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Hey, in this video we are going to discuss how to apply encode or
decode attention architecture to a hot topic nowadays, to chat-bots.
First of all, let us understand what do we mean by chat-bots?
As for any hot topic, every one means something slightly different.
So first we can have goal-oriented bots, and
just bots that have a nice conversation with us.
So for goal-oriented bots, we usually speak about some narrow domain and
specific tasks to fulfill.
For example, this can be a bot in a call center in our bank
that can help customers with their needs.
Worth is what can serve some specific
answers to some specific questions of the customers.
It means that usually these bots are based on some retrieval approach, for
example, we could have some database of information, and
then it'll just solve those answers for the users.
Now, entertaining bots or bots that can just have some conversation with us.
I usually call chit chat bots, and the model for them would be generative.
What do I mean by generative models.?
It means that we can just generate new responses.
Just to compare for retrieval-based models,
we would get some predefined responses just ranked from some repository.
Or we would have some patterns, and then we would just
use these patterns to get some specific replies.
Now, there are some pros and cons of two models, obviously for
generated models you have more freedom, you can generate whatever you want.
But you can also have some mistake, and it is more complicated to build these models.
So in this video, we'll speak only about what generative models for
conversational chat-bots.
And those bots that have some goals to assist users with their specific needs,
will be more covered in the next week.
Okay, so let us just recap that we could have our encoder to encode the incoming
message for the bot, and decoder that would generate the bot's response.
And we can have, for example,
LSTMs in encoder and decoder, and we would also want to have attention.
One other alternative for attention would be to have reversed input.
Actually, it is a very simple thing that had been studies before attention.
So, we shall say, maybe we need to reverse the input of the sentence,
and then our thought vector of the sentence will be somehow more meaningful.
Why?
To understand that, let us cover one technical detail that we need to
understand to built any system of that kind.
The detail is about a set effect that all the sequences have different lengths.
Okay, so you have some questions, for example, and you need to somehow
pad those questions to have some fixed length for all of them.
Why?
Just because you need to pack them into batches and
send these batches to your neural network for your.
So it suggests some technical implementation detail that
if you implement as static recurrent neural network,
you need to pad your sequences with some pad tokens.
So if you do this in the end of the sequence,
you could see that the end the sequence is absolutely not meaningful, right?
It is just PAD, PAD, PAD, and so on.
So if you try to build your thought vector base on it, maybe it will not be nice.
That's where you just reverse everything, and
then you have your words in the end of the sequence.
Now you can encode to that, and
the decoder will get some answer for you also pad it as news.
One other idea would be to do bucketing.
What it means is that let us group our sentences into buckets,
based on their length.
For example, those sentences that have the length less than five,
would go into the first bucket.
And then for them, we can just pad them to the length of five.
So this approach will give us an opportunity to have
not that many pad tokens.
Because we will have the adaptable length,
based on the maximum length in each bucket.
The only important thing is to put these buckets into different batches then,
just to make sure that the recurrent neural network will not get
buckets with a different lengths in one of the same batch.
Okay, in the rest of the video, I'm going to show you how the chat-bots work,
but we will also discuss how they do not work.
So you'll see some analysis over the problems, and
just a very ideas of what can be fixed.
So, you can see that this is a human tool machine talk,
trained for movie subtitles, and it is rather impressive.
So, what is the purpose of living?
And the machine says, to live forever, okay sounds good.
But you can also notice that it is very dependent on the data set
that we use to train the model.
So if we try to use the same model, lets see for assistant in a bank,
maybe that's not a good idea.
So there responses will be too dramatic and the topics might be unrealistic.
So it is very important to understand that you have some specific
properties of the outcome, based on the domain of the data.
Now, if you want to use it for calls, let us train it on calls.
So you have some meaningful lexis here, but it is inconsistent.
So the chat-bot says, what is the operating system of your machine?
And the user says linux.
After that, just in a few turns,
the machine says again, so is it a windows machine?
And the user has to answer again.
So this is not nice, the bot doesn't remember what was happening
before in our conversation, and you can try to fix that.
So there is a paper that says, you would need to track the intent and
the context of the conversation with some separate recurrent neural network.
And then we can somehow just memorize for
the bot that these topics have been already covered.
And we do not need to ask again what is the operating system of the machine,
let's say.
So you do not see such problems for this example of the dialogue.
Now another important problem is that the bot has no personality.
So if you try to ask the bot, where do you live now?
The bot can say, I live in Los Angeles, and that sounds okay.
But then if you ask the bot again you will get some other responses.
Just because it was trained on the data of questions and
answers, and the bot doesn't know about consistency in them.
So, one idea would be to build persona-based models.
It means that, we need to memorize that the bot has some personality.
So we just train iton some coherent pieces of dialogues from different persons,
and we built this knowledge of persons.
So that when we ask what is the country, what is the city,
we still get coherent responses.
Now, another problem is diversity in the responses.
So this is a smart reply technology by Google,
that says that it can help you to answer Gmail automatically.
For example, if you see the email, let us meet up and discuss,
you want to get some proposed responses.
And the model would propose how about tomorrow, what about tomorrow,
I suggest tomorrow, so there is no diversity.
And the user cannot pick one of them because all of them
are about the same thing.
Also another problem would be that you have two popular responses
that can come to any email.
Again, you will all have not enough diversity and you will have I love you,
even before some email from a colleague that is not good for your chat-boat.
So how can we cope with that?
Do you have any idea how may be to track that?
One idea would be to do intent clustering for our responses.
For example,
we can have some small supervised data, about the types of the responses.
For example, you can have the label, how about time for
how to how about Friday or something like that.
So you have actually some graph of different responses, and
you have similarities between the responses built by some bearings,
or some distributional semantics model.
Now, you have some labeled nodes in this graph from your supervised data,
and you want to propagate this knowledge to other labels of the graph.
So this technique is called label propagation on graphs, and
Expander is a library that implements this technique.
So the main idea here is that we will try to propagate the labels of the responses,
in such a way that close responses will get close labels.
And those in such a way that those labels that already are known
from our supervision, will be stayed the same, awesome.
So the methods can be different but the idea is just to do something clustering,
and then to pick up one example from every cluster, and suggest it for the user.
So what we get out of it is very nice, so this is the query and
this is the top generated responses.
And you see that now we have how about Tuesday?
I can on Wednesday, I can on some other day, so
you see that you have some diversity, and that's what we want you to have.
Well, even though the bots can try to have some meaningful conversation with you,
you can see that there are still so many problems with them.
And it is so easy to understand that you are speaking to a bot and not to a human.
And that's why actually we should be very careful about that hype.
And we should realize that well, indeed, we are very promising and
we have some good opportunities in the future,
but current models are still not humans.
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