Interventional Rationalization
Authors: Linan Yue
Qi Liu
Li Wang
Yanqing An
Yichao Du
Zhenya Huang
Venue: Submitted to ICLR 2023 (2023)
Keywords: rationalization
causal intervention

Abstract: Selective rationalizations improve the explainability of neural networks by selecting a subsequence of the input (i.e., rationales) to explain the prediction results. Although existing methods have achieved promising results, they still suffer from adopting the spurious correlations in data (aka., shortcuts) to compose rationales and make predictions. Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales. Specifically, we first analyse the causalities among the input, rationales and results with a structural causal model. Then, we discover spurious correlations between input and rationales, and between rationales and results, respectively, by identifying the confounder in the causalities. Next, based on the backdoor adjustment, we propose a causal intervention method to remove the spurious correlations in input and rationales. Further, we discuss reasons why spurious correlations between the selected rationales and results exist by analysing the limitations of the sparsity constraint in the rationalization, and employ the causal intervention method to remove these correlations. Extensive experimental results on three real-world datasets clearly validate the effectiveness of our proposed method.


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INTERVENTIONAL RATIONALIZATION
Anonymous authors Paper under double-blind review
ABSTRACT
Selective rationalizations improve the explainability of neural networks by select- ing a subsequence of the input (i.e., rationales) to explain the prediction results. Although existing methods have achieved promising results, they still suffer from adopting the spurious correlations in data (aka., shorteuts) to compose rationales and make predictions. Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales, Specifi- cally, we first analyse the causalities among the input, rationales and results with a structural causal model. Then, we discover spurious correlations between the input and rationales, and between rationales and results, respectively, by identifying the confounder in the causalities. Next, based on the backdoor adjustment, we propose a causal intervention method to remove the spurious correlations in input and ratio- nales. Further, we discuss reasons why spurious correlations between the selected rationales and results exist by analysing the limitations of the sparsity constraint in the rationalization, and employ the causal intervention method to remove these correlations. Extensive experimental results on three real-world datasets clearly validate the effectiveness of our proposed method.
INTRODUCTION
The remarkable success of deep neural networks (DNNs) in natural language understanding tasks has prompted the interest in how to explain the results of DNNs. Among them, the selective rationalization task[[ei et al.] (2016); Yu et al. (2019) (021)) has received increasing attention, answering the question "What feature has significant impact on the prediction results of the model?". Specifically, the goal of selective rationalization is to extract a small subset of the input (i.e., rationale) to support and explain the prediction results when yielding them. Existing methods often generate rationales with conventional framework consisting of a selector (aka., rationale generator) and predictor Lei et al. (2016). As shown in Figure giving the input X, the selector and the predictor generate rationales R and prediction results Y cooperatively (i.e., P(Y|X) = P(Y R))(R) Among them, the selector (P(R)X) first extracts a subsequence of the input. Then, the predictor (P(Y) R)) yields results based only on the selected tokens, and the selected subsequence is defined as the rationale.
Despite the appeal of the rationalization methods, the current implementation prone to exploit spurious correlations (aka., shortcuts) between the input and labels to yield the prediction results and select the rationales [Chang et al. (2020): Wu et al. (2022). We illustrate this problem with an example of the charge prediction" Considering Figure although this case is corresponding to the Manslaughter, a DNNs model readily predicts the charge as Intentional homicide. Specifically, as Intentional homicide occurs more frequently than Manslaughter and is often accompanied by tokens denoting violence and death, DNNs do not need to learn the real correlations between the case facts and the charge to yield the result. Instead, it is much easier to exploit spurious correlations in data to achieve high accuracy (i.e., predicting the charge as Intentional homicide directly when identifying the tokens about violence and death.). As a result, when facing the cases such as the example in Figure|] []] the effectiveness of such DNNs tends to degrade (c.g., the underlined tokens in Figure denoting the offence is negligent will be ignored in rationales extraction and the charge will be misjudged.). Therefore, these types DNNs depending on spurious correlation in data fail to reveal truly critical subsequence for predicting labels.
Charge prediction: predicting the charge such as Robbery and Theft based on the case fact. Detailed definition of charge prediction is described in section 4.3 https: /wtps//ww///../m//ee/ court gov.t.

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P(Y
The decendant and the victim fought over a trivial matier, the defendant punched the victim the face with the fist, cansing the
The decendant and the vietan lought over trviill
selecto
the defendant punched victim the facc with the for, carriey the
predictor
Manddnugher
ground victim fail and hit his head detendant resulting serious injuries. immediately resusciattdd the vietim but be died after buing sent to huspiial....
victim to full and hit his head on the ground resulting tn serious injuries. The defendout immodiatell resnvcitted the viction but he died being hoopital
the input X
rationales (the bolded)
the result Y
Figure 1: Conventional framework of rationalization presented in this paper. In the charge prediction, the input X represents the case fact and the result Y denotes the charge.
To solve that, Chang et al. (2020) propose an environment-invarion the causal rationales. They argue that the causal rationales should remain stable as the environment shifts, while the spurious correlation between input and labels vary. Although this method performs well in selecting rationales, since the environment in rationalization is hard to observe and obtain, we argue that this "causal pattern" can be further explored to improve the rationalization.
method (INVRAT) to discover
Along this research line, in this paper, we propose an interventional rationalization (Inter- RAT) method which removes the spurious correlation by the causal intervention Glymour et al. (016).
Specifically, motivated by the causal inference theory, we first formulate the causal re relationships among X, R and Y in Structural Causal Model (SCM)/Pean shown in Figure 2(a) Then, we identify the confounder C' in this SCM, which opens two backdoor paths X < C It and R + C Y, making X and R, R and Y spuriously correlated. Next, we address the above correlations, respectively. For spurious correlations between X and R. we assume the confounder is observed and intervene the X (i.e., calculating (()))o(X)) instead of P)R to block the backdoor path and remove the spurious correlations based on the backdoor adjustment Glymour et al. (2016). Among them, the do-operation denotes the pursuit of real causality from X to R. For spurious correlations in R and Y, since by the definition of R (rationales are the only basis for yields prediction results), we argue that there should be no spurious correlations between R and Y. However, in practice, we discover the sparsity constraint commonly defined in rationalization Lei et al. (2016): Cao et al. (2020); Chang et al. (2020)g Yu et al. (2019), ensuring the selector to extract short rationales, results in the spurious correlations between R and Y. Therefore, we further analyse this discovery and employ the causal intervention to remove these correlations.
al. (2000); Glymour et al. (2016) as
Our experiments conducted multi-aspect sentiment analysis dataset BeerAdvocate McAuley et al. a movie reviews prediction dataset MovieReview Zaidan & Eisner (2008) and a legal judgment prediction dataset CAIL Xiao al. (2018). The experimental results validate the effectiveness of removing spurious correlation with causal interventions, where our proposed approach gains an average improvement of 8.6 token Fl Fl-score over the INVRAT baseline BeerAdvocate, 7.4 token Fl-score on MovieReview, and 4.3 Fi-score on CAIL.
THE CONVENTIONAL FRAMEWORK OF RATIONALIZATION
This section formally defines the problem of rationalization, and then presents the details about the conventional rationalization framework consisting of the selector and predictor, where these two components are trained cooperatively to generate rationales and yield the prediction results.
