right name of lena

i am from the natural language dialogue systems

you see characters

i'm presenting the paper modeling linguistic and first now an adaptation for natural language generation


each our view

in the parse tree and marilyn walker

and that the first author is unable to attend its content could be the issue

linguistic adaptation is the phenomenal

and human can first thing in dialogue adjust their be a here is to their

conversational partners

it is also referred to as entrainment for me

here is an example

of linguistic adaptation in the dialogue

if one person said this is why make a less specific apply all users to

specific expression are make a lot

to express the direction

which is considered in certain

feature of an utterance

we call this utterance the prize in response is dialogue are there

can either choose to that on this feature i think yes make allow a specific

avenue or not so that i think yes sir that is the fact that until

we call the response utterance

the target

we will use prime and target rat presentation

linguistic adaptation can happen on different features

for example words referring expressions syntactic structures and discourse markers

previous research has shown that there exists on the correlation between adaptation and task success

and that users prefer

adaptive computer agents

with the right of the intelligent

computer agents such as and of on the lattice a realistic stint in theory it

really need to adapt well certainly make user experience even better

however how much adaptation is needed in natural language generation remains an open question

this paper attempts model linguistic and personality adaptation

for natural language generation

it challenges of this work are first of all we don't really have a good


for turn by turn adaptation

second while most work in this area used human annotated corpora we want to automatically

measure adaptation without human annotation


we also want to work with different feature subset

especially feature sets that reflect linguistic style such as lexical and syntactic features hedges referring

expressions and personality features

that's we propose a new venture dialogue adaptation score yes

comparing it with other measures on data requirement

can be computed on-the-fly

almost other measurements

require complete dialogues to calculate

in terms of linguistic features ta yes measures adaptation of feature sets


measures focus on single linguistic features

the goal that is to model adaptation and dialogue progress

whereas most measures thing to find out

what happens after dialogues complete

here's the definition of the s

any dialogue turn can be considered as i

and target if it's following dialogue turn i different speaker

we form a prime target pairs using window size one for most of our experiments

which means only one target dialogue turn back to the prime is considered

in the following equation and it is the number i'm target her s u i

p the number of features in crime of the prime target here and that

a p to the t is the number of features in both prime and target

we can use we describe da

as follows

the numerator is the number of features but i am and target and the denominator

is the number of features and right

within a feature set yes reflects the average probability of feature is in prime better

at that point

in target across all prime target paris

here is an example showing how the a s works

you have to be calculated with different adaptation target

if we used articles

for the dialogue excerpt on the right we have read prime target source

and gas is calculated as follows

using that word based features if we used articles e

there is only one prime target pair f one hundred and d one o one

we apply yes one for dialogue corpora the first one is our walk corpus are

walking the pedestrian direction task corpus or director instructs the follower define public or in

downtown santa cruz the telephone conversation

there are sixty dialogues total approximately four hundred fifty turn each

i walk in it or is designed to test series of adaptation in the y

the second is the walking around corpus

walking around can also work that's free interaction

time task corpus

where director instructs follower

to eighteen destination on campus yourself well

there are thirty six dialogues in total

the hundred and seventy five to eight hundred and eighty five turn each

is the director combat destinations together with labels in pictures of destinations

destination labels are provided to participants

for example twenty however memorial

and shown in section

this is a corpus designed to elicit adaptation behaviors

the third corpus is the map task corpus

widely used in much previous work

a task is a collection of cooperative task oriented dialogs where director instructs a follower

to reproduce a route on a paperback

error hundred and twenty eight dialogues total with thirty two to four hundred and thirty

for each

the map says

a line drive the landmarks with labels for example are there

the last corpus is the switchboard corpus a collection of topics and


it's one thing is telephone conversations an example topic is to find out what kind

of the other colour path

switchboard had very skewed dialogues where two speakers take turns in the main speaker

for example a parsed first

be back channels than a and b switch

a dialogue a

annotated subset in

available for switchboard

them example the annotations are

e for gas pedal that the for statement opinion

as shown in the sample here

there are over a thousand dialogues and the subset with fourteen to three hundred and

seventy three different each

some theories discourse defined dialogue turns as extending over backchannels and we think that is

but a lower ones

to measure adaptation more faithful

so we utilize as the u e d dialogue act


to filter turns that only contains backchannels

filtering process removes forty point one percent of original dialogue turns only twelve point six

percent of the works

next i will introduce the feature sets we use well-matched previous work on measuring adaptation

use human annotation here we use automatic annotation and feature extraction

it's the end for nlp we extracted unigram lemma for example building and n

and the sample dialog turn and bigram i'm up for example the brick

the thing syntactic features

i taking all subtrees from a parse tree

for example and he structure shown here for than a building

we referring expressions by taking all subtrees with route and he

and removing articles for example a building

we build the dictionary of hedges in discourse markers for example you know and like

