How to implement a conversational chat-bot?

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Natural Language Processing

283 classificações

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National Research University Higher School of Economics

Natural Language Processing

283 classificações

Curso 6 de 7 em Specialization Advanced Machine Learning

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.

Na lição

Sequence to sequence tasks

Nearly any task in NLP can be formulates as a sequence to sequence task: machine translation, summarization, question answering, and many more. In this module we will learn a general encoder-decoder-attention architecture that can be used to solve them. We will cover machine translation in more details and you will see how attention technique resembles word alignment task in traditional pipeline.

Encoder-decoder architecture6:51

Attention mechanism9:27

How to deal with a vocabulary?12:34

How to implement a conversational chat-bot?11:25

Conheça os instrutores

Anna Potapenko
Researcher
HSE Faculty of Computer Science
Alexey Zobnin
Accosiate professor
HSE Faculty of Computer Science
Anna Kozlova
Team Lead
Yandex
Sergey Yudin
Analyst-developer
Yandex
Andrei Zimovnov
Senior Lecturer
HSE Faculty of Computer Science

[MUSIC]

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.

[MUSIC]

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