2.1 PROBLEM FORMULATION
Considering text classification task, only the text input X (x1, I2...., where x; represents the i-th token, and the discrete ground truth Y are observed during training, while the rationale R is unavailable. The goal of selective rationalization first adopting the selector to learn a binary mask variable M {m, m2,,,,........................,. ... mn). where m, E {0, 1}. and further select a subsequence of input R = M @ X = {mi I,, m2 Z2...., m, Jn J. and then employing the predictor to re-recode the mask input R to yield the results. Finally, the whole process of rationalization is defined as:
P(Y = R(Y(R) P(R[X).
(1)
predictor selector
"The definition of token Fi-score can be found in section 4.1.1
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2.2 SELECTOR
The selector divides the process of generating rationales into three steps. First, the selector samples each binary value m, from the probability distribution P(M X) {pI, .2.... pn)) where Pj represents the probability of selecting each Ij as the part of the rationale. Specifically, Py is calculated Pj- P(mylar) (zy) softmax(W..felt) (xy)) where the encoder fe(-) encodes the token æ; into d-dimensional vector and W. RR2xd Then, ensure the sampling operation is differentiable, several reparameterization tricks have been proposed such as policy gradient Lei et al. (2016) and HardKuma BBsttings et al. (2019). In this paper, we adopt the Gumbel-softmax method Jang al.
(2017) to achieve this goal:
exp ((log (p,) + 9,) /T) m Σ. exp (log (p:) + 9b) /T)
where is a temperature hyperparameter, 9j from the uniform distribution (0, 1). Finally, the rationale can be selected as M X {m 21, 122 ......, Mf.. -E, Therefore, we conclude that the probability of generating rationales P(R calculated as: P(R]X) P(M x|X) P(M[X).
log lolog (u,)) and uj is random sampled
2.3 PREDICTOR
Based on selected rationale tokens R. the predictor outputs the prediction results (i.e., calculating P(Y R) P X)), and then R can be seen as an explanation of Y. Specifically, after obtaining R from the selector, we adopt the neural network fp(.) to re-encode the rationale into d-dimensional continuous hidden states to yield results. The objective the predictor is defined as:
Lrask E X.Y-Dur (Y, (M @ X))],
M-P(M)X)
where Drr denotes the training set, E(-) represents the cross-entropy loss function, W, E Nxd is the trained parameter and N is the number of labels (e.g., N 2 in the binary classification).
2.4 SPARSITY AND CONTINUITY CONSTRAINTS
Since an ideal rationale should be short and coherent part of original inputs, we add the sparsity and continuity constraints Lei al. (2016); Chang et al. (2020):
1
Cre A1
my
A2
my m
n
j=1
j=2
where the first term encourages the model to select short rationales and a is a predefined sparsity level scale of [0, 1J, and the second term ensures the coherence of selected tokens. Finally, the overall objective of the rationalization is defined as: L Liask + Lrc.
INTERVENTIONAL RATIONALIZATION
In this section, we first reveal how the confounder C causes spurious correlations in rationalization with causal graph. Then, we remove these correlations by using a causal intervention method.
3.1 STRUCTURAL CAUSAL MODEL
As shown in Figure (a)) we formulate the causalities among the text input X, rationale R, ground- truth label and the confounder with Structural Causal Model (SCM) Pearl et al. (2000): Glymour et al. 20166) where the link between two variables represents causal relationship.
In the following, we introduce the causal graph with these variables at a high-level:
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C' -- X. The confounder C' in rationalization can be seen as the context prior, determining which tokens can be "put" into the text input X. For example, in Figure [T] the con- text prior decides where the tokens denoting violence and manslaughter appear, and also decides where other tokens that are meaningless appear. In practice, the confounder is commonly partially observed (e.g., in the text classification, we consider the entire label set as the partially observed confounder. See section 3.2 for details.).
(b)
X + R + C. Besides the selector extracts subsequence of X as the rationale R, making X - holds, R is also affected by the context prior C. Figure [(b] offers a fine-grained causal relationship between R and C' with a
(d)
Figure 2: Structural Causal Model for
mediator K. Specifically, in X + K f- C, K denotes the context-specific representation which is a weighted representation of the prior knowledge associated with X in C. In X + R + K, R is affected by the context prior C through K indirectly. For example, in Figure the underlined tokens denoting negligent are ignored in rationalization, since the context prior in C' misleads the model to focus on the violence and death feature in X by the mediator K. Detailed examples about explanations for this SCM can be found in Appendix A.
Rationalization.
C+Y + R. As the predictor yields the result based on the rationale, Rt + Y holds. Mcanwhile, in the ideal situation, since the rationale is defined there should be no direct causal relationship between C' and Y. However, in practice, rationales / are commonly extracted with shortcut tokens (we will introduce it later in section 3.3). making - Y exists. Figure 2(d) describes fine-grained sem between Y and C' with mediator H, where H is the context-specific representation of by using the context prior C. More detailed descriptions of this SCM are presented in Appendix A.2
subsequence of X sufficient to predict the Y.
From the graph, we find that X and R, R and Y are confounded by the context prior C with two backdoor paths X C + R (or X +- C' + K + R for elaboration) and R + C > Y (or R R+C C + H + Y). The above backdoor paths result in spurious correlations among the text input X, rationale R, and label Y. Based this, we propose causal intervention method to remove the confounding effect by cutting off the link C + X and C + R, respectively.
3.2 CAUSAL INTERVENTION VIA BACKDOOR ADJUSTMENT
To pursue the real causality from X to R (or R to Y), adopt the causal intervention P(R]do(X) instead of (R)X) ) (or P(Y (d(R)) instead of P(Y|R) to remove the effects of confounder C. Next, we introduce the causal intervention method by taking Po (x)) an example, and P(Y Ido R)) is similar. Specifically, since adopting the randomized controlled trial to intervene X is impossible, which requires the control over causal features, apply the backdoor adjustment Glymour al. (0166) to achieve P P(R]do(X)) by cutting off C + X (Figure 2(c)
P(R]do(X))) = Σ P (X,c:)) c.) (c)).
