so my name is monday madam and i'm presenting today work which was actually done

by my intent

at the beginning of the and the we should but i got vol

okay

so this work is based on the same data set that was just presented no

by on trash

and the this was the first release of that was in march

with an updated version in june actually

this dataset is you have just scene consisting about

fifty j meaning representation reference utterances

but as

off

of the following kind is as you can see here

and a starting point in this work

was to test this idea which

as been advocated in this block quite well known in the our and then deep

learning community

the unreasonable effectiveness of recurrent neural networks

a by an underage property

actually is i is it especially you know if stressing tractor just not about bits

required no

and

we wanted to test this simple id if a can we go

with and out of the box car based sec two sec model

with minimal intervention on all parts

so i in april about the same time as the data for the challenge was

first released

was really this framework by then you brits and collaborate tackle the

tf for transfer rule

sec two sec framework

which was original downfall experiments massive experiments with different configurations option and so on

in your own machine translation

with many yes

options and parameters which are pretty simple

to the two

to concede your

in net namely the number of layers of the hour and then is whether it

is the gru lstm

optimization regimes with the stochastic gradient descent of different types

a bidirectional on coding is possible we so that in the previous talk bidirectional coding

a different attention mechanisms also

and option web based as opposed to cap based

and this is the picture representation from that paper

overview of the model is a standard

on code the decoder sing with this possibilities this options

what we need

so we directly train a complex version of this framework

with bi directional encoding the source and plus some attention mechanism

on the data

namely this means that if you look at this data a namely the meaning representation

name the elicit right so

we take that as a simple string of characters with out any preprocessing any change

to that right

and similarly for the utterance the generated utterance than we hear human produced

we take this string of characters

we don't do any

well pretty a post-processing we don't do any tokenization no low a casing

and maybe very importantly no then lexically station

the icsi causation is we have since produce in some talks

is the process but we you replaced certain and name that it is typically by

such as small but at the start time

so i want to make a note that there isn't the right there is a

problem well known problem with word based model in sec two sec

called the real world problem where well problem

which is due to the fact that you need to have very big vocabularies and

that the value that type sec two sec model

and in section six mobile

doesn't know how to copy

words i it only knows how to

known

that a web scores in the source corresponds to a word in the target and

you need to

to learn these things in the plenty of each other so that means that excuse

nation is way to avoid this problem and all other mechanisms for like coping mechanisms

to handle this problem too

but with a base model you don't have this problem at all because the vocabulary

of symbols

is very small

or in our case in the order of fifty seventy characters were used in total

and no need to do delexicalise

and then and we conducted an evaluation

of our results on a the original dataset

so the bleu score there was a twenty five which is pretty low

but this was used to the fact that the original dataset dean group

the

the human reference a menu several around five missing human references

meaning representation different

and zero that's that the group them

and meaning that the blue evaluation that we did was a basically a single write

the evaluation

which gives much lower result then the result more recent evaluation that we did

on the probably grouped with multi rate

and this gave us all an order of seventy

point blue point which is much more

we also need a small scale human evaluation

i wish to evaluate those and what we found there is that the predictions of

the model where almost perfect in terms of linguistic quality

in terms of grammatical at and naturalness

there were no unknown words produced a normal has been invented words isolated words which

can happen

could i and we scatter bayes model because they produce character by character they don't

evolution of where

and the

annotators of and judge that the

prediction from the model was superior to the actually to the human reference which sometimes

was not was not great

in terms of linguistic

the content is

i thought that where some important semantic adequacy issue

the this the source prediction of the model that the prediction of the model right

was semantically correct in only fifty percent of the cases

and the main problem actually deal almost only problem was the admissions some sometimes a

mission of semantic material

all in or around fifty percent of the cosby test

have a perfect solution boss linguistically and from the point of view of semantic content

was found a into twenty based least

in around seventy percent of the cases

you know and

this is what we're stop that the summation time but

since then we have we've been working on explore trying to exploit that reranking model

models and

and similar scene

so

since a lot

i think

many details because they want to see that the pasta also