IN VIVO

Claidière et al. (2014)

Moussaïd et al. (2015)

IN VITRO

Adamic et al. (2016)

Online data

Transmission chains

Mesoudi and Whiten (2004)

Leskovec et al. (2009)

Realistic content

Control over data-generation

Computational analysis

Already coded

Do-it-by-hand

Simple setting

Danescu-Niculescu-Mizil et al. (2012)

Moussaïd et al. (2015)

Lauf et al. (2013)

Cornish et al. (2013)

Claidière et al. (2014)

Web experiments

Sequence alignments

Lerique & Roth (2017)

Control over experimental setting

Fast iterations

Scale

Exp. 1

MemeTracker, WikiSource,

12 Angry Men, Tales,

News stories

Exp. 2

Memorable/non-memorable quote pairs

(Danescu-Niculescu-Mizil et al., 2012)

Exp. 3

Nouvelles en trois lignes

(Fénéon, 1906)

reading and writing time $\propto$ number of words

At Dover, the finale of the bailiffs convention,their duty said a speaker are delicate, dangerous and detailed

Needleman and Wunsch (1970)

AGAACT-
 | ||
-G-AC-G

AGAACT

GACG

Finding her son, Alvin, 69, hanged, Mrs Hunt, of Brighton, was so depressed she could not cut him down
Finding her son Arthur 69 hanged Mrs Brown from Brighton was so upset she could not cut him down

Apply to utterances using NLP

$\hookrightarrow E_1$

$\hookrightarrow E_2$

Extend to build recursive deep alignments

Frequency

Frequency

Frequency

|chunk|

|chunk|

Deletion

Insertion

Replacement

Position in $u$

$|u|_w$

Number of operations vs. utterance length

Susceptibility vs. position in utterance

Deletions tend to gate other operations

Insertions relate to preceding deletions

Stubbersfield et al. (2015)

Bebbington et al. (2017)

Links the low-level with contrasted outcomes

Susceptibility

Feature variation

Realistic content

Control over data-generation

Computational analysis

Already coded

Do-it-by-hand

Simple setting

Quantitative analysis of changes

Structural changes from exchanges

Relating insertion and deletion chunks

Inner structure of transformations

Sequence alignments of semantic parses

In vivo applications to more complete data sets (social networks)

Sentence processing $\leftrightarrow$ Higher level evolution

Feedback loops: utterance distribution $\leftrightarrow$ detailed transformations

Long-lived chains with recurring changes

Semantic parsing and NLP methods on the inner structure

Connect to the constitution of meaning in interaction and context

Openings

Semantics

Can you think of anything else, Barbara, they might have told me about that party?

I've spoken to the other children who were there that day.

S

B

Abuser

The Devil's Advocate (1997)

?

Strong pragmatics (Scott-Phillips, 2017)

Access to context

Theory of the constitution of meaning

Challenges

$\theta_{open}$

$\theta_{extend}$

$\theta_{mismatch}$

$\theta_{exchange}$ by hand

All transformations

Hand-coded training set size?

Train the $\theta_*$ on hand-coded alignments

Simulate the training process: imagine we know the optimal $\theta$

1. Sample $\theta^0 \in [-1, 0]^3$ to generate artificial alignments for all transformations

2. From those, sample $n$ training alignments

3. Brute-force $\hat{\theta}_1, ..., \hat{\theta}_m$ estimators of $\theta_0$

4. Evaluate the number of errors per transformation on the test set

Test set

$\Longrightarrow$ 100-200 hand-coded alignments yield $\leq$ 1 error/transformation

Immediately after I become president I will confront this economic challenge head-on by taking all necessary steps

immediately after I become a president I will confront this economic challenge

Immediately after I become president, I will tackle this economic challenge head-on by taking all the necessary steps

This crisis did not develop overnight and it will not be solved overnight

the crisis did not developed overnight, and it will be not solved overnight

original

This, crisis, did, not, develop, overnight, and, it, will, not, be, solved, overnight

this, crisis, did, not, develop, overnight, and, it, will, not, be, solved, overnight

this, crisis, did, not, develop, overnight, and, it, will, not, be, solved, overnight

crisi, develop, overnight, solv, overnight

tokenize

lowercase & length > 2

stopwords

stem

The crisis didn't happen today won't be solved by midnight.

crisi, happen, today, solv, midnight

d = 0,6