(5)
where the confounder is stratified into pieces C= fc, ee,,,,...., 901). P ((c:)
denotes the prior
distribution of c, which calculated before training, and (.) a function achieving X + K + C. However, the confounder C' is commonly hard to observe. Fortunately, based on the existing researches Wang et al. (2020); Amour 2019), we can consider the entire label set as the partially observed children of the unobserved confounder. Therefore, we approximate it by designing dictionary D. = { fci, C2 . |N as an N x matrix, where N represents the number of labels and d is the hidden feature dimension. As described in section 2.2 we conclude P(R[X) P(M]X). Therefore, we can achieve P (()))o(X)) = (M do(X)). Specifically, to calculate the probability of each token 2, selected as the rationale, the implementation is defined
IN|
|
P(myldo(X)) = Σ (m)
((c))) Σ
(sotmma((f.(t,
(6)
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Among them, fa(-) is the function achieving X - R + K, K2 E K is defined the content- specific representation by using the context prior C₁. we express it as h; = ( = A,c,, where A E X. E RV is the set of the normalized similarity between 2, and each C, in the confounder set C' (i.e.. À = (oftmax(fe(r;)) (²)) Besides, since Eq 6 requires sampling of C and this sampling is expensive, we try to find an alternative function that would be easy to compute to approximate it. Empirically, based on the results in Xu et al. (2015); Wang et al. (202);; Yue et al. (2020). we can adopt the NWGM approximation to move the outer sum into the softmax (i.e., P(mm (d((())) (X) = ( f. f.(,,. k. (c))). In this paper, we adopt the linear model [ k.) Wi W1) + W2h:; = W (Zz) + W2 to fuse the information of the input X and the confounder C. Then, the final implementation of the intervention is formulated as:
[N]
P(m [d)()) = softmax (ainax((iif(r))) + W2
Σ P.G (ci)). i=1
3.3 LIMITATIONS ON THE PREDEFINED SPARSITY & IN RATIONALIZATION
In this section, we discuss why C+ Y in Figure 2(a) holds in detail. Since rationales are defined as the subsequence of inputs, being sufficient to yield results, C + Y should not exist. However, unfortunately, in practical implementation, the sparsity constraint (denoted by a-constraint) in the first term of Eq (4) may result in spurious correlations between the extracted rationale and the predicted result. Specifically, the a-constraint encourages the selector to extract a of tokens from the original text input. When the predefined number of extracted tokens is greater than the length of the practical rationale, a few tokens corresponding to shortcuts of Y may still be selected. For example, as a converges to I, all tokens in the input will be extracted, including the rationales tokens and shortcuts tokens (more examples are shown in Appendix B.I.. Then, the shortcuts tokens will hurt the prediction performance. To alleviate this situation, we first construct fine- tneeggained causal graph (Figure 2 between the selected rationale R and the prediction results Y. Among them, R represents the rationale generated by a-constraint, H denotes the context-specific representation of R based on the context prior C. As mentioned before, from the graph, we find that as there exists backdoor path R 4 C - H Y, R and Y are confounded. Then, based on the above observation, the predictor adopts the causal intervention methods described in section 3.2 (i.c., calculating P(Y R)) softmax( (R, P,) to remove the spurious correlations and further yield prediction results, where fr()) is the function to obtain R + Y + H, and h4 = 9r g,, c4) represents the process of R + H C. Detailed descriptions of the graph high-level and the derivation are shown in Appendix A.2 Besides, although many rationalizers Jain cet al. (2200); Paranjape et al. (2020) do not use a-constraint, we believe their constraint on selecting short rationales can be considered as a variant of a-constraint, as detailed in our Appendix B.2 Then, our intervention method will still be effective on these methods.
EXPERIMENTS
In this section, we validate the effectiveness of our method on three real-world tasks including the beer reviews sentiment analysis, movies reviews prediction and the legal judgment prediction.
4.1 BEER REVIEWS SENTIMENT ANALYSIS
Beer Reviews Sentiment Analysis is formulated as a sentiment prediction task, predicting the ratings (at the scale of [0. 1]) for the multiple aspects of beer reviews (e.g.. appearance, aroma and palate). We use the BeerAdvocate McAuley et al.] as our dataset, which is commonly used in the field of rationalization. As there is high sentiment correlation in different aspects in the same beer review Lei et al. 2016), which may confuse the model training, several researches Lei et al. (2016); Bastings al. 2019) adopt the de-correlated sub-datasets (i.e., part of AverAdv in the training stage. However, a high correlated dataset is more conducive to validating our RAA which is designed to remove the spurious correlations in data. Although Chang et al. | (2020) also conduct a correlated sub-dataset, the data split and processing are not available. Therefore, for fair comparison, different from the previous study which makes experiments on the sub-dataset, we train and validate models on the original BeerAdvocate containing more than 220,000 beer reviews. Besides, following the setup of Chang et al. (2020), we consider the beer review prediction as a binary classification where the ratings < 0.4 as negative and 2 0.6 positive. Then, the processed BeerAdvocate is non-balanced dataset. For example, the label distribution in the appearance is positivennegative ss 20:1. For testing, take manually annotated rationales as our test set, detailed statistics are shown in Appendix C.1
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Table 1: Precision, Recall and F1 of selected rationales for the three aspects. Among them, a the predefined sparsity level.
Methods
Appearance
Aroma
Palate
Procision
Recall
F1
Precision
Recall
Procision
Recall
F]
RNP
0.1
32.440.55
18.6+0.3
23.6+0..4
44,8+0.4
32,4+0.7
37.6++0.5
24.6+0.5
23.5+00.5
24.0=0.5
HardKuma
0.1
53.6.10.1
28.7:
37.4:0.1
14.
25.9.338
27.3::2.1
7.7
6.0:1.11
6.810.1
INVRAT
42.6+40.7
31.5+00.6
36.240.66
41,2+00,3
39.14228
40.1++1.6
34
45.6++0.2
39.5-1.0
Inter-RAT
0.1
66.0+4044
46.5=0.88
54.6=0.7
55,4+0.9
47.5=00.6
51.10088
34,6+0.8
48.2+00,4
40.2=0.5
RNP
0.2
39.4:10.4
44.910.11
42.0:1.22
37.510.11
51.9=0.7
43.5:0.33
21.6:::.4
38.9100.5
27.8=0.4
HardKuma
0.2
64.910.9
6..2
67.0:10.8
37.0
5,
44.51:555
14.6:10.3
22.3110.8
17.7=0.4
INVRAT
0.2
58.9+40.4
67.2+2233
62.8+111
29.3++1.0
52.1-00.6
37.5++0.6
2..0++1.
55.2++2.33
33,5-1.6
Inter-RAT
0.2
62.0:10.5
76.7=:1.7
68.6=0.4
44.2+:0.1
68.4:0.2
52.8:0.11
26.3:0066
59.1+0.88
36.4=0.7
RNP
0.3
24.2410.4
41.2:10.8
30.5:10.5
27.110.33
55.7=1.8
36.410.4
15.4:10.4
42.2:10.9
22.6=0.5
HardKuma
0.3
42.140.3
12
55.7+0.5
24.6+-0.1
57,7-00.6
34.5
21.7+40.1
49.74-0.4
30.2 0.1
INVRAT
0.3
41.540.44
74.8-0.3
53,4+0.3
22,8+1.6
65.1+117
33.8+1.88
20,9+111
71.6+0.4
32.3-1.3
Inter-AAT
0.3
48.1+0.77
82.7=0.5
60.8=0.4
37.9+0.7
72.0=0.11
49.6:10.7
21.8=0.1
66.140.88
32.8=011
4.1.1
COMPARISON METHODS AND EXPERIMENTAL SETUP
In this section, we present the comparison methods with Inter-RAY including RNP Lei et al. 2016).
HardKuma (Bastings et al. (2019) and INVRAT Chang et al. (2020). Among them, RNP is an original rationalization method which generates rationales by yielding the Bernoulli distribution of each token and sampling from it. HardKuma Bastings et al. (2019) has improved RNP by designing a new reparameterized gradient estimation method. INVRAT is our main baseline to directly compare with, which is also a method of removing the spurious correlation in data. The difference with our Inter-RATT is that INVRAT learns environment invariant representations by obtaining multiple environments from the training set.
