I was trying to understand the examples given in RASA core git. I have seen an example story
greet
utter_ask_howcanhelp
inform{"cuisine": "italian"}
utter_on_it
utter_ask_location
But I didn't understand what {"cuisine": "italian"} is. Whether it is the default value of the slot or user has to provide italian in the input string. Can anybody help me to understand how to write stories in RASA core
Regards
One of the most powerful features of any dialog flow is stories. This is how you are telling the model what are the possible flows of conversational dialog.
In the questions you have asked about. Clearly the Italian is not the default value.
inform{"cuisine": "italian"}
Here you are telling the machine learning engine that you are looking for an Intent 'Inform' which will have a slot named cuisine. Here Italian is an example. At the runtime, it can be anything. You can also have another story line where Intent inform without cuisine slot. That story might ask for cuisine in the next dialog.
Defining the story lines, should not be confused with programming language. It is just an indication for Machine learning algorithms.
More details about using slots can be found here and here
This story describes how the dialogue model would behave in the case the user said something like "I want to eat Italian food". As you note, the slot "cuisine" is set to the value "italian".
In the restaurant example, the cuisine slot is a simple TextSlot. This means that the dialogue model only gets to see if the slot has a value or not. The behaviour would be exactly the same if the user had asked for chinese food, thai food, or anything else.
If you want the value of a slot to influence the dialogue going forward, you can use a different slot type, e.g. a categorical slot
Related
I'm currently working on an internationalisation project for a large web application - initially we're just implementing French but more languages will follow in time. One of the issues we've come across is how to display adjectives.
Let's take "Active" as an example. When we received translations back from the company we're using, they returned "Actif(ve)", as English "Active" translates to masculine "Actif" or feminine "Active". We're unsure of how to display this, and wondered if there are any well established conventions in the web development world.
As far as I see it there are three possible scenarios:
We know at development time which noun a given adjective is referring to. In this case we can determine and use the correct gender.
We're referring to a user, either directly ("you") or in the third person. Short of making every user have a gender, I don't see a better approach than displaying both, i.e. "Actif(ve)"
We are displaying the adjective in isolation, not knowing which noun it's referring to. For example in a table of data, some rows might be dealing with a masculine entity, some feminine.
Scenarios 2 and 3 seem to be the toughest ones. Does anyone have any experience handling these issues? Any tips would be appreciated!
This is complex, because we cannot imagine all the cases, and there is risk to go in "opinion based" answer, so I keep it short and generic.
Usually I prefer to give context in translation (for translator), e.g. providing template: _("active {user_name}" (so also the ordering will be correct if languages want different ordering).
Then you may need to change code and template into _("active {first_name_feminine}") and _("active {first_name_masculine}") (and possibly more for duals, trials, plurals, collectives, honorific, etc.). Note: check that the translator will not mangle the {} and the string inside. Usually you need specific export/import scripts. Or I add a note inside the string, and I quickly translate into English removing the note to the translator). Also this can be automated (be creative on using special Unicode characters which should not be used in normal text, to delimit such text).
But if you cannot know the gender, the Actif(ve) may be the polite version used in such language. You need a native speaker test, and changes back and forth.
I am writing an app that has a sign-up form. This article made me doubt everything I knew about human names. My question is: does a person's name necessarily have positive length? Or can I validate names in this way and be confident that I have not denied anyone their identity?
P.S.: one might ask why am I validating at all. The answer is that this is for a school project and proper validation is a part of the mark. The article above proves that person's name can be pretty much any string of positive length but I don't know if zero length is OK.
With all types of programming, you have to draw a distinction between what is meaningful in the real world, and what is meaningful for your software solution.
How the data is to be used will validate what type of validation is required.
For instance, if your software interfaces with a government API, and the government API requires a first name and surname, you should do the same.
If you're interacting with bank accounts, you may have a single string which represents that account name, which many or may not be a human name or not, but may have other constraints around length.
If the name is only to be used for display purposes, maybe there is no point to capture the name at all, and instead you should capture a preferred display name (which doesn't needlessly assume a certain number of name components).
When writing software, you should target to make as few assumptions as possible, unless those assumptions will cause an increase in complexity of your software solution. If the software requires people to have non-empty names, then you should validate at the border that this is true.
In addition, if you were my student, you would have already lost marks for conflating null, and an empty string. In this instance, null would represent you lack data about the name, and an empty string would indicate that user has specified that their name is empty.
Also, if you decide not to validate something, you should at least leave a comment to indicate that you thought of it. If you do something unusual, it's possible a future developer may come along and fix the "bug". In addition, this helps you avoid losing marks.
I trained my luis model to recognize an intent called "getDefinition" with example utterances such as: "What does BLANK mean" or "Can you explain BLANK to me?". It recognizes the intent correctly. I also added an entity called "topic" and trained it to recognize what topic the user is asking about. The problem is that luis only recognizes the exact topic the user is asking about if I used that specific term in one of the utterances before.
