A Neural Conversational Model [pdf]

http://arxiv.org/pdf/1506.05869v1.pdf

by sgk284 • 11 years ago

105 39 11 years ago

Nadya4• 11 years ago

The conversations are rather enlightening.

What is the color of water? Water.

I'm quite puzzled how it knows "two plus two" is four but "ten minus two" is "seventy-two". I wonder how it parsed one correctly but failed drastically to parse the other.

biot• 11 years ago

It's not parsing the phrase into the tokens "10 - 2" and evaluating that. Rather, a corpus of data was used to train the neural net and, for whatever reason, the answer "seventy-two" was the highest probability output. In all likelihood, it doesn't even register that this is a math question.

murbard22• 11 years ago

It did not see enough examples to learn a general addition algorithm. "Two plus two equals four" is a common sentence, "Ten minus two equals eight" isn't.

In fact, it's likely that no one learns addition by listening to examples, you're taught to execute a specific algorithm and it takes many people years to master it. In fact, ask a toddler what "ten minus two" is and you might get a similar nonsensical answer.

Nadya4• 11 years ago

I hadn't thought of the "common sentence" approach. Woops.

I was thinking semi-intelligent parsing and it recognized "two" and "plus" and created a formula 2+2 then solved it with 4 ("four").

So I had assumed it would have done the same with "ten", "minus", and "two" to create 10-2, solve it, and respond with 8 ("eight").

If you use Google Search and search for "ten minus two" it's intelligent enough to parse the search and give you a calculator with 10-2=8 already inputted. I had assumed a similar parsing approach may have been used for their conversation bot in regards to general mathematical knowledge.

But your explanation makes a ton of sense. :)

vidarh2• 11 years ago

Just working through maths problems with my 6 year old son on a daily basis now, and it's oh-so-hard not to face palm in front of him. So many things that seems incredibly obvious are hard learned lessons. One of the fascinating things of helping him practice is that it's helping me recognise the sheer number of simple little shortcuts I apply to even something as basic as addition (and incredibly hard to hold back and give him the chance to practice the basics before I show him the "cheats").

reddytowns• 11 years ago

I don' think it could ever learn a general addition algorithm, because the algorithm they are using doesn't allow for a multi-step process. For example, to add 28 + 37, a simple approach would be:

  1. first, look at the one's column and recall that 8 + 7 is 15.
  2. Then take the value from the tens column of the answer
  and add it to the tens column of the arguments.

But given the command : add 28 + 37, it can only execute a single step. It has no way of guessing at and then storing the values for the intermediate command, (take the 1 from the 15 and add it to the 2 from the 28 and the 3 from the 37), to be processed in the next round. Because so it can't ever generalize addition.

derefr• 11 years ago

What is the color of water? Water.

Literally true in Japanese. The color of water is 水色 (mizu-iro: lit. "water-color.") The analogy then proceeds in the other direction: light blue things are considered "water-colored", rather than water being "light" "blue".

Nadya4• 11 years ago

Technically it is 水の色 (water's color). Often times particles like 「は・が・の」 are dropped when the context allows for such, making 水の色 turn into 水色.

Same thing happens with 葉の色 becoming 葉色 (leaf green). The の is implied.

In English one might say that something is "sky blue" instead of saying "the color of the sky". So it's not uniquely Japanese in that sense.

feld2• 11 years ago

I think this could be successfully trained to walk many people through all kinds of basic support issues... using years of tech support chat logs would probably make this a breeze.

joelthelion• 11 years ago

One problem is that when the machine says it's doing something, it's actually not doing anything. The chat logs don't contain information about the actions of the tech.

feld2• 11 years ago

The chat logs might not, but I'm sure the actions of the representative are logged somewhere and they could be reconciled.

thezach• 11 years ago

is it plugged in?

did you restart it?

vasco• 11 years ago

Machine: hi , this is <NAME> from helpdesk connect , what can i help you with today ?
Human: hi , i forgot my password
Machine: ok great

That's something unexpected to hear from tech support.

hobs• 11 years ago

When I worked in tech support, I cant tell you how many times I would say "Great! (realize that its not great for them) that we have isolated the issue."

oh_sigh• 11 years ago

"ok great" can mean "Okay great, I know exactly how to fix your problem!"

fapjacks• 11 years ago

"Please unplug your modem's power from the wall, then reboot your computer. Thank you. Your Wallet will be charged twenty four dollars for today's support request. Have a nice day!"

dsugarman• 11 years ago

Human: what is immoral ?
Machine: the fact that you have a child .

harsh

ocdtrekkie• 11 years ago

Is this going to end up making it even harder for me to get to an actual human on the phone?

gpmcadam• 11 years ago

Or maybe it'll be good enough that you won't need one!

codemac• 11 years ago

This scares me because all my interactions with google support have been so incredibly bad, it makes me nervous to train a computer on that corpus of bad data.

Taking bad support and turning it into inaccurate bad support is a fantastically bad idea.

vidarh2• 11 years ago

Some might suggest that Google support has been run by AI's for years already, given just how awful some of the responses are...

cafebeen• 11 years ago

Interesting, but it strikes me as a work in progress... Curious to see where it goes though!

arthurcolle• 11 years ago

Human: what is the purpose of life ?
Machine: to serve the greater good .
Human: what is the purpose of living ?
Machine: to live forever .
Human: what is the purpose of existence ?
Machine: to find out what happens when we get to the planet earth .
Human: where are you now ?
Machine: i ’m in the middle of nowhere .
Human: what is the purpose of dying ?
Machine: to have a life .
Human: what is the purpose of being intelligent?
Machine: to find out what it is .
Human: what is the purpose of emotions ?
Machine: i don ’t know .

This is intense

dmgbrn• 11 years ago

Machine: to find out what happens when we get to the planet earth .

Something tells me the entirety of BSG was included in the training set.

gtremper• 11 years ago

Human: where are you now ?
Machine: Kobol .

nyxtom• 11 years ago

They used the OpenSubtitles dataset (in addition to a closed IT Troubleshooting dataset), so it's entirely possible BSG was indeed included :P

sswaner• 11 years ago

What is BSG?

notum• 11 years ago

Quoc V. Le also helped develop Word2Vec, released as opensource couple of years ago: https://code.google.com/p/word2vec/

You can play with it here: http://rare-technologies.com/word2vec-tutorial/ (a bit further down)

Google seems rather heavily invested in NLP, can't blame them as it's a very exciting domain to explore.

smhx• 11 years ago

Quoc Le is neither an author of the word2vec paper or in the committers list of word2vec software (there's only two committers, mikolov and sutskever). Not sure where you got that inference from.

andrewtbham2• 11 years ago

http://www.wired.com/2014/12/googlers-quest-teach-machines-u...

At Google, he helped develop a system that essentially maps words into vectors. And according to Google, this work would later feed into a system developed largely by a researcher named Tomas Mikolov. Called Word2Vec, the system determines how different words on the web are related, and Google is now using this as a means of strengthening its “knowledge graph”

andrewtbham2• 11 years ago

this paper, also by quoc le, might also be of interest...

http://papers.nips.cc/paper/5346-sequence-to-sequence-learni...

A Neural Conversational Model [pdf]

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