For training, implement the encoder in both fc(-) and fp(.) as the bidirectional GRU Cho et al. (214)) with
the pre-trained glove embeddings Pennington et al. (014) with size 100, and
hidden size 100. We optimize the objective of rationalization using Adam Kingma & Ba 20 with mini-batch size of 256 and an initial learning rate of 0.001. Besides, we consider the a in Eq (4) as (0.1, 0.2, 0.3], respectively. For testing, we report the token precision, recall and Fl-score to evaluate the quality of selected rationales. Among them, token precision is defined as the percentage of how many the selected tokens are in annotated rationales, and token recall is the percentage of annotated rationale tokens that are selected by model. The token F1-score is calculated as 2eprechsiomereaal precision+
4.1.2 EXPERIMENTAL RESULTS
To demonstrate the effectiveness of our Inter-RAT, we briefly compare it with RNP. HardKuma and INVRAT in T where Inter-RAT outperforms the baselines consistently finding correct rationales. Specifically, Inter-RAT surpasses RNP and HardKuma on all three aspects (i.e, appearance, aroma and palate) by a large margin in most metrics. Besides, although INVRAT has shown helpful in discovering the de-confounded rationales, Inter-RAT still performs better than it, improving 10.5, 14.0 and 1.4 on the average token Fl-score across three aspects, and Inter-RAT has a lower variance illustrating our method is more stable than INVRAT. Such observations strongly demonstrate that Inter-RAT can remove the spurious correlation in data to select rationales effectively.
As discussed in section 3.3 several tokens corresponding to spurious correlations in data may be selected and further mislead the prediction with increasing a. Here, we conduct an experiment to validate the effectiveness of the causal intervention. Since there is only about 1,000 beer reviews in the test set, we report the binary classification Fl Fl-score" with different a in the dev which contains about 30,000 reviews. As shown in Figure [ we make experiments on the palate aspect, and Inter-once is a variant of Inter-RAT, which yields the rationales based on P P(R]do(X)) but predicts the results based on P(Y| [R), rather than P( |do (.)). From the observation, we can conclude that when a is small (i.e., the length of selected rationales smaller than real rationales), the difference between Inter-RAT and Inter-once minor. However, as a increases, Inter-RAT steadily improves, while the Inter-once grows slowly and even degrades. The above observation illustrates that our causal intervention method can alleviate the spurious correlations problem between R and Y caused by the a-constraint.
propose the causal intervention method to alleviate the problem, where
Different from token F1, F1-score is commonly adopted to evaluate the performance of binary classification.

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baokkAT DESRAT
Isc-AAT DVB.1
28
Tokan Rocall
02 82
Profefiad Spasity
Egoch
Figure 3: The Fi-score on the pal- Figure 4: The token precision and recall for rationales on the appearance aspect with = 0.3.
ate aspect with an increasing
As mentioned in section 4.1 make experiments the non-balanced dataset, which is different from the previous study Chang al. (2200) 2019); Huang al. (2021) adopting the balanced datasets. Therefore, there exists a research question we need to answer: the
RLT RATto
Toker
information of label distributions (or prior distributions) somehow influence Inter-RAT to yield better rationales instead of the causal intervention ?". For instance, as the label distribution the appearance aspect posi- tive:negative R 20:1, we conduct experiments appearance dataset with P (c1) = 21
Prokfind
the 20 and P (c2) = य
Figure 5: The token F1 for rationales on the appearance aspect with different prior distributions.
where P (c,) in Eq 5) represents the prior distribution of c; C1 is the positive label and 02 is the negative one.
The above non-balanced label distribution might be inducing the model "better rationalize" for the majority class (i.e., the positive), further reflecting the improvement of Inter-RAT over the whole dataset. Therefore, compute token-F1 scores for positive and negative examples separately for safer evaluation, where we denote the evaluation of Inter-RAT on positive examples as Inter-RAT(+) and the negative ones as Inter-RAT(, Figure summarizes the results on the appearance aspect. From the result, we find that the performance of extracting positive rationales is better than extracting the negative, although the difference between the two types results is not significant, and the scores for the negative are still high (better than INVRAT). Therefore, to further validate the effect of label dis- tribution, we add the analysis as follows: we re-run the experiments with P (c)) P (c2) = (i.e., assuming this is balanced dataset with uniform label distributions) and denote the corresponding model as Mite--RT-ballnnee
We report experimental results Figure 5 From the observation, we find adopting the true prior distribution P (c:) (ltte-RRA)) performs better than the assumed one (ater-RRT-ballncce) which demonstrates the prior distribution is critical for the backdoor adjustment method. Besides, it interesting minority label (i.e., negative) are worse than using the true label distribution, which suggests that Inter-RAT not simply "paying more attention" to instances of the majority class.
that with balanced label distribution, the results of the
Besides, comparing with INVRAT, we investigate the model performance by showing the changes in token precision and recall with training epochs. Figure shows the experiments the appearance aspect with 0.3. From the observation, we can conclude that Inter-RATT significantly outperforms INVRAT in both precision and recall with lower variance from the training onwards, which proves the effectiveness of our proposed method.
4.2 MOVIES REVIEWS PREDICTION
Besides the beer reviews sentiment analysis task, we also make experiments on another binary classification task (i.c.. movie review prediction Zaidan & Eisner (2008) in the FRASER benchmark De Young et al.) (2020). which contains token-level human annotations. We follow the same experimental setups in 4.1 and report the experimental results with
Table 2: Results on movie reviews, where several results baselines are quoted from Yu et (202)).
Movie
Methods
Precision
Recall
RNP Bert RNP
35.6
21.1
24.1
32.2
0.2 in Ta- ble 2 Detailed description of the dataset is shown in Appendix C.I As shown in the table, Inter-RAT per- forms better than RNP, HardKuma and INVRAT on the three metrics, which further validates the effectiveness of Inter-RAT. Furthermore, to validate
HardKuma INVRAT
31.1
28.3
27.0
33.9
24.3
28.3
Inter-RAY RAT Bert_Inter-B
35.7+-0.2
35.8-1.7
35.74-0.8
31.7-0.1
43.1-1.11
36.5-10.3
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Table 3: IJP results CAIL.. Among them, the underline scores the state-of-the-art performances in LJP but lacking explainability. and the results in bold perform second only to NeurJudge but with an explainable rationale. Results of LJP baselines are quoted from Yue al. (22TT)
Charges
Artilees
Penalty
Methods
Acc
MP
MR
F1
Ace
MP
MR
F1
Acc
MP
MR
Fi
Topludge
86.5
87.3
85.8
35.7
32.2
Pew-Shot
88.2
87.5
80.6
82.0
89.4
86.8
77.9
79.5
39.6
37.1
30.9
31.6
LADAN
88.3
86.4
82.1
88.8
85.2
79.5
38.1
34.0
31.2
30.2
NeurJulge
89.9
$7.8
86.8
87.0
90.1
87.2
85.8
$6.1
41.7
40.4
37.2
37.3
RNP
85.1=022
82.2:003
78. 18..5
79.0:10.5
86.51:0.1
82.12:033
77.7:0077
78.8=0.6
37.1:10.2
30.110.3
3033004
27.7:0033
HardKama
86.2=022
84.7=1.2
79.0.0.8
80.610.4
86.810.8
83.34117
07.3
78.9=0.9
35.810.1
34.3113
27.310.3
25.810.4
INVRAT
83.4=0.2
8.1
78.7:0.3
80.::4033
65.1=00.1
83. 03.1=00.
76.2:001
18.1-0.2
38.530,3
34,510077
33.02022
32.0:20.5
RAT
8.24-0.2
87.5.0.3
85.310.3
85.910.3
89.510.1
86.3...1
83.5.0.3
84.6.0.3
39.6.0..1
36.310.4
34.310.3
32.810.3
Inter-RAT is agnostic to the structure of the selector and predictor, we adopt Bert Devlin al. to replace bi-GRU in f-(-) and fp(-) in both RNP and Inter-RAT, and denote them as Bert RNP and Bert Inter-RAT, respectively. From the result, we observe that Bert Inter-RAT still outperforms Bert_RNE illustrating the effectiveness of Inter-RAT.
