How to explain the classifier's decision?

This album is terrible and some of the songs are really bad

NEG

NEU

POS

How to explain the classifier's decision?

This album is terrible and some of the songs are really bad

NEG

NEU

POS

How to explain the classifier's decision?

NEG

NEU

POS

How to explain the classifier's decision?

CREST (ContRastive Edits with Sparse raTionalization)

NEG

NEU

POS

This album is terrible and some of the songs are really bad

CREST-Generation

This album is terrible and some of the songs are really bad

$\bm{x}$

CREST-Generation

This album is terrible and some of the songs are really bad

$\bm{x}$

CREST-Generation

$\bm{z}$

This album is terrible and some of the songs are really bad

$\bm{x}$

CREST-Generation

$\hat{y}$

$\bm{z}$

This album is terrible and some of the songs are really bad

$\bm{x}$

CREST-Generation

$\bm{z}$

This album is terrible and some of the songs are really bad

$\bm{x}$

POS: This album is MASK 
and some of the songs 
are MASK

CREST-Generation

$\bm{z}$

This album is terrible and some of the songs are really bad

$\bm{x}$

$\tilde{\bm{x}}$

POS: This album is MASK 
and some of the songs 
are MASK

Experiments

• Masker: SPECTRA rationalizer [1]

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

• Two datasets: IMDB and SNLI

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

• Two datasets: IMDB and SNLI

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

• Two datasets: IMDB and SNLI

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

• Two datasets: IMDB and SNLI

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

• Two datasets: IMDB and SNLI

– Controllable sparsity

– Contiguity

– Robustness

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

Human Evaluation

• 100 examples (IMDB + SNLI)

• Validity and naturalness (e.g., based on style, tone, and grammar)

• 5-point Likert scale

Human Evaluation

• 100 examples (IMDB + SNLI)

• Validity and naturalness (e.g., based on style, tone, and grammar)

• 5-point Likert scale

Human Evaluation

• 100 examples (IMDB + SNLI)

• Validity and naturalness (e.g., based on style, tone, and grammar)

• 5-point Likert scale

How to leverage counterfactuals?

CREST-Generation

How to leverage counterfactuals?

CREST-Generation

How to leverage counterfactuals?

CREST-Generation

How to leverage counterfactuals?

CREST-Generation

🥰

Exploit the paired structure of factual and counterfactual inputs

CREST-Rationalization

CREST-Rationalization

CREST-Generation

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

FACTUAL  
FLOW

COUNTERFACTUAL  
FLOW

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

FACTUAL  
FLOW

COUNTERFACTUAL  
FLOW

shared

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

FACTUAL  
FLOW

COUNTERFACTUAL  
FLOW

$\bm{z}$

$\tilde{\bm{z}}$

This album is terrible and some of the songs are really bad 

This album is amazing and some of the songs are very well written

shared

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

FACTUAL  
FLOW

COUNTERFACTUAL  
FLOW

$\bm{z}$

$\tilde{\bm{z}}$

This album is terrible and some of the songs are really bad 

This album is amazing and some of the songs are very well written

Predictor

Predictor

$\hat{y}$

$\tilde{y}$

shared

shared

CREST-Rationalization

$\bm{z}^\star$

$\bm{x}$

CREST-Generation

$\tilde{\bm{x}}$

$\tilde{\bm{z}}^\star$

FACTUAL  
FLOW

COUNTERFACTUAL  
FLOW

$\bm{z}$

$\tilde{\bm{z}}$

This album is terrible and some of the songs are really bad 

This album is amazing and some of the songs are very well written

Predictor

Predictor

$\hat{y}$

$\tilde{y}$

shared

shared

Agreement regularization:

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

Original inputs

Original + Human CFs

Original + CREST CFs

CREST-Rationalization

Data Augmentation

in-domain

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

contrastive

out-of-domain

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

• Plausibility (human-likeness)

• Forward simulability (informativeness)

• Counterfactual simulability:

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

input with infilled tokens by $G$ in the positions marked by $\bm{z}$

• Plausibility (human-likeness)

• Forward simulability (informativeness)

• Counterfactual simulability:

ability of a rationale $\bm{z}$ to change the classifier's prediction when the classifier receives a rationale-guided contrastive edit as input

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

0.6733 ± 0.02                        91.70 ± 0.92                          81.18 ± 2.79

0.6718 ± 0.04                        91.44 ± 1.46                          80.53 ± 4.17