we also use linguistic and yuri enquiry and word count


to extract higher level features

we automatically classifies words in over at linguistic and logical and topical categories for example

a sample dialog turn has words in categories second or skin and informal

previous work has also shown that the big five personalities correlate highly with certain look

categories for example

extra version correlates highly

i think the motion in swear words

use all my personality

luke feature sets introduce our previous work

we performed for experiments using in the first experiment we validate our measure

i comparing da f-score is of original version is randomized dialogues

randomized dialogue turns within speakers

we calculate the is scores for each dialogue starting with lexical features

and had other low-level features

syntactic structures referring expressions and hedges and finally at the look features

we performed a paired t-test on all pairs of original and random dialogues


the table shows the average dis scores of different p sure that in four corpora

numbers in bold indicate a statistically significant difference in the paired t-test

in almost all paired t-test

scores the original dialogues for significantly higher in the randomized dialogues

the results indicate that the gas measure sensitive to dialogue turn order

in the second experiment

we aim to compare adaptation models across corpora and feature set

you calculate average gas scores for each corpora and feature set results are shown in

the table

comparing columns we have adaptation models across corpora

one or walking around have similar yes scores which indicate that the yes is able

to reflect the similarity of that you corpora

to map task has higher lexical adaptation

and lower personality adaptation

possibly due to more controlled experiment setting

three switchboard corpus have the highest adaptation scores

despite the fact that switchboard is filtered

it is also possible that the social chitchat setting allows why adaptation to can occur

comparing rows we have adaptation models across features

we have highlighted the is course

well they're linguistic features are largely contents if it consists of higher level features that

have a broader categories

so it's high da f-score

are extracted

gas scores for the limit feature range from zero point one forty zero twenty nine

followed by syntactic structure hedge and bigram

referring expressions have the low with es score

among personality features emotional stability agreeable this and in its to experience

traits are adapted more than extra virgin conscientious this

it yes that are shown in the table here is the same are more column

in the previous table

adapted models

and that adaptation model learn dialogue corpora can be represented as the is there like

is in used to control adaptation

e javier's in different nlg architectures

in overgenerate and range we can calculate it da is better

for every generated response and response with the shortest distance

to learn adaptation model

it statistical

our timit pair parra materially sorry error

you know right nlg we can use yes scores as probability based generation parameters

in your not we can encode

where an adaptation model into context catcher in control general generation behaviors

in the third experiment we explore fine grained adaptation models

first examine adaptation

i dialogue segment

i along the segment at feast one number of targets in the first three taskoriented


switchboard is simply segmented into five parts you can you yes for each segment in

taken averaged across all segments in the same position

in that you features on top of our work

corpora corpus scores for the lose features that slightly decrease of dialogues progress

well expiration two shows a distinct increasing trend

despite being a subset of look

this indicates that as dialogues progress

speaker six the bit more adaptation

behaviors one extra version features

however in switchboard both feature sets display the same decreasing trend

possibly due to the set of the switchboard

and the dialogues progress conversants have less discuss about the topic and are less interested

we then examine adaptation by speaker you compared yes for different speakers

in the figures red lines are da you have scores calculated using target

pupils director or color in switchboard

blue lines for target equals follower or receiver

in general red lines are higher than blue lines which means dialogue initiators

using the bit more adaptation

please refer to the paper for figures other corpora

the last experiment examined the trend of gas course is the window size increases the

window size and mean that a prime is pair and target after it

we begin with the window size of one in gradually increased it to five

is similar to the first experiment we calculate the average gas score for each window


in comparing here scores between dialogue in their original order pizzas dialogues with randomly scrambled


results show that original dialogues shown in red


e f scores decrease its window size increases

randomized dialogs shown in blue lines yes scores stay relatively stable

it's result is consistent with previous work on repetition tk measures

do you conclude this paper shows that when the model for adaptation the and

on the features that used to different

conversational situations can have different adaptation models three the level of adaptation varies according to

which speakers

and the initiative and for the degree of adaptation varies over the course of a


and decreases as the adaptation window size increases

this research was supported by the following grants

if you're time first author is on tonight will be happy to take any questions


although not shown

right speaking

and that'll


no i



one question i wish i

i heard things about four

functions and

you know that


is that the first thing about the results reported question

i think a separate

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where r is

the way to extract

you are so that one

if you are information for example very well

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in a in part so i think i want to use l o l o

or a reprint

adaptation paper we are looking into extract


like a whole

another problem of paper

you are

at this work this actually proposed work what working all to infinity the idea

language generation and was you have reached a whole


you need to find a match you change the order of it

do you have any

finally a match with a human rights respect people have done


your question

what i right there is a war

my question

you need a metric dress i rearrange order

we don't know

if this metric

three what humans would say about whether there's adaptation

so you have any ice metric right

sorry the problem is in most of my understanding is your question

so i rated differently for human evaluation

if a question of or

always useful like that

but not