4.3 LEGAL JUDGMENT PREDICTION
Since there are only two categories (positive and negative) in both beer and movie reviews prediction, we further generalize Inter-RAT to the multi-classie caasiiilassiiiotooonn task. Specifically, focus the Legal Judgment Prediction (LJP) task, which yields the judgment results such as the charges based on the case fact. We conduct experiments on publicly available datasets of the Chinese AT and Law challenge (CAII)). CAIL. contains criminal cases consisting of the fact description and corresponding charges, law articles, and terms of penalty results. For data processing, referring to Yue al. (2021), we remove several infrequent and multiple charges cases, and divide the terms into non-overlapping intervals. The detailed statistics of the datasets can be found in Yue et al. (202T). Figure shows an example of LJP, which predicts the charge according to the case fact.
4.3.1 COMPARISON METHODS AND EXPERIMENTAL. SETUP
In addition to comparing RNP et al. (2016), HardKuma Bastings et al. 2019) and INVRAT INVRATCChang al. (2020), we also compare our method with some classical baselines in the LJP task, including TopJudge Zhong et al. 20188), Few-Shot Hu et al. (018). LADAN [ et al. (020)) and NeurJudge Yue et al. 2222)). D. All the above baselines are trained by exploiting legal particularities. Among them, NeurJudge is the state-of-the-art model in LJP, which adopts different crime circumstances to yield corresponding results. Meanwhile, it employs label embedding method to enhance the prediction. We conduct experiments on one of versions of CAIL containing 134,739 cases Yue et al. (202T). For testing, as there are no annotated rationales, we first employ the accuracy (Acc), macro-precision (MP), macro-recall (MR), and macro-F1 (F1) to evaluate the performance of yielding judgment results. Then, we provide a human evaluation for selected rationales LJP. Detailed description of comparison methods and experimental setups can be found in Appendix C.2
4.3.2 EXPERIMENTAL RESULTS
To evaluate the performance of our model on LJP, we show the experimental results from two aspects. First, 3 shows that our Inter-RAT still performs better than the rationalization methods when generalizing to the multi-classification task. Meanwhile, compared with the LJP approaches (e.g. TopJudge and NeurJudge), even though our model trained on the three subtasks separately, while these LJP approaches explore the dependencies between tasks and are trained with multi-task learning framework, our model still achieves promising performance. However, Inter-RAT does not perform better than NeurJudge. A potential reason is that NeurJudge is designed only for LJP, exploiting the legal particularities well (c.g., crime circumstances). In contract, our Inter-RAT is designed for general text classification tasks. Therefore, the performance of Inter-RAT does not surpass NeurJudge. Furthermore, different from the NeurJudge and other LJP baselines, our Inter- RAT can provide an intuitive explanation (i.c., rationales) when yielding the judgment results while
https: thub. com/ chi ai law cha 1tw.ehhhaeeegge//12//21111111
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LJP baselines fail to produce them. The above observation provides strong validation of adopting the causal intervention method to remove spurious correlation in data for predicting results. Interestingly, we find there exists minor difference between nnter-RAT and NeurJudge yielding the charge and law article. We argue a potential reason is the label embedding method NeurJudge can be approximated the causal intervention method. We further discuss in Appendix [.I in detail.
Second, as CAIL does not provide annotated rationales like Beer- Advocate, we make a human evaluation to evaluate the performance of selected rationales. Specifically, we sample 100 examples and ask human annotators to evaluate rationales in the charge prediction. Besides, following (Sha et al. (20TT) we employ three metrics with an interval from (lowest) to (e.g. 2.0 and 3.2) to evaluate ratio- nales, inc including usefulness (U), completeness (C), and fluency (F). Appendix C.3] describes detailed scoring standards for human anno-
Table 4: Human evaluation on charge prediction.
Churge
Methods
U
C
F
RNP INVRAT
3.85
3.28
3.34
3.88
3.42
3,41
Inter-RAT
4.52
4.10
4.25
tators. The human evaluation results are shown in Table 4 From the results, can find Inter-RAT outperforms RNP and INVRAT in all metrics, further demonstrating our causal intervention method can select more sufficient rationales for yielding results.
5 RELATED WORK
Rationalization. To improve the explainability of DNNs, the rationalization has attracted increasing attention Lei et al. (2066; Treviso & Martins 2020); Bastings et al. (2019); Chang et al. (2019); 9); Yu
et al. 2021p. Specifically, Lei et al. (2016) first proposed rationalization framework which consists of a selector and a predictor. Following this framework, multiple variants were proposed to improve rationalization. Among them, replace the Bernoulli sampling distribution in Lei et al. (2016), Bastings et al. (2019) introduced a HardKuma distribution for reparameterized gradient estimates. And Paranjape al. (2020) studied the Gumbel-sofimax trick for reparameterization. Mcanwhile, they also adopted the information bottleneck method to manage the trade-off between selecting sparse rationales and yielding accurate results. Additionally, another fundamental direction is adding external components to enhance the original framework. Yu et al. (2019) employed an introspective selector which incorporated the prediction results into the selection process. Some researchers Huang al. (202I); (Sha et al. (202T); Cao et al. (2020) proposed an external guider to reduce the difference between the distributions of rationales and input. However, few considered the spurious correlations in data which degraded the rationalization. Among them, Chang al. (2020) discovered the causal rationales with environment invariant methods by creating different environments, /Wu et al. (002)) extracted the rationales from the graph study the explainability of graph neural networks (GNNs)
by the intervention distributions Tian et al. (2006).
Causal Inference. Causal inference Glymour al. (016)) has been widely explored in various fields, including medicine Richiardi et al.] (013) and polities Keele (2015), which aims to empower models the ability to achieve the causal effect. Recently, several researche<Deng & Zhang (2021): Dong et al. 2000):: Yue et al.] (2020) introduced causal inference into machine learning with causal intervention to remove the spurious correlations in data. Especially, it has inspired several studies in natural language understanding such as Named Entity Recognition Zhang et al. (202T), Topic modeling Wu al. (202T), and Relation Extraction Liu et al. (202T). In this paper, we focus on improving the rationalization with causal intervention.
6 CONCLUSION
In this paper, we proposed a causal intervention method (Inter-RAT) to improve rationalization. To be specific, we first formulated the causalities in rationalization with a structural causal model and revealed how the confounder hurt the performance of selecting rationales with opened backdoor paths. Then, considering the entire label set as the observed confounder set, we introduced a backdoor adjustment method to remove spurious correlations between inputs and rationales, and between rationales and results. Besides, we further discussed the potential bias between selected rationales and predicted results caused by the sparsity constraints, and adopted the above causal intervention method to yield de- confoooooddd prediction results. Experimental results on three real-wor worldworld datasets have clearly demonstrated the effectiveness of our proposed method.

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A INSTANTIATED STRUCTURE CAUSAL MODEL
A.1 SCM FOR THE SELECTOR
In this section, we describe SCM for the selector (Figure 2(b) in detail with examples:
C+ X. The context prior determines which tokens can be "put" into the text input X, Among them, the context prior consists of unobserved prior and observed prior (such as the label set). For example, Figure] ! both observed Intentional homicide and Manslaughter priors decide where the tokens denoting violence and death appear the Manslaughter prior determines where the tokens representing manslaughter appear; the unobserved prior decides where other tokens that are meaningless appear.
