I am looking to understand how detect intent confidence impacts entity value selection in Dialogflow. For example, using two user generated phrases:
Phrase 1: "For snack yesterday I had an apple and peanut butter". This phrase has an intent detection confidence of '1' and 'snack' and 'yesterday' are tagged correctly to their respective entities, and the foods, 'apple' and 'peanut butter' are correctly matched within their entity [food], with values of 'apple' and 'peanut butter' respectively.
Phrase 2: "Are the snack yesterday I had an apple and peanut butter". This phrase was mumbled by the user or garbled by Siri (we use an iOS voice app). Here the intent detection confidence is '0.852' and while 'snack' and 'yesterday' are tagged to their entities correctly, the foods are not treated as above. Specifically, while both are tagged to the correct entity [food] and 'apple' was correctly tagged to 'apple', the 'peanut' of 'peanut butter' was tagged as one food [value = 'peanut'] and the 'butter' of 'peanut butter' was tagged as another food [value = 'butter'].
As context we have ~500 intents, the intent matched above has ~400 training phrases (clearly not including 'Are the...') and ~200 entities, the largest of which has 29,998 values.
So it appears the intent detection confidence impacts the entity parameter value matching. Can anyone shed any light on this? From our viewpoint, it is not a useful 'feature'. Quite the opposite.
When searching for a matching intent, Dialogflow scores potential matches with an intent detection confidence, also known as the confidence score.
These values range from 0.0 (completely uncertain) to 1.0 (completely certain). Without taking the other factors described in this document into account, once intents are scored, there are three possible outcomes:
If the highest scoring intent has a confidence score greater than or equal to the ML Classification Threshold setting, it is returned as a match.
If no intent meets the threshold, a fallback intent is matched.
If no intents meet the threshold and no fallback intent is defined, no intent is matched.
Update: As per the GCP DialogFlow development team:
Scores are referred to intent recognition confidence
Parameter extraction is not taken into account when scores are computed"
In other words, there is no relationship between intent classification confidence and entity extraction.
The described behavior could potentially be a bug within DialogFlow or something specific to your GCP project and further inspection for your GCP project is required with GCP Support to investigate why this is happening. You can create a GCP Support Case.
Over the last few weeks we have interacted at some length with GCP Support who've interacted with the DF engineering team.
The short answer is they indicate the entity value extraction is 'as designed', and specifically, when using a composite entity (a nested entity structure), values that match input terms (here, 'peanut', 'butter' and 'peanut butter') are extracted and matched at random. So a user's utterance of 'peanut butter' may be matched to 'peanut' and 'butter' or 'peanut butter' at random.
This behavior cannot be controlled by adding additional training phrases to the agent.
From our point of view, the behavior is not as desired but we understand the implications for our design. Hopefully this 'answer' helps others navigate this and similar issues.
Related
I am trying to build a chatbot using Google Dialogflow. I have a set of 100 utterances with me for different Intents. How can I select the training phrases out of this 100 utterance list? Is there any rule to select the unique patterns from the list on the basis of entity pattern or unique paraphrase?
It would be very helpful if you could share the answer here.
Thanks!
Training phrases are example phrases for what end-users might type or say, referred to as end-user expressions. For each intent, you create many training phrases. When an end-user expression resembles one of these phrases, Dialogflow matches the intent.
For example, the training phrase "I want pizza" trains your agent to recognize end-user expressions that are similar to that phrase, like "Get a pizza" or "Order pizza".
You don't have to define every possible example, because Dialogflow's built-in machine learning expands on your list with other, similar phrases. You should create at least 10-20 (depending on complexity of intent) training phrases and try to include more unique training phrases which are relevant to that specific Intent, so your agent can recognize a variety of end-user expressions.
Reference link :https://cloud.google.com/dialogflow/es/docs/intents-training-phrases
I have few intents which are getting trigger on the inappropriate User input. Below are a few examples.
Intent 1). Training phrases I have given
When will I get a job abroad?
Is there any possibility that I will be settled in foreign
When will I settle in foreign
This intent is getting called for user input I had a fight with my friend, will it settle down
Intent 2). Training phrases I have given
When my financial problems will over
Tell me about my financial condition
How will be my financial condition in the future
What will be my financial condition
This intent is getting called for user input When my family problems will over
Please help me out to handle these scenarios.
According to this documentation, you should use at least 10-20 trainning phrases.
You don't have to define every possible example, because Dialogflow's
built-in machine learning expands on your list with other, similar
phrases. You should create at least 10-20 (depending on complexity of
intent) training phrases, so your agent can recognize a variety of
end-user expressions. For example, if you want your intent to
recognize an end-user's expression about their favorite color, you
could define the following training phrases:
"I like red"
"My favorite color is yellow"
"black"
"Blue is my favorite"
...
Given that, to increase the assertiveness of your intents I'd recommend you creating more training phrases and focus them in the main terms necessary in your problem.
For instance, I've created two Intents on Dialogflow, A and B:
A - Question: It's going to rain? -> Answer: yes, it is.
