Video

Transcript

hi everyone thank you for taking time to

come and see what we’ve been working on

last a couple of months

my name is Manoj Jaya and my project was

on investigating the effectiveness of

using somatic trees in place of regular

sentences when it comes to natural

language processing our problems so to

get started let’s take a look at some

quiz questions okay

I have pairs of sentences that either

mean the same thing or doughnut hole or

somewhere in the middle I want you to

take a look at them think how closely

related in their meaning they are and

come up with a score from 1 to 5 if

that’s too hard for you you could come

up with a score from low medium to high

alright I’m gonna give you couple

seconds to think all right now I’m gonna

tell you this course the first two

sentences they are pretty closely

related in the meaning talking about

dogs fighting wrestling sort of the same

thing the second one not really related

except that it’s somebody doing

something the last one somewhere in the

middle in one sentence

there are kids with a football in the

other there kids with that if the ball

so like I say my goal is to investigate

the effectiveness of using semantic

trees in natural language processing

instead of using a regular sentences

alright so for the tests that I just

gave you the quiz if one is to build a

model for it you’re probably lsdm s

because we’re talking about sentences

it’s sequences and it looks something

like this

data would flow through the Ella stem

cells from the first element of the

input to the end all right and then at

the end of the Elysium layers you have

some combination that gives you a

prediction so now let’s take a look at

the inputs it’s a sentence and

represented as a sequence but is that

really how we really think about

sentences is that really how we portray

the meaning of a sentence if I were to

summarize this sentence let’s say maybe

it’s about I mean it’s really about our

fighting ok I’ll put that down fighting

but who’s fighting some dogs dogs and

when is it happening it’s happening now

or which dogs two dots okay you can see

from the top you have the most important

aspect of the sentence basically the

essence are the meaning and then as you

go down you get more details about how

this is happening the further down you

get you get even more details about the

children are far the top-level aspect

same thing goes with the other sentence

which was in the quiz two dogs are

wrestling and hugging

it’s about wrestling and they’s hugging

happening with wrestling okay let’s move

on the task that I chose for this

investigation was semantic relatedness

so you get two sentences just like in

the quiz and you have to come up with a

school that saves how similar again in

meaning the sentence is armed and I used

it is it cold sick no idea come up with

that name this dataset has 10,000 pairs

of our sentences 10,000 scores to go

into the

the sentences and I also used a glove

which is an embedding for words okay in

order to convert my sentences from this

data from this sick database this is

sick dealer said I used synthetic net

which is very well known as parsing on

phosphates and the output looks

something like this make some mistakes

sometimes so you have to manually

correct it so from there I built a model

again based on SDO I’m sorry LS CMS and

it looks like that same as from before

just with additional details so the

question now is how do I feed my trees

into an LST M because remember elysium

sequences they work with sentences but

trees are you know not quite sequences

so what I did is was to express my trees

in sequential form pretty easy I did

depth-first search

so the sentence on the left ribs end

about that tree which is truth of the

finding Simpson is from before that

becomes fighting the main main idea

forward by the children are fighting

children are dogs and are ok but dogs is

a child of its own so the child appears

in its onset Bart gets set of brackets

right in front of dogs so I trained my

models in fact I’ve trained two models

one I used depends the trees oh by the

way this fries are called dependency

trees so one model I use that this

depends the trees and in the other I

just use regular sentences and again

when I say dependency trees here are I’m

talking about

the sequential presentation alpha the

tip it is dependent the trees from the

precursor slide over here what I

observed was that the model trained on

dependency trees had significantly lower

min squid error a surprise there 0.35

and the one that was trying to radiances

had 1.3 so that’s about three point

seven times the amount of the air if you

look at the scale on the horizontal axis

you can see that the trees model only

trained for very few number a very small

number of cycles so it got to the

perfect level at about 150 steps but the

other model it took about one point

eight so 106 million steps so

significant significantly more they had

about the same amount of for training

loss it’s just one arrived at the same

loss after significantly more steps the

next step from here is to instead of

using a LCM cells I’m planning to use

three LST ohms which is this STM that is

a specifically for our trees so instead

of for working on sipping seeds it works

on trees

I’m also going to try to use that this

same idea on question answering are

using the squad database sorry the squad

dataset what I’ve noticed is that with

that depends the trees it’s actually

really easy to manipulate them so if for

instance you wanted to up meant the

dealer could maybe change the order of

five children in the trees and then come

up with a new set of trees which you can

just pile onto your data set you have

more data it works in most cases there’s

some cases where you just can’t do that

I’d like to thank my mentor I was not

able to come to this event

my name is that yes mean she’s been a

great help and also I’d like to thank

the open e aí team for supporting us

throughout this program all right I’m

gonna open out for questions so feel

free

[Applause]

yeah good that’s a good question this

question was how were the scores for the

sentence pears are calculated wasn’t

actually an empirical score it was

decided by people there was significant

consensus but it’s really not it’s

really not an empirical so you can

really quite measure it

I’m sure you could try to come up with

ways of creating a criteria for you know

what’s a 1 and what’s a 5 and wasn’t

middle but uh yeah it was a lot of

people who curated the data said this

cause and there was a significant

consensus on now on the schools and you

can go and take a look at this cause and

I’ll see how you feel about it when I

was looking at the date of myself just

you know taking browsing through it I

sort of agreed with this course it makes

sense but yeah it’s not empirical yes

great question the question was how did

I fit in the words for the tree and what

I do with the parentheses I used our

glove for embedding the words and then

for the parentheses because you know

parentheses actually do up here in

language you could have sentences that

do have parentheses so I I couldn’t

really use parentheses so what I did is

I created my own symbols and I

specifically made him so that they were

far away from the rest of the words

I give them you know really high values

for their vectors and the two the two

symbols for the opening parentheses in

the closing parentheses closer together

but again they were really far away from

the rest of the day just to make it

create the model that this is these are

not you know this is not the same same

kind of data all right I think that’s

all I have

all right sorry I ran out of time but

thank you so much

[Applause]