X + K + C. K denotes the context-specific representation which is weighted representation of the prior knowledge associated with X in C. Taking Figure an example, we assume that the label set consisting of Intentional homicide, Manslaughter, and Theft is the observed prior. Then, we can get the context prior which consists of four parts (i.e.. the Intentional homicide prior cı, the Manslaughter prior C22 the Theft prior and the unobserved prior c₁). Next, we calculate the association between X and C, and obtain the corresponding scores, assuming aj 0.3, d2 = 0.6, 4g = 0.0, a4 0.1, where Munslaughter prior 02 and X are the most relevant, and the Theft prior cg and X are the least relevant. Finally, we can calculate the K as ajc1 + a2 + a303 + 41c4 = 0.3c; + 0.6o2 + 0.1c4.
X R . As the rationale R is subsequence of X, X + Rt holds. Besides, K - R represents the contextual constitution the text that affects the composition rationales. Taking the previous example example, since K is calculated as 0.3c, + 0.6c₂ + 0.1c,, tokens in R will be more inclined with the Manslaughter prior C2.
A.2 SCM FOR THE PREDICTOR
In this section, we describe the detailed causal relation- ship (Figure ((a)) between the selected rationales R and the results Y in the predictor at high-level:
6 G
H1
11
+ R. Based on Eq (5) in section 3.2, we can con- clude that the context prior C determines which tokens are corresponding to rationales.
(a)
Figure 6: SCM for the predictor.
R + H C. H represents the context-specific repre- sentation of R by using the context prior C.
R Y H. As rationales R selected by a-constraint and consist of real rationales and shortcuts tokens, we argue that R + Y holds. Besides, the context prior C' affects the label Y by the mediator H. The reason similar K - R Appendix A.1
From the graph, we can clearly see the context prior C' is the confounder between R and Y, which opens the backdoor path A C' + I| + Y. Therefore, remove the spurious correlations between R and Y, we adopt the causal intervention method to calculate P(Y (oY))((F)) (R)) by cutting the link C (Figure 6(b)
N
P(Y)do(R)) Σ
- (R.
N (()))((R,h)))
Σ
(8)
N
Σ
(R,
where f-(-) is the function achieving R P-YY H, H, gr (R, 3,c,, 3. N
the set of the normalized similarity between R and each e; in the confounder set C (i.e.,
8_ softmax(f, (R)) Among them, fx(-) encodes R into d-dimensional vector, and the dictio-
nary D. (cr, -2, } is approximated as the observed confounder.
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Besides, also adopt the NWGM approximation to Eq (8) and set fr h4) as linear model (i.e., (R, h₂) = Waff(() + Whh; = (Rfp(R) + (.).)) Then, the final objective of intervention is formulated as:
M
P(Y R))
(oftmax((f((R,
IN
2 softmax
((:)))
(9)
1=1
[N|
= softmx(Wa/p(R)
+ W4
Σ 1=1
Bae (c))
B EXPLANATIONS FOR a-CONSTRAINT
B.1 EXAMPLES FOR a-CONSTRAINT
As mentioned in section 3.3 the practical, higher predefined sparsity level may bring shorteuts tokens which hurt the prediction performance, an extreme example being that all tokens in the text input will be selected. Below, we take a beer review as an example to further illustrate this problem, where this example is adopted to predict scores of the smell aspect.
the original text He thinks this beer smells great and tastes terrific.
rationale: smells great
rationale with shortcuts tokens, where a set to 0.5: smells great and tastes terrific. Among them, "and tastes terrific" can be considered as shortcuts tokens.
B.2 DISCUSSIONS ON a-CONSTRAINT
Although several rationalization methods do not set a-constraint to extract rationales, believe that their methods of constraining the short rationales extraction can be considered a variant of a-constraint, and intervention method in section 3.3 will still be effective these methods. Specifically, we argue that these methods should set hyperparameters to encourage the model to select short rationales. However, if the hyperparameters are not set properly, it possible that more shortcuts tokens will be extracted, making It and Y confounded. For example, for several methods Chen 20200; Paranjape al. (2020) adopting the information bottleneck to ensure the model extracts short rationales, there exists a KL divergence between the posterior distribution P(m,la;) and the prior distribution r r(m;), where r(m;) = Bernoulli(T) for some constant т (0, 1). For instance, if we set 0.1, it means we encourage the model to extract 10% of the input text. Therefore, we consider π as a variant a proposed by us.
C SETTING DETAILS
C. STATISTICS OF BEERADVOCATE AND MOVIEREVIEW
In this section, we show the detailed statistics of BeerAdvocate and MovicReview in Table Among them, BeerAdvocate contains three aspects, including appearance, aroma and palate. From the T we can observe that the processed BeerAdvocate is non-balanced dataset. In the training set, the prior distribution is positivernegative 20:1 in appearance, positive:negative 17:3 in aroma, posiltive:negative RO 17:3 in palate. Meanwhile, MovieReview is a balanced dataset with posilive:negative 1:1.
C.2 COMPARISON METHODS AND EXPERIMENTAL SETUPS FOR LJP
In addition to comparing RNP Lei et al. (016)), HardKuma Bastings et al. 2019) and INVRAT Chang et al. 2020) also compare method with some classical baselines the IJP task:
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Table 5: Detailed statistics of the processed BeerAdvocate and Mevie
Train
Dev
Test
Dataset
Pos
# Neg
# Pos
# Neg
Pos
Neg
BeerAdvocate (appcarance)
202,385
12,897
28,488
1,318
955
14
BeerAdvocate (aroma)
172,299
30,564
24,494
3,396
913
31
BeerAdvocate (palate)
176,038
27,639
24,837
3,203
940
24
MovieReview
800
800
100
100
99
100
TopJudge Zhong et al. 2018 explores the dependencies among the three subtasks LJP.
Few-Shot Hu et al.
2018 utilizes the charge attributes to identify the confusing charges.
LADAN Xu al.
2020) learns the distinguished law articles representations for LJP prediction.
Judge Yue et al. (2021) is a circumstance aware approach adopting different crime circum- stances yield corresponding results. Meanwhile, employs label embedding method to enhance the prediction.
For training, we adopt the word2vec Mikolov et al. (2013) for word embedding pre- vre-training with size 200, and set encoder in fa(-) and fp(-) as Bi-GRU. Besides, we implement the learning rate of 0.0002 with batch size 256, and take as 0.2. For evaluating, we employ the accuracy (Acc), macro-precision (MP), macro-recall (MR), and macro-FI (FI) to evaluate the performance of yielding judgment results.
C.3 SCORING STANDARDS FOR HUMAN EVALUATION
Following Sha et al. (021) we evaluate the rationales with three metrics: usefulness (U), complete- ness (C), and fluency (F) in the charge prediction. Among them, each scored from I (lowest) 5. Below, we introduce scoring standards for the above metrics in brief. Detailed standards for human annotators can be found in Sha (202
C.3.1 USEFULNESS
Q: Do you think the selected rationales can be useful for explaining the predicted labels?
5: Exactly. Selected rationales useful for get the correct label.
4: Highly useful. Although several tokens have no relevance to correct label, most selected tokens are useful to explain the labels.
3: Half of them are useful. About half of the tokens are useful for getting labels.
2: Almost useless. Almost all of the tokens are useless.
