I would like to perform an action based on a user specific number extracted from a text request: "Open contract number XYZ". Where 'XYZ' is the parameter value I need. There are no obvious boundaries for the content of XYZ. It could be a true number or it could be a string. Can an entity type be based on the term position within the request? I expect dialogflow can match the intent based on the first part of the phrase: "Open contract number". How can I get the parameter value that follows that phrase?
In theory, yes. For this particular use case, you can use the #sys.any system entity. Just make sure to add many training phrases that can help your agent to detect it.
For example:
Make sure to mark the contract numer (or id) in the training phrases, so it can be correctly detected as a parameter.
Now, you can use the value detected in your responses
Example of a detected intent
Keep in mind, that the use of any is not ideal (can lead to unintended behavior); however, if you add a good amount of training phrases and you use contexts to handle the conversation flow, it should work fine.
An additional suggestion. If there's any recognizable pattern in the contract id, you can use regexp entities instead of #sys.any, which will also increase the accuracy.
Related
I am working on text normalization. I have descriptions of variables/attributes, which I need to convert to correct english.
A an example is shown below:
"This is the sta of the customer's order"
The word 'sta' above needs to be converted to 'status' based on the error and the context.
I tried out a character level encoder decoder architecture, but did not get good results.I need some direction on how to approach this problem.
input :"This is the sta of the customer's order"
output: "This is the status of the customer's order"
This is called spell checking. There are ways to do so, one common way is to use minimum edit distance. An edit is one of these actions : adding a char, removing a char, replacing a char with another, transposing two adjacent chars. You can use edits to make new words out of mistaken words, and use a dictionary to see if the word really exists in English language. There may be more than 1 candidate for each incorrect word to choose from. There are also ways for candidate ranking.
Reading this paper may be a good start :
A Survey of Spelling Error Detection and Correction Techniques
I am trying to make a search algorithm with dialogflow that could take any combination of: first name, address, phone number, zip code or city as input to a search algorithm. The user does not need all of them, but we will refine our search with each additional answer until we only have one result. Basically we are trying to identify which customer we are talking to.
How should this type of intent (or set of intents) be structured? We have tried one intent with multiple parameters, but we do not need all of them to be required. We have also written a JavaScript function for fulfillment but how can we communicate back to dialogflow as to whether we need more information?
Thank you very much for your help.
Slot filling is designed for this purpose.
Hope that helps.
Please post more code/details to help answers be more specific.
First, keep in mind that Intents reflect what the user is saying, and not typically what you're replying with or what other information you need. Slot filling sometimes bends this rule, but only if you have required slots.
Since you don't - you need a different approach.
This can be done with a single intent, although you may find that multiple intents make it easier in some ways. The approach is broadly the same:
When you ask the question, make sure you set an Outgoing Context with a relatively short lifespan (2-3 is good) to indicate you are collecting user info.
Create an Intent (or Intents) that have sample phrases that capture the information you need.
Some of these will have obvious entity types (phone number and zip code) while others will be more difficult (First name has a system entity type, but it doesn't include all possible first names).
You will need to create sample phrases that collect the parameters by themselves, along with phrases that make sense. You're the best judge of this, and you should probably write some sample conversations before you write the phrases.
In your fulfillment, you'll figure out if you have enough information.
If you do, you can reply and clear the Context that was set. (Clearing it is important so Dialogflow doesn't match the information collecting Intent again.)
If you do not, you can add the information you have as parameters to the Context so you can save it for later processing, make sure you reset the Context lifespan (so it doesn't expire), and prompt the user for additional information. Again, having a conversation mocked out ahead of time will help here.
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?
I have a requirement where the user says “ location is ECC86623”, where ECC86 623 is the room number in a building. Is it possible that I can provide an alphanumeric string for intents? Also, I want to take value L1005 also. Or tell me about any method of setting custom values which take every value in the same format like flight number. Is there any way to set the condition like validation or something.
I think what you're meaning to ask about is training phrases as well as extracting and validating the entities from those training phrases (such as location in this case). You can see Dialogflow's Regex sample to get a better idea of how this will look in your fulfillment logic.
We’ve been having a great deal of difficulty with chatbot entities over-generalising on Api.ai, i.e. returning values that have not been specified for that entity when using the “Define Synonyms” feature on custom entities, even when the “Allow automated expansion” flag is turned off.
Our key example is an entity we use for confirming a user choice called confirm_accept. We had an entry: “that’s it”, with synonyms: “thats it”, “that is it”, “that’s it thanks”, “thats it thanks”, “that is it thanks”. This entity value was being returned unexpectedly in expressions where just a stray “it” was appearing.
In general, we have seen a lot of inappropriate entity generalisation which seems to indicate there is some form of stop word removal and stemming/lemmatization going on during entity identification... and which can’t be turned off.
This returns poor entity classifications, making it difficult to create entities for which very precise values are important, e.g. where a single word or character can make a big difference in meaning. Our key use case involves a lot of address processing, so it is important we get back only values we have specified.
Types of over-generalisations we’ve seen include:
inappropriate identification of determiners (a, an, the, this, that, etc.) as part of entities: as in “it” returning “that’s it”
stemmed words: as in stray mentions of “driving”, returning “drive” (a valid street type entity)
inappropriate plural stems: a stray mention of “children” returning “child”, or a stray “will” returning “wills” (which in our case “child” and “wills” are street name entities, so we don’t want “children” or “will” to be returned)
This is currently making it difficult to create a production quality chatbot using the Api.ai service.
Anyone had more luck at either getting a response from Api.ai or solving the over-generalisation problem?
Entities are meant to extract information from conversation:
API.AI's entities are meant to be used to extract data from conversational input not parse different phrases and parts of speech. For your examples (that’s it, thats it, that is it, that’s it thanks, thats it thanks, that is it thanks) all seem to indicate that the user's intent is to indicate that the last message from the API.AI agent was correct. For instances like these, it would be best to use these phrases as examples for an intent or an existing intent with other responses indicating that the user wants to indicate that the last response was correct.
API.AI captures entity tenses and plurals automatically: To address your other concern (driving entity, returning drive value, children returning child, or wills returning wills): API.AI intentionally captures different tenses and plurals of entities to provide a better experience for users who many not know the exact entities you've entered in your database. This allows users of your conversational app to have a natural conversation with your users and not require precise wording or