Since you work with Twitter and most likely there would be just one event mentioned in a tweet, it can work pretty well.