B - Question: what is the color of the sky ? -> Answer: blue
If I ask separately A or B, the answer it's right, but if I ask: "It's going to rain and what is the color of the sky?" It's just answer one of the questions.
It's there a way where the dialogflow can understand the two intents?
Thanks.
Currently, this feature is not present in the dialogflow (or any NLP engine i guess). Dialogflow takes an input sentence and maps it to an intent based on confidence score.
Even if you the NLP engine gives you confidence score of all the intents, you would have problem in combining them.
One solution could be making an intent C which would contain such training phrases, but that would require thorough planning about what users might ask, and what intents should be grouped together to make new intents.
I am facing an issue whereby words that does not match with any intents, it will assume it belongs to intent with the most labeled utterances.
Example: if
Intent A consists of utterances such as Animals
Intent B consists of utterances such as Fruits
Intent C consists of utterances such as Insects
Intent D consists of utterances such as People Name
Desired: If the random word(s) does not fit into any of the luis intent, it will fit into none luis intent. Example of desired: If word such as "emotions" or "clothes" were entered, it will match as "None" intent.
Actual: When user type random word(s), it match with luis intent with highest number of labeled utterances. If word such as "emotions" was entered, it will match as "A" intent as intent A consist of highest number of labeled utterances.
Please advise on the issue.
Set a score threshold, below which your app won't show any response to the user (or could show a "sorry I didn't get you" message instead). This avoid responding to users with anything LUIS is unsure about, which usually takes care of a lot of "off topic" input too.
I would suggest setting it your threshold between 0.3 and 0.7, depending on the seriousness of your subject matter. This is not a configuration option in LUIS, rather in your code you just do:
if(result.score >=0.5) {
// show response based on intent.
} else {
// ask user to rephrase
}
On a separate note, it looks like your intents are very imbalanced. You want to try and have roughly the same number of utterances for each intent, between 10 and 20 ideally.
So without more details on how you've built your language model, most likely the underlying issue is that you either don't have enough utterances in each intent that have enough variation displaying the different ways in which different utterances could be said for that particular intent.
And by variation I mean different lengths of the utterance (word count), different word order, tenses, grammatical correctness, etc. (docs here)
And remember each intent should have at least 15 utterances.
Also, as stated in best practices, do did you also make sure to include example utterances in your None intent as well? Best practices state that you should have 1 utterances in None for every 10 utterances in the other parts of your app.
Ultimately: build your app so that your intents are distinct enough with varying example utterances built into the intent, so that when you test other utterances LUIS will be more likely able to match to your distinct intents--and if you enter an utterance that doesn't follow any sort of pattern or context of your distinct intents, LUIS will know to detect the utterance to your fallback "None" intent.
If you want more specific help, please post the JSON of your language model.
I am using "list" entity. However, I do not achieve my expected result.
Here is what I have for LUIS intent:
getAnimal
I want to get a cat**[animal]**.
Here is what I have with LUIS entities:
List Entities [animal]
cat: russian blue, persian cat, british shorthair
dog: bulldog, german shepard, beagle
rabbit: holland lop, american fuzzy lop, florida white
Here is what I have with LUIS Phrase lists:
Phrase lists [animal_phrase]
cat, russian blue, persian cat, british shorthair, dog, bulldog, german shepard, beagle, etc
Desired:
When user enters "I want to get a beagle." It will be match with "getAnimal" intent.
Actual:
When user enters "I want to get a beagle." It will be match with "None" intent.
Please help. Your help will be appreciated.
So using a phrase list is a good way to start, however you need to make sure you provide enough data for LUIS to be able to learn the intents as well as the entities separate from the phrase list. Most likely you need to add more utterances.
Additionally, if your end goal is to have LUIS recognize the getAnimal intent, I would do away with the list entity, and instead use a simple entity to take advantage of LUIS's machine learning, and do so in combination with a phrase list to boost the signal to what an animal may look like.
As the documentation on phrase lists states,
Features help LUIS recognize both intents and entities, but features
are not intents or entities themselves. Instead, features might
provide examples of related terms.
--Features, in machine learning, being a distinguishing trait or attribute of data that your system observes, and what you add to a group/class when using a phrase list
Start by
1. Creating a simple entity called Animal
2. Add more utterances to your getAnimal intent.
Following best practices outlined here, you should include at least 15 utterances per intent. Make sure to include plenty of examples of the Animal entity.
3. Be mindful to include variation in your utterances that are valuable to LUIS's learning (different word order, tense, grammatical correctness, length of utterance and entities themselves). Highly recommend reading this StackOverflow answer I wrote on how to build your app properly get accurate entity detection if you want more elaboration.
above blue highlighted words are tokens labeled to the simple Animal entity
3. Use a phrase list.
Be sure to include values that are not just 1 word long, but 2, 3, and 4 words long in length, as different animal names may possibly be that long in length (e.g. cavalier king charles spaniel, irish setter, english springer spaniel, etc.) I also included 40 animal breed names. Don't be shy about adding Related Values suggested to you into your phrase list.
After training your app to update it with your changes, prosper!
Below "I want a beagle" reaches the proper intent. LUIS will even be able to detect animals that were not entered in the app in entity extraction.