0.6758 ± 0.01                        91.68 ± 0.59                          84.54 ± 1.09

0.6904 ± 0.02                        91.93 ± 0.83                          86.43 ± 1.56

Setup

Plausibility
(AUC)

Forward Sim.
(ACC)

Counterfactual Sim.
(ACC)

Data augmentation:

CREST-Rationalization:

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

0.6733 ± 0.02                        91.70 ± 0.92                          81.18 ± 2.79

0.6718 ± 0.04                        91.44 ± 1.46                          80.53 ± 4.17

0.6758 ± 0.01                        91.68 ± 0.59                          84.54 ± 1.09

0.6904 ± 0.02                        91.93 ± 0.83                          86.43 ± 1.56

Setup

Plausibility
(AUC)

Forward Sim.
(ACC)

Counterfactual Sim.
(ACC)

Data augmentation:

CREST-Rationalization:

Interpretability Analysis

Are the rationales generated by CREST-Rationalization interpretable?

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

0.6733 ± 0.02                        91.70 ± 0.92                          81.18 ± 2.79

0.6718 ± 0.04                        91.44 ± 1.46                          80.53 ± 4.17

0.6758 ± 0.01                        91.68 ± 0.59                          84.54 ± 1.09

0.6904 ± 0.02                        91.93 ± 0.83                          86.43 ± 1.56

Setup

Plausibility
(AUC)

Forward Sim.
(ACC)

Counterfactual Sim.
(ACC)

Data augmentation:

CREST-Rationalization:

Conclusions

     •  produces valid, fluent, and diverse counterfactuals
     •  controls the amount of perturbation
     •  leads to plausible explanations
     •  achieves high counterfactual simulability

https://arxiv.org/abs/2305.17075

https://github.com/deep-spin/crest

Selective Rationalization ❤︎ Counterfactual Generation

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

– Controllable sparsity

– Contiguity

– Robustness

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

– Controllable sparsity

– Contiguity

– Robustness

validity

fluency

diversity

closeness

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

– Controllable sparsity

– Contiguity

– Robustness

validity

fluency

diversity

closeness

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

– Controllable sparsity

– Contiguity

– Robustness

validity

fluency

diversity

closeness

Experiments

• Masker: SPECTRA rationalizer [1]
• Editor: Pre-trained T5 model

[1] Guerreiro, N.M. and Martins, A. F. T. SPECTRA: Sparse structured text rationalization. EMNLP 2021.

– Controllable sparsity

– Contiguity

– Robustness

validity

fluency

diversity

closeness

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

91.1 ± 0.3        91.4 ± 0.8       88.5 ± 0.9       76.5 ± 1.6       79.8 ± 1.6       86.0 ± 0.7       88.5 ± 0.7

90.9 ± 0.5        92.9 ± 0.9       90.4 ± 1.6       76.6 ± 1.5       80.7 ± 1.3       86.3 ± 1.0       89.1 ± 1.2

90.8 ± 0.2        91.6 ± 1.3       89.2 ± 0.4       76.7 ± 1.0       80.6 ± 0.6       86.4 ± 0.6       89.1 ± 0.5

91.2 ± 0.5        92.9 ± 0.5       89.7 ± 1.1       77.3 ± 2.3       81.1 ± 2.4       86.8 ± 0.8       89.3 ± 0.7

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp

Experiments

• SPECTRA rationalizer as the masker
• Experiments on IMDB and SNLI

in-domain    contrast    out-of-domain

$F$

$F + C_H$

$F + C_S$

$F \,\&\, C_S$

91.1 ± 0.3        91.4 ± 0.8       88.5 ± 0.9       76.5 ± 1.6       79.8 ± 1.6       86.0 ± 0.7       88.5 ± 0.7

90.9 ± 0.5        92.9 ± 0.9       90.4 ± 1.6       76.6 ± 1.5       80.7 ± 1.3       86.3 ± 1.0       89.1 ± 1.2

90.8 ± 0.2        91.6 ± 1.3       89.2 ± 0.4       76.7 ± 1.0       80.6 ± 0.6       86.4 ± 0.6       89.1 ± 0.5

91.2 ± 0.5        92.9 ± 0.5       89.7 ± 1.1       77.3 ± 2.3       81.1 ± 2.4       86.8 ± 0.8       89.3 ± 0.7

Setup

IMDB

Revised IMDB

Contrast IMDB

RotTom

SST-2

Amazon

Yelp