1: No Use. The selected rationales useless for identifying labels.
C.3.2 COMPLETENESS
Q: Do you think the selected rationales are enough for explaining the predicted labels?
5: Exactly. Selected rationales enough for me to get the correct label.
4: Highly complete. Several tokens related to the label are missing.
3: Half complete. There are still some important tokens that have not been selected, and they are in nearly the same number as the selected tokens.
2: Somewhat complete. The selected tokens are not enough.
1: Nonsense. None of the important tokens is selected.
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C.3.3 FLUENCY
Q: Do you think the selected rationales fluent?
5: Very fluent.
4: Highly fluent.
3: Partial fluent.
2: Very unfluent.
1: Nonsense.
D MORE EXPERIMENTAL RESULTS
D.1 A CAUSAL VIEW ON NEURJUDGE
In this section, from the causal view, we discuss the reason why the difference between Inter-RAT and NeurJudge on the charge and law article prediction is not significant. Here, we explain the observation by taking the charge prediction NeurJudge adopts a label embedding method to incorporate the semantics of charge into the case fact to yield the corresponding result. We argue that this method be approximated intervention method. To illustrate this discovery, we assume Figure [[d) the SCM of the charge prediction task, and consider the case fact R (i.e., a=1) and the charge label set as C. Then the process of label embedding can be formulated as R H < C and R Y + H. The objective of NeurJudge is written as:
example, and the article prediction is similar. Specifically,
the causal
I
P(Y R) = softmax
Σ i=1
Ineru R. hs)),
(10)
where 44 = Ineru RR c.). We can find the difference between the Eq 10 and our causal intervention method that Eq (10) ignores the prior distribution P(c)). is worth noting that although NeurJudge ignores P(c;), it performs slightly better than our model. A potential reason is that NeurJudge exploits the dependencies among LJP tasks well, while our model is trained the independent task.
D.2 VISUALIZATION
We provide several visualization cases in CAIL dataset as shown in Figure 7 Figure S and Figure 9 which are selected by Inter-RAT and INVRAT, respectively. Among them, annotated rationales are underlined. Inter-RAY and INVRAT rationales are highlighted in pink and green colors, respectively.
CAIL. Law Article
Label Law Article 233
The defendant was driving a tricycle without a driver's licence to deliver walnuts to certain place. When he drove into the door of the victim's house, he collapsed the gate pier of the victim's house, eausing the vietim to be injured by the collapsed gate pier, and he died after being rescued....
The defendant was driving a tricycle without driver's licence deliver walmuts certain place. When he drove into the door of the victim's house, he collapsed the gate pier of the vietim's house, causing the victim to be injured by the collapsed gate pier, and he died after being rescued....
Figure 7: Examples of selective rationalization on the law article prediction. Among them, INVRAT
fails to predict the law article and consider as Law Article 133 with capturing the tokens driving tricycle without a driver's ' licence. On the contrary, Inter-RAT predicts the law article correctly.
16

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Under review as a conference paper at ICLR 2023
CAIL- Charge
Label Manslaughter
The defendant and the victim were both students, after dormitory relocation, the new dormitory, the defendant and the victim for sleeping in the lower bunk bed dispute, the defendant picked up bottle and forcefully smashed the victim's 's head, resulting in the victim's head injury, then the defendant sent the vietim to hospital. The vietim died in hospital treatment failed. The forensic medical appraisal showed the injury to the victim's face was a pre-existing injury and the degree of injury was minor. The victim's death consistent with acute heart attack where trauma and emotional stress as precipitating factors...
The defendant and the victim were both students, after the dormitory relocation, in the new dormitory, the defendant and the victim for sleeping in the lower bunk bed dispute, the defendant picked up bottle and forcefully smashed the vietim's 's head, resulting in the victim's head injury, then the defendant sent the victim to hospital The vietim died in hospital treatment failed. The forensic medical appraisal showed the injury to the victim's face a pre-existing injury and the degree of injury was minor. The victim's death was consistent with an acute heart attack where trauma and emotional stress as precipitating factors...
Figure& 8: Examples of selective rationalizations on the charge prediction. Although both Inter-RAT and INVRAT predict the charge correctly, Inter-RAT can extract more comprehensive rationales (i.e., The victim's 's death was consistent with an acute heart attack), which support the victim's 's death was due to the negligence.
Label -A fixed-term imprisonment of two years The trial found that the defendant saw the victim, who was a waiter in the hotel, when he was preparing go duty in the lobby of the xx hotel, and he kicked the victim in the buttocks, and fight broke out between the two. In the course of the fight, the defendant pulled out the folding fruit knife he was carrying and stabbed the victim in the inner right thigh. After seeing that the victim's leg was bleeding profusely, the victim and his colleague finished sending the vietim to the hospital, where it was determined that the victim suffered minor injuries. The defendant then surrendered to the police and made truthful confession to the crime.....
CAIL Term of Penalty
The trial found that the defendant saw the victim, who was a waiter in the hotel, when was preparing to go duty in the lobby of the xx hotel, and he kicked the victim in the buttocks, and fight broke out between the two. In the course of the fight, the defendant pulled out the folding fruit knife he was earrying and stabbed the victim in the inner right thigh. After seeing that the victim's leg was bleoding profuscly, the victim and his colleague finished sending the victim to the hospital, where was determined that the victim suffered minor injuries. The defendant then surrendered to the police and made truthful confession to the crime.....
Figure 9: Examples of selective rationalization on the term of penalty prediction. Among them, both RA and INVRAT predict the term of penalty correctly, but Inter-RAT extracts more plausible rationales (i.e., sending the victim to the hospital which important for sentencing.).