Does this mean I have to train it with all the possible terms a user can ask about or is there some way to have it recognize it anyway?
For example when I ask "What does blockchain mean" it correctly identifies the entity (topic) as blockchain because the word blockchain is in the utterance. But if I ask the same version of the question about another topic such as "what does mining mean", it doesn't recognize that as the entity.
Using a list or phrase list doesn't seem to be solving the problem. I want to eventually have thousands of topics the bot responds to, entering each topic in a list is tedious and inconvenient. Is there a way LUIS can recognize that its a topic just from the context?
What is the best way to go about this?
Same Doubt, Bit Modified. Sorry for Reposting this here.
At the moment LUIS cannot extract an entity just based on the the intent. Phrase lists will help LUIS extract tokens that don't have explicit training data. For example training LUIS with the utterance "What does blockchain mean?" does not mean that it will extract "mining" from "What does mining mean?" unless "mining" was either included in a phrase list, or a list entity. In addition to what Nicolas R said about tagging different values, another thing to consider is that using words not commonly found (or found at all) in the corpuses that LUIS uses for each culture will likely result in LUIS not extracting the words without assistance (either via Phrase list or list entity).
For example, if you created a LUIS application that dealt with units of measurement, while you might not be required to train it with units such as inch, meter, kilometer or ounce; you would probably have to train it with words like milliradian, parsec, and even other cultural spellings like kilometre. Otherwise these words would most likely not be extracted by LUIS. If a user provided the tokens "Planck unit", LUIS might provide a faulty extraction where it returns "unit" as the measurement entity instead of "Planck unit".
I got a financial application and I wish to add to it the ability to get user command or input in textbox and then take the right action. for example, wish the user to write "show the revenue in the last 10 days" and it'll show the revenue to him/her - the point is that I wish it to really understand the meaning of the question, so the previus statement will bring the same results as "do I got any revenue in the last 10 days" or something like that - BI (something like the Wolfram|Alpha engine).
I wonder if there's any opensource library or algorithm books or whatever that I can use to learn the subject. Regards to opensource libraries - I don't mind which language it'll be written in.
I've read about this subject and saw many engines and services (OpenNLP, Apache UIMA, CoreNLP etc.) but did not figure out if they're right for my needs.
Any answer or suggestion is welcome.
Many thanks!
The field you're talking about is usually called "natural language processing". It's hard, and an active field of research. There are various libraries which you could consider based on your preferred programming language and use case:
http://en.wikipedia.org/wiki/List_of_natural_language_processing_toolkits
I've used NLTK a little bit. This field is seriously difficult to get right, so you might want to try to restrict your application to some small set of verbs and nouns such that people are using a controlled vocabulary in the first instance, and then try to extend it beyond that.
assuming that I know nothing about everything and that I'm starting in programming TODAY what do you say would be necessary for me to learn in order to start working with Natural Language Processing?
I've been struggling with some string parsing methods but so far it is just annoying me and making me create ugly code. I'm looking for some fresh new ideas on how to create a Remember The Milk API like to parse user's input in order to provide an input form for fast data entry that are not based on fields but in simple one line phrases instead.
EDIT: RTM is todo list system. So in order to enter a task you don't need to type in each field to fill values (task name, due date, location, etc). You can simply type in a phrase like "Dentist appointment monday at 2PM in WhateverPlace" and it will parse it and fill all fields for you.
I don't have any kind of technical constraints since it's going to be a personal project but I'm more familiar with .NET world. Actually, I'm not sure this is a matter of language but if it's necessary I'm more than willing to learn a new language to do it.
My project is related to personal finances so the phrases are more like "Spent 10USD on Coffee last night with my girlfriend" and it would fill location, amount of $$$, tags and other stuff.
Thanks a lot for any kind of directions that you might give me!
This does not appear to require full NLP. Simple pattern-based information extraction will probably suffice. The basic idea is to tokenize the text, then recognize/classify certain keywords, and finally recognize patterns/phrases.
In your example, tokenizing gives you "Dentist", "appointment", "monday", "at", "2PM", "in", "WhateverPlace". Your tool will recognize that "monday" is a day of the week, "2PM" is a time, etc. Finally, you can find patterns like [at] [TIME] and [in] [Place] and use those to fill in the fields.
A framework like GATE may help, but even that may be a larger hammer than you really need.
Have a look at NLTK, its a good resource for beginner programmers interested in NLP.
http://www.nltk.org/
It is written in python which is one of the easier programming languages.
Now that I understand your problem, here is my solution:
You can develop a kind of restricted vocabulary, in which all amounts must end witha $ sign or any time must be in form of 00:00 and/or end with AM/PM, regarding detecting items, you can use list of objects from ontology such as Open Cyc. Open Cyc can provide you with list of all objects such beer, coffee, bread and milk etc. this will help you to detect objects in the short phrase. Still it would be a very fuzzy approach.