17


Reviews:
[{"review_id": "qpDe55NtAG", "reviewer": "Reviewer_qL6H", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a new rationalization method, in which the standard conditional probabilities are replaced by interventional probabilities, which in turn are expressed by the back-door formula. The purpose, roughly, is to encourage generating rationales that have sufficient causal influences on the outcome rather than merely contain sufficient information about the outcome, so as to resist the temptation to select rationales based on spurious correlations due to confounding. Experiments on some benchmark datasets are presented to demonstrate the competitive performance of the proposed method. ", "summary_of_the_review": "This is a sensible and promising paper with interesting empirical results but some confusing theoretical details.", "strength_and_weaknesses": "Strengths:\n\n1. The task is an important one, and the proposal is sensible, at least in the abstract.\n2. The experimental results seem promising.\n\nWeaknesses:\n\nMy main reservation has to do with the theoretical or conceptual development of the proposed method. As I said, I appreciate the general goal, of distinguishing causal effect from spurious correlations and using the former to select rationales and make predictions. But the exposition of the details of the proposed method is rather hard to follow and some details are particularly puzzling. For example, equation (1) is already confusing; shouldn't R be integrated out? More importantly, I have two main concerns. First, I don't quite follow the explanation in the paper regarding the possible confounding of R and the *prediction* Y. As stressed more than once in the paper, the basic framework is such that in the prediction module, the prediction Y is made solely based on R. Despite the presence of the sparsity and continuity constraints, I fail to see how C can have a causal influence on R that bypasses Y. It is not entirely clear to me, but the proposed method seems to build a predictor not just on R but on a function of R and C (labeled as H). If so, then the proposed method deviates from the standard rationalization framework and the confounding by C is really self-imposed. On the other hand, I am also puzzled why it should be the predictor of the target variable (which is perhaps better denoted as Y_{\\hat}) rather than the target variable itself that is of interest here. Shouldn't we be more interested in which parts of the input text causally influence the true value of the target variable rather than the predicted value of the target variable? In fact, if we are talking about the target variable itself, then I have no trouble understanding the possibility of some confounding between R and Y.  \n\nSecond, as acknowledged in the paper, the confounding is only partially observed. Then why should we regard the back-door adjustment as appropriate for the intended purpose? A related worry is that the proposed method seems to assume a specific causal structure, which licenses back-door adjustment. As I recall, some motivating examples in this literature feature quite different causal structures. I wonder how the proposed method would perform in those cases.  ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "NwWCCuPTpUL", "reviewer": "Reviewer_78ZX", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors attempt to improve the accuracy of methods that explain the inferences of natural language processing (NLP) models (particularly neural network models). Specifically, the authors propose to: (1) learn a causal model; (2)use that causal model to identify which factors in the neural network model are causal; and (3) Use those factors only to explain the inference.", "summary_of_the_review": "The paper addresses an interesting and important problem (explanation). However, the idea of explaining NLP inferences seems inherently problematic, given the odd status of causation in natural language (see above). In addition, the experiments, although extensive, are unclear and may not even address the right question.", "strength_and_weaknesses": "– Figure 2 will be confusing to readers, largely because it lacks a caption. \n\n– Inferences of natural language processing models are an unlikely target for causal modeling. Much of the actual generative structure of the world that produces natural language is latent, thus the inferences NLP models are likely to be non-causal in nature. That doesn't make their inferences incorrect, but it will make them difficult to explain to human interpreters. Those human interpreters will likely be expecting a causal explanation that better corresponds to their knowledge of the world (which the NLP model does not have).\n\n– It is unclear how a structural causal model (SCM) is created. Specifically, it is not clear from the paper how to formulate variables, derive the structure of the SCM, or learn the conditional probability distributions of the SCM. If the SCMs discussed in section 3 are purely notional, then this should be clearly specified. Based on my best interpretation of Section 3, it appears that variables correspond to tokens, the structure of the SCM is pre-determined, and that the causal effects are estimated heuristically as given in equations 6 and 7. However, this is not nearly as clear as it should be from the text.\n\n– The nature of the experiments in Section 4 are also unclear. The goal of Inter-RAT appears to be \"better explanations\". However, the results reported in the tables of Section 4 are about precision, recall, and F1. This implies that Inter-RAT is producing categorical predictions which are either being compared to ground truth or to the predictions of a neural network. Neither of these assess the accuracy of the explanations, on how well Inter-RAT reproduces the predictions of the neural network or the actual class labels. Merely because the Inter-RAT model predicts well doesn't mean that its explanations are good ones.\n\n\\+ That said, it is interesting that the accuracy increases (substantially, in some cases). Inter-RAT does appear to be doing something interesting. What that is, though, is less clear.\n\n– The authors repeatedly use the term \"real causality\". The term \"causality\" or \"causal dependence\" alone is sufficient. Rather than \"false causality\", the authors appear to be distinguishing between mere association and causality. Those terms should be used in preference to \"real causality\".", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "5TiZ4Xcjih", "reviewer": "Reviewer_DUoA", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a method \"interventional rationalization (Inter-RAT)\" to discover the causal rationales. Inter-RAT tries to detect spurious correlations between the input and rationales, and between rationales and results, respectively, by analyzing the causalities among them and identifying the confounder in the causalities, and further remove these spurious correlations using a causal intervention method based on the backdoor adjustment. Experimental results on three real-world datasets are used to verify the effectiveness of Inter-RAT.", "summary_of_the_review": "Some major issues in the paper need to clarified and improved.", "strength_and_weaknesses": "- Strength\n    - The paper is trying to work on a practical and useful problem regarding spurious correlations between the input and rationales, and between rationales and results, respectively.\n- Weaknesses\n    - The writing and organization of the paper can be much improved. For example, the format of the citations in the main text is incorrect.\n    - Figure 1 seems not an appropriate example to illustrate spurious correlations\n        - Since the label is “Manslaughter”, if the generator fails to select the underlined text, the predictor can not give the right prediction. So, the optimization process will force the generator to select the underlined text, right?\n    - Error in the Equation (1) in the Problem Formation of rationalization\n        - It should be $P(Y|X) = \\sum_R P(Y|R)P(R|X)$, instead of $P(Y|X) = P(Y|R)P(R|X)$\n    - Chang et al. (2020) worked on the same problem of spurious correlations and used the same casual inference method closely related to this paper, but the difference between these two papers is not well illustrated with enough details. I suggest the authors to clarify the difference and contributions clearly.\n    - The readability can be improved as well. Although the paper exploits some existing methods of causal inference, such as Structural Causal Model (SCM), it should be more self-contained for better understanding.\n    - The experiments are pretty weak. I do not buy the results of the experiments.\n        - (1) Baselines. It is ok to choose RNP and INVRAT (2020) since they are most related. But some latest or STOA rationalization methods, such as SPECTRA (2020), A2R(2021), are missed in the experiments. To tackle the challenge of spurious correlations, there is also a new method CDA(2021). It is pretty strange that the results in Table 2 are from A2R but A2R is not compared in the experiments.\n            - [SPECTRA (2020)] SPECTRA: Sparse Structured Text Rationalization, EMNLP-2020\n            - [A2R(2021)] Understanding Interlocking Dynamics of Cooperative Rationalization, NeurIPS-2021\n            - [CDA(2021)] Making a (Counterfactual) Difference One Rationale at a Time, NeurIPS-2021\n        - (2) Results. The implementation details are quite vague. How the batch size and learning rate are chosen? Are they the best for all the baselines? The results are sensitive to the sparsity, but from Table 1, I noticed that the paper only reports the preset \\alpha in equation 4 but the real sparsity of selected rationales on the test set is not presented. My previous experiences showed that Equation 4 can only constrain the sparsity softly and the final sparsity may be quite different from \\alpha in various methods. The paper does not report \\lambda_1 and \\lambda_2 in equation 4 as well. For Table 2, the paper mentions that it adopts Bert to replace bi-GRU for both RNP and Inter-RAT. But I noticed that the results of Bert_RNP are the same as the one in A2R implemented by DeYoung et al. (2020), which does not replace GRU with Bert but uses Bert embedding as input to GRU. I suggest author to clarify and fix the issue.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "4905KAjOXEt", "reviewer": "Reviewer_vjRf", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper aims to design a rationalization method based on causal inference. To achieve this goal, the authors first analyze the potential confounder underlined the input and output. Such latent confounder may influence the relation between the input and the rational as well as the rational and the output. To alleviate the impact of the confounders, the authors design a method to achieve the real causal relation. In the experiments, the authors have conducted many experiments to demonstrate the effectiveness of the proposed models.\n", "summary_of_the_review": "See the above comments", "strength_and_weaknesses": "\nThe paper is well written, and it is very clear. The motivation is interesting.\nMy major concerns are as follows: the confounder is hard to estimate, using entire label set can be not enough. The authors should better detail why the label set can be used to represent all the latent confounders. In addition, if different labels are correlated, whether we can still have equation 5. It seems that, without the causal concepts, the models can still be easily obtained, why we need causal inference. Basically, if we know that the labels can influence X, R and Y, we can easily build a more advanced neural model without the causal inference notions. The identifiability of (5) can only be proved when C can fully represent the latent confounders.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject