Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
Authors: Linan Yue
Qi Liu
Yichao Du
Li Wang
Weibo Gao
Yanqing An
Venue: ICLR 2024 poster (2024)
Keywords: Selective Rationalization
Shortcut
Primary area: representation learning for computer vision, audio, language, and other modalities

Abstract: The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shortcuts. Then, by introducing the identified shortcuts, we propose two strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods to close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method.

Published as a conference paper at ICLR 2024
TOWARDS FAITHFUL EXPLANATIONS: BOOSTING RA- TIONALIZATION WITH SHORTCUTS DISCOVERY
Linan Yue', Qi Liu! Yichao Du', Li Wang", Weibo Gao', Yanqing An'
I: State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China
2: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center Hefei, China
3: ByteDancce
(inyue, duyichao, w1063, weibogao, anyg) iimail.uutc.ed... qiliuql @ustc.
censscc.euu.rr edu.
ABSTRACT
The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts data compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts- fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shorteuts. Then, by introducing the identified shortcuts, we propose strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method, Code released /ttpsu///ithu..... com htw.aaaa.ccoooylellaaaa/eeeeeee-
INTRODUCTION
Although deep neural networks (DNNs) in natural language understanding tasks have achieved compelling success, their predicted results are still unexplainable and unreliable, prompting significant research into how to provide explanations for DNNs. Among them, the selective rationalization (Lei) et al. 2016 Bastings al., 2019 Paranjape al., 2020: Li et al., 2022a) has received increasing attention, answering "What part of the input drives DNNs to yield prediction results?". Commonly, the rationalization framework consists of selector and a predictor. The goal of rationalization is to yield task results with the predictor, while employing the selector to identify a short and coherent part of original inputs (i.e., rationale), which can be sufficient to explain and support the prediction results.
Existing selective rationalization methods can be grouped into three types. The first type trains the selector and predictor in tandem (Lei et al. 2016 Bastings et al. 2019 Paranjape et al., (020)). Specifically, as shown in Figure [(a) it adopts the selector to extract text span from the input (i.e., rationale), and then yields the prediction results solely based on the selective text by the predictor. It is worth noting that the gold rationale is unavailable during the whole training process. Therefore, we refer to this type of method as "unsupervised rationalization" Although this approach achieves promising results, recent studies (Chang et al. 2020 Wu et al. 2022 have proved that the success this method is exploiting the shortcuts in data to make predictions. Typically, shortcuts have potentially strong correlations (aka., spurious correlations) with task labels, but would not be identified as rationales to the prediction task by human. For instance, in Figure T(a)) there is a movie review example whose label is "negative". Among them, the underlined tokens extracted by the unsupervised rationalization method are the shortcut tokens, where a poor quality movie is always associated with "received a lukewarm response". An unsupervised rationalization easy predict the movie as "negative" based on these shortcut tokens. However, for a human being, judging movie is not influenced by other movie reviews (i.e., even if movie has low rating on movie
"Corresponding author.

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Published as a conference paper at ICLR 2024
uncupervised intionlization
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Figure I: Schematic of rationalization methods presented in this paper. (a) is the process of unsuper- vised rationalization with the with a multi-task framework. Semi-rationalizatte can be considered the combination of (a) and (b). review sites, someone will still enjoy it). Therefore, although the model predicts the right outcome, it still fails to reveal true rationales for predicting labels but depending on the shortcuts. In general, while shortcuts are potentially effective in predicting task results, they are still damaging to compose rationales. Based on this conclusion, we can get important assumption:
pattern. (b) illustrates the supervised rationalization
Assumption A well-trained unsupervised rationalization model inevitably composes rationales with both the gold rationale and shortcuts tokens.
Then, as the second type is to include the gold rationales annotated by human during training YDeYoung et al. 2020 Li et al., [202aa] Chan et al. 2022), we denote it as "supervised rationalization" It models the rationalization with a multi-task learning, optimizing the joint likelihood of class labels and extractive rationales (Figure I(b)). Among them, the rationale prediction task can be considered as a token classification. Since this method exploits real rationales, the problem of adopting the shortcuts to predict task results can be mitigated. However, such extensive annotated rationales are infeasible to obtain for most tasks, rendering this method unavailable.
To combine the superiority of the above two types of methods. Pruthi et al. (2020); Bhat et al. (2021) propose a 'semi-supervised rationalization" method, consisting of two-phases training. They first train the rationalization task with few labeled rationales in a multi-task learning framework (the super- vised phase) like the second type method, where denote the used training dataset as Deup- Then, they train the remaining data Dan following the first type method (the unsupervised phase). Since this method still suffers from limited gold rationales and employing shorteuts to generate rationales, we argue this "semi-supervised pattern" can be further explored to improve the rationalization.
Along this research line, in this paper, we propose boosted method Shortcuts-fusee Selective Rationalization (SSR) which enhances the semi-supervised rationalization by exploring shortcuts. Different from the previous methods that are degraded by shortcuts, SSR explicitly exploits shortcuts in the data to yield more accurate task results and extract more plausible rationales. Specifically, in the cun--supervised setting, we first train SSR with Deup in the supervised phase. Among them, since there exist no labeled shortcuts, we design a shortcuts discovery approach to identify several potential shortcut tokens in Daup. In detail, we employ a trained unsupervised rationalization model to infer potential rationales in D sup. As discussed in Assumption i the rationales extracted by unsupervised rationalization inevitably contain several shortcut tokens. Then, by introducing the gold rationales, we can explicitly obtain the shortcut tokens. Next, we design two strategies to learn the extracted shortcut information and further transfer it into the unsupervised phase, which can mitigate the problem of adopting shortcuts to yield rationales. Besides, due to the limited rationales labels, we develop two data augmentations methods, including a random data augmentation and semantic augmentation, by replacing identified shortcut tokens in D D.up- To validate the effectiveness of our proposed SSR, we conduct extensive experiments on five datasets from the ERASER benchmark Young et al.) 2020). The experimental results empirically show that SSR consistently outperforms the competitive unsupervised and semi-supervised baselines on both the task prediction and rationale generation by a significant margin, and achieves comparable results to supervised baselines.
2 PROBLEM FORMULATION
Considering a text classification task, given the text input x = {01.02,.. } and the ground truth y, where 2; represents the i-th token, the goal is employing predictor to yield the prediction
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results y while learning mask variable m {m1. m2...... ,, by selector. Among them, my E {0, 1 indicates whether the i-th token a part of the rationale. Then, the selected rationale is defined
@ {m, 21, m2 22..... mn In). Take the case in Figure [ for example, the selective rationalization aims to yield an accurate prediction result (i.e., negative) and extract the rationale (the italic tokens) as the supporting evidence to explain this result.
PRELIMINARY OF SELECTIVE RATIONALIZATION
Unsupervised rationalization. In the unsupervised rationalization, since the gold rationales are unavailable, to achieve extracting rationales, this type of method trains the selector and predictor in tandem. Specifically, the selector first maps each token I; to its probability, pe (m, of being selected part of rationale, where pe (m, (2)) softmax s.. fe.. (24)). Among them, fe.. (-) represents an encoder (e.g. BERT (Devlin et al.)), encoding x; into a dddiimnnioonl vector, and W... R2xd Then, sample my E {0. from the po.Malar.) distribution and ensure this operation differentiable, [ei et al. (2016) introduce a Bernoulli distribution with REINFORCE (Williams) 1992). Since this method may be quite unstable (Paranjape al.. 2020), in this paper. we implement this sampling operation with a Gumbel-Softman reparameterization (Jang et al. 2017):
exp (log (po (x)) + 9:) /T)
(1)
Σ, exp (llog (m,))11))))))))) +.))/T)
where 9i = --logg log (- log (u:)) and T is a temperature hyperparameter. u; is sampled from a uniform distribution (O, 1). Details of Gumbel-Softmax are shown in Appendix C.I Naturally, the rationale
2 extracted by the selector is calculated as = m O x.
Next, the predictor qu (ylz) yields the prediction results solely based on the rationale 2, where qy (y]=) = softmax [ S Jp... (z)). Wherein fp...) (.) re-encodes into d-dimensional continuous Pun
hidden states, Wp... E R xd is the learned parameters, and N is the number of labels (e.g., N = in the binary classification). Finally, the prediction loss can be formulated as
Lan_task task
E
2:, 2.gr--Dur morppe (m)
[- log (y)mm @ x))
where Dun is a training set (gold rationales are unavailable). Besides, to impose the selected rationales are short and coherent, incorporate sparsity and continuity constraints into the rationalization:
1 m,
Cre
A1
Σ n
A2
m: m4-1l
(3)
sparsity
continuity
where a E [0, 1] is the predefined sparsity level (the higher the a, the lower the sparsity). Finally, the objective of the unsupervised rationalization is defined as Lun Lun + Emm-pp (m]z) [Cre].
Although this type of method can extract rationales without the labeled rationales supervision and achieve promising results (Bastings et al.) 2019 Sha et al. 2021; Yu et al.) [021]) Chang et al. (2020); Wu et al. (2022) have proved that it is prone to exploiting the shortcuts in data (c.g.. the statistics shorteuts) to yield prediction results and rationales. In other words, such shortcut-involved rationales fail to reveal the underlying relationship between inputs and rationales.
Supervised rationalization. Since both the ground truth and the human annotated rationales available in supervised rationalization, several researches (Li et al. 2022a Chan et al. (002)) introduce joint task classification and rationalization method. Specifically, in the supervised rationalization, we denote D sup as a joint task and rationale labeled training set, containing additional annotated rationales é. Then, similar the unsupervised rationalization, we generate the probability of selecting I; as the part of rationales by calculating pe (m, (z,) softmax Softmaa(W....fF. S (z+)). Next, given gold
rationales é, we can consider the rationalization task as a binary token classification, and calculate the corresponding loss with token-level binary cross-entropy (BCE) criterion:
Lselect
m, log po ( (x:)
(4)
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where m {m,, m2..................... ................................ .,, } is the gold mask corresponding 2. Next, for the task classification, different from the unsupervised rationalization employing extracted rationales as the input, the predictor in supervised rationalization yields results with g,s (,s(yaa)) at), and the prediction loss is calculated as Ssup task = Brx.y-D... [- log qp )), where qe (z) = softmax Poup Spoup (I)). Finally, the objective of supervised rationalization is Crup Laup_tast tast 1 [Lsctect)
Since the real rationale label is explicitly introduced into the supervised rationalization, the "shortcuts" problem posed by unsupervised methods can be eased. However, such extensive rationales annotated by human are the main bottleneck enabling the widespread application of these models.
Semi-supervised rationalization. For the semi-supervised rationalization, researchers (Paranjape et al. 20200 Pruthi et al.) 2220;; Bhat et al., 2021) consider low-resource setup where they have annotated rationales for part of the training data Deemi In other words, D Daemi consists of Dun and suup. where [Dunl >> [Daupl- Then, we use the following semi-supervised objective: Lsemi
Soup + Lun, where we first train the model on Duup and then on D un
Despite this method appearing to combine the advantages of the previous two methods, it is still prone to adopting shortcuts for prediction (Lun in Lsems). Besides, due to the gap in data size between the two datasets (i.e., Dun and sup)). the prediction performance of this semi-supervised approach is considerably degraded compared to the supervised rationalization (Bhat et al. 202T)
SHORTCUTS-FUSEI SELECTIVE RATIONALIZATION
In this section, we follow the above semi-supervised rationalization framework and further propose a Shortcuts-fusse Selective Rationalization (SSR) method by discovering shortcuts in data. We first identify shortcut tokens from the input by exploring gold rationales in Duup (section 4.1). Then, to mitigate the problem which exploits shortcuts for prediction, identified shortcuts (section 4.2) Finally, to bridge the data size gap between Dun and D rap we develop two data augmentation methods also depending on identified shorteuts (section
introduce two strategies by leveraging
4.1 SHORTCUTS DISCOVERY
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As described above, identifying and detect- ing shortcuts in the data is fundamental to our approach. However, since there exist no la- beled shortcuts, posing a challenge to discover shortcuts. Based on Assumption []] we can identify the potential shorteut token from ratio- nales extracted by a well-trained unsupervised rationalization model via introducing labeled rationale tokens.
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Figure 2: Process of the shortcut generator.
Definition (Potential Shortcut Token) We first assume the unsupervised rationalization model Mun is already trained. Then, given the annotated rationales å and rationales extracted by Mun we define FST(2;) as whether a token n, is considered to be potential shortcut token or not:
(ST(:;) [(x, E = Axa $ 2).
(5)
where A is the logical operation AND. PST( =1 denotes x, is defined as a potential shortcut token. Motivated by the above assumption and definition, our shortcuts discovery method is two-fold: (i) We train an unsupervised rationalization method Man with Dun, which sufficiently exploits shortcuts to make predictions.
(ii) We design a shorteut generator (Figure 2 to combine labeled rationales in Deup and Mun to identify shortcuts. Specifically, the shortcut generator first employs Mun to infer the potential rationales z in D sup. Next, we introduce the gold rationales 2 and compare them with the predicted rationales 2. If token 2; E and x; 2 (i.e., this token is incorrectly predicted as rationale tokens), we define as a potential shortcut token.
In the practical implementation, considering the coherence of rationales, identify subsequence with three or more consecutive potential shortcut tokens as the shortcut z,, For example, as shown in Figure Man has predicted the movie review as "negative" correctly and composed the predicted
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(a) the supervised plase
(b) the unsupervised plase
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Figure 3: Architecture of Sir consisting of the supervised and unsupervised phases. Among them, @ represents the frozen shortcut imitator, and white boxes in indicate the rationale tokens and the black are non-rationale ones.
rationales. By comparing with gold rationales, identify "received lukewarm response review sites" as shortcuts. It's worth noting that the shortcut discovery is only used in the training phase.
4.2 TWo STRATEGIES BY EXPLORING SHORTCUTS
As mentioned previously, we follow the semi-rationalization framework and train SSR on Dun and Duup in tandem. Since have identified the shortcuts in Dupp we design two strategies by exploring identified shortcuts to mitigate the impact of adopting shortcuts for prediction in Dun
4.2. SHARED PARAMETERS.
Before we introduce the two strategies, we present the shared parameters in both unsupervised and supervised rationalization. Here, we let "A ++ 8" represent the parameter A and B share parameters.
To allow the model to capture richer interactions between task prediction and rationales selection, we adopt feun (-) Spmn (-), and fs.up (+) + fp... (-) (Bhat et al. 2021 Liu et al. 2022).
To let the unsupervised and supervised rationalization learn better from each other, we let the selector. frnm (-) W Wpeupppaupp
W. Wonn fewn (-) feapp (-)
(-) in the predictor.
In summary, all encoders in both selector and predictor share parameters: fea. (-) +> fpum (-) + f (+)++ Spup (.). Linear parameters in selector are shared: W W... ++ W #aup" Linear parameters in predictor are shared: W Pan ++ W W Poug. For clarity, in both unsupervised and supervised rational- ization, we represent the f-(-) and fp(:) as the encoder in selector and predictor, W, and W, are the corresponding linear parameters. Table q Appendix C.2 lists all shared parameters.
4.2.2 INJECTING SHORTCUTS INTO PREDICTION.
In the semi rationalization, the unsupervised rationalization may still identify the shorteuts as rationales due to the unavoidable limitations (section B). To this end, we propose strategy, injecting shorteuts into the task prediction. Specifically:
In the supervised phase, besides the original loss Leup, we add a "uniform" constraint to ensure the predictor qu identifies the shortcuts features meaningless features:
Lunif Ee,-D... JKL (2, [
(6)
where KL denotes the Kullback-Leibler divergence, |N| is the total number of classes, and 2/(0, |N] denotes the uniform class distribution. When the predictor qu (y/z)) ()) adopts the input to yield task results, Eq (6) encourages the predictor to identify shortcut tokens 2g meaningless tokens, and dis- entangles shortcuts features from the input ones (i.c., making shortcuts and rationales de-correlated).
In the unsupervised phase, the learned features information can be transferred into the unsupervised rationalization method through the shared predictor (p....p... (.) <> W Ppnpp (.))
Finally, we denote SSR with this strategy as SSRunef and the objective of SSRuni/ can be defined as the sum of the losses: L Lesrence unis Lun + Loup + Auni Luniy. Detailed algorithms of SSR,nif shown in Appendix A.I
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Table I: Task Fl and Token Fl of selected rationales for the five datasets, Among them, the underlined scores are the state-of-the-ar performances of the supervised rationalization. The results in bold are the best scores in our SSR and its variants.
Methods
Movios
MuliRC
BoolQ
Evidance Inference
FEVER
Task
Token-Fi
Tank
Token-Fl
Tack
Teken-Fi
Taken-Fi
Tack
Token-Fi
Vanilla Ua~RAT
$7.0 D.1
28.
0.2
572
0.4
23.9 0.5
62.0
013
107
0.4
462 + 0.5
83= B:2
21.3 + 0.4
25.4 = 0.7
0.0
INVRAT
87.
26.6 06
612 1.0
30.4 1.5
64: + 255
208 11
47.0 +
0.0 1.5
816 + 18
4144 +
65.
0.8
MCD
80.1 1 0.5
29.1+ 0.5
628 0.
31.1 0.5
+
23.1 0.08
47.1 0.66
10.5+:0.7
84.4 +.066
44.6
18 (255 rationales)
854 0.8
282 00
664+000
14.0--03 6.5
63. 0.0
+03+000
46. 0.00
10.5 113++00 0.0
88.3+000
61. 019-002 0.0
WSILE.
56-1 0.0
323
011
65.0 1 0.8
35.8 1.8100
044
236 1.4
19.2 1.4
10. 0.3
81.1 4 1.0.3
11.9 - 0.5
St
6200
Vanilla Sup- Sup-RAT RAT
91.6 0.3
382 1 02
63.8 0.0
59.4 0
4:50::03
51. 4:3:002
$2.3 1 D.
16.5 3: 0.2
83.6 3: 1.4
68
0.9
Pipeline UNIREX
0.
623
1: 0.0
70.8
34.8 0.0
0.0 87.7
21 0.0
02.1
21.3 0.3
81.3
AT
0.6
40.2
0.6
SSRaui
94 0.3
33.2 0.4
628 0.3
56.2 0.2
GR.S + U1
47.6 0 0.5
#6.8 + 0.3
26.5 + 0.2
86.3 + 0.9
46.6 0.2
trandom DA
90.7 03
36.5 4 0.1
63.6 0.5
56.1
6134001 0.7
28.3 05
46l1 + 201
25.1 0.2
87 4: 0L3
47.6 = 0.5
suiited DA
045 03
35.1 01
65.3 DUG
40 =
604 01
203 00
47.6 + 11
2
B8L1 4 0.3
48.8 = 8.7
Wp
SSR +random DA
00:0 0.0
36. 02
64
57. 02
58. 0.5
438 03
90.4 03
313+00 04
43.0 0.5
46 43
10
48
02
03
46
0
emixed
M1 08
47 0.
0.
-hhree W. W.
$7.
319
25
40.
-Sarrdd W. and W.
= 03
304 011
62.4.00
54,0
575 2
03
429 01
45.8 2 04
25.0 02
818 3 0.7
39.6 0.7
4.2.3 VIRTUAL SHORTCUTS REPRESENTATIONS.
Intuitively, the supervised rationalization with gold rationales will perform better than the semi- supervised one. However, due to the limited resource, it is difficult for us to annotate the rationales with Dun and further obtain shorteut tokens improve the performance. To close the resource gap, we propose a virtual shortcuts representations strategy (SSRwirt) with transferred shortcuts knowledge from Dsup as guidance. As shown in Figure 3 SSR also contains two phases (the supervised and unsupervised phase). Specifically;
In the supervised phase, when training the supervised rationalization with Daup. we first adopt an external predictor 9, (y/ (2,) to predict task results based on the shortcuts 2s. and ensure the encoder Spn (.) in yn captures sufficient shortcuts representations fp,, (zx) E IRd by minimizing:
L. 1E,,-D,au Dup [- log on (y) [=)] = Ez,~Douy [- log softmax ( (n, (z.))
(7)
Then, learn additional shorteut imitator fa (3 that takes in Duup as the input (denoted by Tsap for clarity) to align and mimic (z,) by minimizing the squared euclidean distance of these two representations, where sa(-) and Sp, (-) share parameters:
Cuirt = E S [1)))(())) - fa fa(
(8)
In the unsupervised phase, during training with Dun, keep fa() frozen and employ it to generate virtual shortcuts representations fo (Fun) by taking r in Dun as the input. After that, to encourage the model to remove the effect of shortcuts task predictions, we first adopt fa (Ian) to match uniform distribution by calculating
Casss = EENDD... KLL (L, IN (y))
where 9, (3)((zun) = softmax (Wafa (xun)) Next, we set Wa and W share parameters (i.e., Wa +> Wr) to transfer the shorteut information into the predictor fy. and further achieve the de-correlation of shortcuts and rationales. Formally, the final objective of SSR,irt is Lesr = Lun + Loup + L. Cvirt + Aait Laifs. Detailed algorithms of SSR are shown in Appendix A.2
4.3 DATA AUGMENTATION
In this section, to close the quantitative gap between Dun and Doups we propose two data augmenta- tion (DA) methods by utilizing identified shortcuts in Daup-
Random Data Augmentation. As we have identified the potential shortcuts 2, in Daup, we can replace these shortcuts tokens with other tokens which are sampled randomly from the datastore Drandom- Among them, the database Drandom contains all tokens of D Dun and D Dsup (i.c., D Drandom (13, Vaz& DDn V xzj E D D.up)).

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Table 2: Task Fl and Token Fl of selected rationales for the five datasets with random DA.
Methods
Movies
MultRCC
BoolQ
Evidence Inference
random DA
Task
Token-Fl
Task
Token-Fl
Task
Token-Fl
Task
Token-Fl
Vanilla Un-RAT
88.0
0.4
28.4 0.3
58.4
0.2
24.7
0.3
62.1 0,3
23.5
0.2
47.0 0.4
10.4 0.3
Vanilla -AT
90.6
31.6
64.2
562
0.3
58.
45.0
26.0 0.3
WSEE
89.9
0.3
65.3=00
0.1
55.7
0.3
610=02
45.5
=0.0
18.7
0.5
Vanilla Sup-Rat RAT
92.0 0.4
39.1 0.3
61.4
0.6
60.6
62.
0.1404
51.9
0.7
52.8-02
18.5
0.3
AT-BMC
92.8 = 0.1
40.4
0.4:0...
66.6
0.6
61.8
62.0
52.6= 0.2
49.5 0.3
19.4
0.6
SSRani
90.7 0.3
3.1
63.6
0.5
56.1
6.
48.3
0.5
0.1
33.1
0.2
SSReiet
92.8 0.2
36.7
+
0.2
65.4
0.2
44.3
58.3
0.6
47.7
0.3
46.5
0.3
32.4 0.2
Semantic Data Augmentation. Besides the random augmentation, we design a retrieval-grounder semantic augmentation method by replacing shortcut tokens with several tokens semantically close to them through retrieval. Detailed retrieval algorithms about semantic DA are shown in Appendix AA.3 Besides, we also mix data augmented from random DA with data augmented from semantic DA to achieve mixed data augmentation.
EXPERIMENTS
5.1 DATASETS AND COMPARISON METHODS
Datasets. We evaluate SSR text classification tasks from the ERASER benchmark Young
et al. 2020), including Movies (Pang & Lee 2004) for sentiment analysis, MultiRC (Khashabi et al. 2018) for multiple-choice QA, BooiQ (Clark al. 2019) for reading comprehension, Evidence Inference (Lchman et al. 2019) for medical interventions, and FEVER (Thorne et al. 2018) for fact verification. Each dataset contains human annotated rationales and classification labels. In the semi-supervised (or unsupervised) setting, partially (or fully) labeled rationales are unavailable.
Comparison Methods. We compare SSR against three type methods as follows:
Unsupervised rationalization: Vanilla Un-RAT is the method presented in section which samples rationale tokens from Bernoulli distribution of each token with dumbel-softmail repa- rameterization. In practice, we employ Vanilla Un-RAT the unsupervised rationalization method Man in section 4. IB (Paranjape al. 2020) employs an Information Bottleneck (Alemi et al. 2017) principle to manage the trade-off between achieving accurate classification performance and yielding short rationales. INVRAT (Chang et al., 2020) learns invariant rationales by exploiting multiple environments to remove shortcuts in data. Inter-RAT (Yue al.) (022b) proposes a causal intervention method to remove spurious correlations in selective rational MCD (Liu et al. 2023) uncover the conditional independence relationship between the target label and non-causal and
causal features to compose rationales.
Supervised rationalization: Vanilla Sup-RAT is the method we describe in section which trains task classification and rationalization jointly. Pipeline (Lehman et al., 2019) is a pipeline model which trains the selector with gold rationales and predictor with class labels independently. UNIREX (Chan et al.) 2022)) proposes a unified supervised rationalization framework to compose faithful and plausible rationales. AT-BMC (Li et al. 2022a) is the state-of-the-art (SOTA) supervised rationalization approach, which is implemented with label embedding and mixed adversarial training.
Semi-supervised rationalization: Vanilla Semi-RAT is the method described in section which can be seen as the ablation of SSR (i.c., without exploiting shortcuts). IB (25 % rationales) (Paranjape) al., 2020) trains the selector with 25% annotated rationales through the BCE loss, and employs the rest data to train the unsupervised IB. WSEE (Pruthi et al.) (020) proposes a classify- siffit-tte--extaaae framework, conditioning rationales extraction on the predicted label. ST-RAT (Bhat et al., (021 presents self-training framework by exploiting the pseudo-labeled rationale examples.
5.2 EXPERIMENTAL SETUP
Following prior researches (Paranjape et al. 2020 Bhat et al., 2021). employ BERT (Devlin cet al.)) as the encoder in both the selector and predictor. For training, we adopt the Adam optimizer (Loshchilov & Hutter 2019) with an initial learning rate as 2e-05, then set the batch size as
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Smatgg Siik.ss
Sinteg? SSB
AT-BMC
AT BMC
1000
berenigg
Keiewige
Tokken
Moxics
BooQ
Infocee
Modia
MulRii
Beak)
Inkenan
Figure 4: Gold Rationale Efficiency.
Figure 5: SSRunfs with full annotations.
maximum sequence length as 512 and training epoch as 30. Besides, we set the predefined sparsity level as {0.1, 0.2, 0.2, 0.08) for Movies, MultiRC, BoolQ and Evidence Inference, respectively, which is slightly higher than the percentage of rationales in the input text. In the semi-supervised SSR and other semi-supervised rationalization methods with 25% labeled
setting, implement rationales. In SSR, we set Lunif: Leirt, and S as 0.1, respectively. For evaluation, we report weighted Fl scores for classification accuracy following (Paranjape al. 2020 Bhat al. 2021).
Then, to evaluate quality of rationales, we report token-level F1 scores. For a fair comparison, results of IB, Pipeline and ST-RAT in Table [ are directly taken (Paranjape al. 2020: Bhat et al. 2021 Besides, as WSEE reports Macro Fl and AT-BMC reports Micro F1 for task classification, we re-run their released codes (Pruthi al. 2020
Li al. 2022a) and present weighted Fl scores.
5.3 EXPERIMENTAL RESULTS
Overall Performance. We compare SSR with baselines across all the datasets, and experimental results are shown in Table I From the results, in general, we observe supervised rationalization methods perform the best, tollowed by semi-supervised rationalization, and unsupervised methods are the worst. Compared with the semi-supervised rationalization (e.g., ST-RAT),, both SSRunif and SSR achieve promising performance, indicating the effectiveness of exploiting shorteuts to compose rationales. Besides, after data augmentations, our approach achieves a similar performance the SOTA supervised rationalization method (i.e., AT-BMC). Considering that our method adopts only 25% annotated rationales, we argue that it is sufficient to demonstrate the strength of replacing the shortcut tokens for data augmentations. Finally, we compare SSRanij and SSR From the ob- servation, we conclude SSR performs better than SSR unif in most datasets, illustrating employing virtual shorteuts representations to rationalization may be more effective with few labeled rationales.
Ablation study. For SSR we first let linear parameters W, and W, not share parameters and the results (-shared W and W,) are shown in the Ta We can find the results degrade significantly after removing the shared linear parameters. Then, we remove the uniform constraint. Since we implement Vanilla Sup-RAT with the same set of shared parameters, we argue that Vanilla Sup-RAT can be seen as the ablative variant of which removes the uniform constraint. From Table [ in the paper, we find SSRunif outperforms Vanilla Sup-RAT. For SSR we set linear parameters Wa and W, not share parameters, Wa and Wp share parameters, respectively, and the results shown in the T We also find the results degrade significantly after removing the shared linear parameters or the shared W, and Wp. Besides, the results of without the shared W. and W, p perform worse than without shared linear parameters W, and Wi,, p, indicating the effectiveness of the shared W. and W,.. From the observations, we can conclude that the components of SSRunif and SSR are necessary.
Analysis on Data Augmentation. We develop two data augmentation methods, including random DA and semantic DA. We augment the data with 25% of the original dataset for both random DA and semantic DA. From Table [ we find SSR with semantic DA performs better than random DA, which validates that replacing shortcut tokens with semantically related tokens is more effective. Interestingly, SSR with mixed DA does not always perform better than semantic DA, and we argue potential reason is that since the augmented data only replace part of the input tokens and most tokens in the text remain unchanged, the augmented data have many tokens that are duplicated from the original data, and too much of such data does not be beneficial for model training and even degrade it. Besides, compare with baselines with the same data augmentation methods. Table 2 shows all baselines implemented with the random DA. With the same amount of data, our SSR achieves competitive results, especially in Token F1. More experimental results are shown in Appendix D.3
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Gold Rationale Efficiency. After the analysis on DA, we also find the performance of SSR with DA degrades a lot on the MultiRC dataset. Therefore, in this section, we investigate SSR performance with varying proportions of annotated rationales in the training set to see how much labeled data is beneficial for MultiRC. We make experiments on the MultiRC dataset and report corresponding results in Figure From the figure, proportions until 15%, and degrade when proportions exceed I 15%. Meanwhile, the performance between f +mixed DA with 15% gold rationales and AT-BMC not significant. The above observation illustrates that more labeled rationales may be not better and SSR can effectively compose rationales and yield results without extensive manual rationales by exploring shortcuts. More experiments can be found in Appendix D.2
observe both F1 and Token F1 of SSR increase with increasing
SSR with Full Annotations. We investigate the performance of SSR with full annotations (i.e., the proportions of annotated rationales achieve 100%.). Specifically, for strategy 2, since there exist no shortcuts need to be "virtual", SSR pirt is unavailable. For strategy 1, SSR with full annotations means it has incorporated all identified shortcuts in the supervised phase. In Figure 5 we compare SSR with full annotations with AT-BMC that also exploits all labeled rationales. From the figure, we find SiR outperforms AT-BMC, indicating introducing all shortcuts into SSR is effective.
Besides, we also implement AM-BMC on Movies by introducing shortcuts (i.c., replace the original objective of AT-BMC Lat-bme Lat-bme + Lunif with Eq (6)). The corresponding FI and Token F1 scores are 94.7 and 43.2, which still perform better than the original AT-BMC. Such observations boost supervised rationalization by introducing shortcuts explicitly.
strongly demonstrate that
Generalization Evaluation. Since SSR explicitly removes the effect of shortcuts on yielding task results and composing rationales, SSR can generalize better to ofitrr-diiti (OOD) datasets than unsupervised rationalization methods, where such unsupervised methods generalize poorly since the shortcuts are changed. To this end, we conduct an ex- periment to validate this opinion. Specifically, we introduce a new movie reviews dataset SST-2 (Socher al., 2013) which contains pithy expert movie reviews. As the original Movies dataset section 5.1 contains lay movie reviews
Table Generalization Evaluation on IMDB and SST-2.
Methods
IMDB
SST-2
Vanilla Un-RAT
85.3 + 0.2
45.3
8.1
+semantic DA
86.6
47.8
1 6.6
Vanilla Semi RAT
89.5 0.4
75.9
1 0.7
semantic DA
89.7
76.4
WSEE
90.5 -.55-00 0.3
77.1
0.6
+semantic DA
91.0 0.4
78.3 0.7
SSRunif
0.2
79.4
+semantic DA
90.7
82.4 1 0.8
SSR
89.9
70.9 = 0.4
+semantic DA
90.3 0.3
83.5
(Hendrycks et al. 2020). SST-2 can be considered as the
OOD dataset corresponding to Movies. Meanwhile, we make experiments on an identically dis- tributed dataset IMDB (Maas et al. 2011). Since there exist no labeled rationales in IMDB and SST-2, we investigate the model performance by calculating weighted F1 scores. In Table 3 we find all mod- els achieve promising results on IMDB. However, when evaluating on SST-2, F1 scores of SSR much higher than baselines, indicating the effectiveness of exploring shortcuts to predict task results.
Visualizations. We provide a qualitative analysis on rationales extracted by SSR in Appendix D.6 By showing several examples of rationales selected by Vanilla Un-RAT and our SSRwnif. we conclude that SSRwnif can avoid to extract shortcuts effectively. Besides, we make some subjective evaluations to evaluate extracted rationales in Appendix D.4] where we find SSRwni/ outperforms baselines in all subjective metrics (e.g. usefulness and completeness), illustrating the effectiveness of SSRunif-
CONCLUSIONS
In this paper, we proposed a Shortcuts-fused Selective Rationalization (SSR) method, improving rationalization by incorporating shortcuts explicitly. To be specific, we first developed a shortcut discovery approach to obtain several potential shortcut tokens. Then, we designed two strategies to augment the identified shortcuts into rationalization, mitigating the problem of employing shortcuts to compose rationales and yield classification results. Finally, we further utilized shortcuts for data augmentation by replacing shorteut tokens with random or semantic-related tokens. Experimental results on real-world datasets clearly demonstrated the effectiveness of our proposed method.
Acknowledgements. This research was supported by grants from the National Key Research and Development Program of China (Grant No. 2021 YFF0901003), the National Natural Science Foundation of China (Grants No. 62337001, 62337001), and the Fundamental Research Funds for the Central Universities.

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A
ALGORITHMS
A.I ALGORITHM OF SSRunif
Algorithm 1 S Injecting Shortcuts into Prediction.
In the supervised phase:
1. Calculate the original supervised rationalization loss Loup-
2. Ensure the predictor dę identifies the shortcuts 2, as meaningless features:
Lumif Ez.-DD.pp (14(0) (N (y)
In the unsupervised phase:
3. Calculate the original unsupervised rationalization loss Cun-
4. Set Ponn (-) and Ppunp (-) sharing parameters:
"p.. (-) <> p.up Pp..,.
5. Calculate the objective of SSRunif:
Lesrunis = Lun + Loup + Aunis Lunis.
A.2 ALGORITHM OF sirt
Algorithm 2 SSR Virtual Shortcuts Representations.
In the supervised phase:
1. Calculate the original supervised rationalization loss Leup-
2. Employ external predictor q, (v)zz)) to predict task results based on the shortcuts zg:
L. D...DD.up log (n [y)] Ez [- log (e,tmax(W,,PP....
3. Learn additional shorteut imitator Sa (1 that takes Taup in D Doup to learn the shortcut representation (Za):
Luirt = EanaiiteDDan [[,, -a Sa(ssap)))?
In the unsupervised phase:
4. Calculate the original unsupervised rationalization loss Lun-
5. Generate virtual shortcuts representations fa (zun) by taking zun. in Dan-
6. Employ (I(nn) match uniform distribution by calculating: Laifs RRDD.... KT. (2, [NT] yo (yl (y]=nn))]
7. Set Wa and W, share parameters: W. ++ W.
8. Calculate the objective of SSRvirt
Lesreit Lunn + Soup + L. + Avirtheirt + Laift.
A.3 ALGORITHM OF SEMANTIC DATA AUGMENTATION
Besides the random augmentation, we design cettrevallgroundee semantic augmentation method by replacing shortcut tokens with several tokens semantically close to them through retrieval. To achieve this goal, we first construct global datastore D Dioobal consisting of of key-value pairs offline, where the key is d-dimensional representation of the input computed by the encoder fp.. (.) in Man (section 4.1] and the value is the corresponding input. Then, for each input x in D Drup. we can search another input x" that is nearest to its semantics (except for itself) by retrieving this datastore. In this paper, we compute the 1.2 distance between the two input representations to indicate the semantic relevance, where the smaller the L2 distance, the closer input semantically to another.
Next, we build local datastore Diocal by employing fpo.. (-) to represent each token xf in x", where the value is zद and key is fr.. (xz)). Then, we adopt fr... (x;) retrieve the nearest token in x" to I; with the Diocal. In the retrieval process, we first calculate the L2 distance between the semantic representation of the retrieved word x; and each word of in the database. Next, choose the word with the closest L2 distance to the retrieved word x; as the semantically similar word. In the specific code implementation, we employ FAISS (Johnson al. 2019 to achieve this retrieval goal.
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Algorithm 3 Semantic Data Augmentation
Input: Supervised dataset D sup: and well-trained encoder fpm. (-) in unsupervised rationalization
model Mun-
Output: Several semantic related tokens.
Create global datastore D globblt
j=1 to [Psupl do
Sample x from D Paup-
Construct key-value pair: (key, value) = (Sp... (a),,
end for
Dotohol = (Vpr... (x), 2), Vx Dsup).
Search the nearest semantic x" to in Dotobol, x² # x.
Create local datastore Diocat
for i=1 to |x"| do
(key, value)= = (Span (x x())
end for
Diocal = [Spp.. (2)), of), Var E x²).
Search the nearest semantic token oy to x; in Dioett xy does belong to gold tokens in or wt.
Finally, we replace shortcuts tokens with retrieved tokens to achieve semantic augmentation. is worth noting that the goal of the semantic augmentation is to retrieve several tokens that are semantically similar to shortcut tokens. However, is still possible to retrieve gold tokens. To avoid this, in our implementation, when the retrieved token belongs gold tokens in r x", we will filter and search the next token. Detailed algorithms about semantic DA are present in Algorithm
B RELATED WORK
Selective Rationalization has achieved significantly progress recently. Existing approaches (Lei et al. 2016; [aao et al. [2018] Antognini et al. [021] |02I] Antognini & Faltings [021 Lakhotia et al. 202 Carton et al.) 2022 Liu et al.,) 2023) can be categorized into three types, including unsupervised, supervised and sumi-supervised rationalization. In unsupervised rationalization, Lei al.) (2016) first
proposed a classical framework consisting of selector and predictor. Following this framework, Bastings et al. (2019) studied HardKuma reparameterization to replace REINFORCE (Lei et al. 2016). Paranjape et al. (2020); Chen & Ji (2020) balanced the task accuracy and sparsity of rationales with an information bottleneck regular. Chang et al. (2020) discovered the causal and invariant rationales by creating different environments. Based the selector prrecctor-rreictor framework, several approaches expanded it by introducing an external guider. Among them, Huang et al. (202T); ) (Sha al. (222T) considered the original rationalization method as the student, and well-trained network as the teacher. Then, they adopted the teacher to guide the student to select rationales. Different from them, Yue et al. (2022a) developed "self-guided pattern, which explored non-rationale tokens to guide the rationale generation with a disentangled method.
In the supervised rationalization, De et al. (2000) studied ERASER benchmark which contains several datasets with both task labels and gold rationales. In ERASER, a pipeline approach (Lehman et al., 2019) was proposed to be a classical baseline in supervised rationalization. Chan et al. (2022) developed unified framework to train classification and rationalization jointly. [L et al. (2022a) proposed employ mixed adversarial training and boundary match constraint improve rationalization (the SOTA model in supervised rationalization).
Since annotating rationales is time-consuming and labor-intensive, several researches (Paranjape) et al. 2020 Pruthi et al.) 2020; Bhat et al. 2021) focused the semi-supervised rationalization. Among them, Paranjape et al. (020) first experimented with a semi-supervised setting. Bhat et al. (2021) developed multi-task teacher-student framework based on self- training pattern. They employed gold rationales to train a supervised rationalization method, and adopted it to label the unsupervised data to obtain pseudo-labeled examples to boost semi-supervised rationalization.
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C SETTING
C.I GUMBEL-SOFTMAX IN RATIONALIZATION.
Gumbel-Softmax is a commonly used technique for handling discrete data in generative models and optimization problems. It combines the Gumbel distribution and the Softmax function sample from a discrete probability distribution. The key idea is to introduce noise from the Gumbel distribution and then transform this noise into a sample from discrete distribution using the Softmax function.
The process of Gumbel-Softmax can be summarized as follows:
Sampling noise from the Gumbel distribution: First, noise vector g is sampled from the Gumbel(0, 1) distribution, where O is the location parameter and is the scale parameter. This noise vector introduces randomness into the sampling process.
Computing the Gumbel-Softmax sample: of the discrete probability distribution p. Then, the Softmax function is applied to obtain sample from the discrete distribution. Specifically, for the logarithmic value of the discrete probability distribution, the Gumbel-Softme sample is calculated as follows:
Next, the noise vector g is added to the logarithmic values
cxp((oo(p:)) + g:)/T)
Gumbel-Softmax// (2,) Σj= exp((log(p)) 9;)/T)
(10)
where is the temperature parameter that controls the smoothness of the sample. Higher temperature values result in smoother samples, while lower temperature values tend to produce more one-hot vectors that represent discrete values. By adjusting the temperature parameter the randomness and smoothness of the Gumbel-Softmax samples can be controlled. As 7 approaches 0, the sample tends be a one-hot vector, where only one element I and the are resembling maximum likelihood estimation. As T approaches positive infinity, the sample tends to approach a uniform distribution, where all elements have equal probabilities.
The benefit of Commbel-Softmax is that it provides a differentiable approximation for sampling discrete variables, making compatible with optimization algorithms such as gradient descent. This property makes it applicable in unsupervised rationalization to sample rationale tokens.
C.2 SHARED PARAMETERS
Table lists all shared parameters (each row's parameters are shared):
Table 4: Detailed shared parameters.
Supervised Phase
Unsupervised Phase
W Soop in the selector
s.. the selector
W Pzup in the predictor
W Pun in the predictor
fenm
f Pao
fp (-)
fxrup
fpan (-)
Spxup
fsnn
C.3 MISSED RESULTS
In this paper, we do not report ST-RAT results MultiRC data. Although ST-RAT has reported its code URL/httss:// aka. ms Rat ionnaleTT the paper, we still fail to find the corresponding codes. Therefore, it difficult for to reproduce the results of ST-RAT. Consequently, we directly use the results in the original ST-RAT paper. Since ST-RAT is not experimented on MultiRC, we do not report the result of ST-RAT on MultiRC.
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D MORE EXPERIMENTAL RESULTS
D.1 MORE BASELINES
In this section, add two unsupervised rationalization methods our baselines, including CAR (Chang et al. 2019) and 3Player Yu et al. 2019):
CAR (Chang et al.
2019) proposes a game theoretic approach to rationalization.
3Player (Yu et al. 2019) adopts an introspective model which predicts and incorporates the outcome
into the rationalization.
The overall experimental results are shown in Table
Table 5: Task F1 and Token F1 of selected rationales for the five datasets. Among them, the underlined scores are the state-of-the-an performances of the supervised rationalization. The results in bold are the best scores in our SSR and its variants.
Methods
Movies
MuliRC
Bool)
Evidence Inference
FEVER
Teken-FF
Vanilla Ln-RAT
$7.0
26
012
57
21.9 + 0.5
62.0 120+-00 01
197 + 0.4
46.2
8.- B22
71314 0.4
25.4 0.7
INVRAT
12
0.0
618
10
15
640
218
45.0
8
0.0 155
81. 188
MCD
16
0.3
0.5
623
04
1L1 0.6
65.
04
19. 08
411 0.
(15.1.0
86. 1 0.6
44.6
3Player
55.0 0.5
29
0.3
303
292 105
64.5 02
196 0.1
44.9 0.6
[191
85.0
1 0.4
40.4 0.1
TA (25% rationalss
83 6.0
183- 00
66.4 0.0
$4.0 0.
634 +.00
103 00
46. 0.0
10.5 + 0.0
88.8 + 0.6
61.9
0.0
WSSEE
961=00 -01
3222
4 011
03.0 à 08
55.8 =
39.9 04
43.6 204
49.2 0.
LAS 0.03
88.1 0L3
44.4 0.5
Vanilla Sup RAT
91.6 0.3
36.2
02
61.8 0.2
59.4 04
61.5 0.3
$1.3 0.
$2.3 1 0.5
16.5 1 0.2
83.6 1.A
68.9 0.9
Pipeline
560
0.0
623 1
18.4 1 0.0
70.8 0.0
36.8 0.0
87.7
0.0
$1.2 0.0
004
91.1
AT-BMC
SSR
943-03
1322+04 04
6288 128+033 0.3
56.2 0.2
60.5 + 04
47.6 176+00
46.8 0.3
26.8 + 0.2
86.8 + 0.9
46.6 0.2
trandom Da
002--1 03
34.5 0.1
63.6 0.5
56.1 03
28.1 1.1+05
46.1 + 0.1
33.1 + 0.2
87 0 0.3
43.0 0.5
emisedd DA
043
35.1+00 0.1
65.3 06
40
604 0.3
40 0.
47.6 1.
35.2 + 02
88.1 +.03
48.8 0:2
SSR
90:0
57.0
58
43.
50.4
47.0
+rancom
46
emiined
W.
41.
403
0.0
-lharod I.. and W.
88.3 0.3
30.4 0.1
02.4 0444000 09
$4.0
57. 5 03
42.9
0.1
45.8 =
04
25.0 +
0.2
81.8 8 0.7
39.6
0.7
Besides, since INVRAT AhRRA al. 2020) is implemented based on the available environments in data and the real environments are unavailable in ERASER, we employ an enviromment inference method Li al. (2022b); Yue et al.] (2024) to partition ERASER into different environments. Specifi- cally, we first divide each dataset in ERASER into two environments, and then reproduce INVRAT using pytorch.
D.2 GOLD RATIONALE EFFICIENCY
We additionally investigate SSR with data augmentation on the Evidence Inference dataset. From the experimental results, we observe that SSR with data augmentation performs best on the Evidence Inference dataset when the data augmentation percentage is 25%.
Table 6: Gold Rationale Efficiency on Evidence Inference.
SSRunit
Tisk
Tokes-Fi
Task
Token-Fi
Tisk
Token-Fi
Task
Token
Task
Token
Jask
Token-Fi
4.4% random DA
46.841.
26.800.2
47.3-0.5
28.54003
47,7604
29.0440.5
48.56066
50.3-02
40.2400
30.7407
460+0.1
33.1002
5% semanntiic DA
168.0.3
26...02
46..10.1
28.91004
47.110.3
29.31.0.5
47.60077
319_04
48.0.0.
32.1
48.7.02
33.5.0.44
From the above experiments, it is found that the optimal data augmentation percentage is different for different datasets. Since data augmentation method relies on human labeled rationales and most semi-supervised methods assume that the labeled rationales percentage are 25%, we also use 25% as percentage in our practical implementation.
D.3 FURTHER RESULTS OF DATA AUGMENTATION
In this section, to validate the effectiveness of the data augmentation methods, we present more experimental results about baselines implemented with the random and semantic DA in Table 7 and
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Table 8 From the results, observe that our SSR achieves competitive results, especially in Token Fl with the same amount of data.
Table 7: Task Fl and Token Fl of selected rationales for the five datasets with random DA.
Methods
Movics
MultiRC
BoolQ
Evidence Inference
+ random DA
Task
Token-Fl
Task
Token-F1
Task
Token-Fl
Task
Token-Fl
Vanilla Un-RAT
88.0 0.4
28.4
0.3
58.4
+ 0.2
24.7 0.3
02.1000 0.3
23.5
47.0
0.4
10.4 + 0.3
Vanilla Semi-RAT
90.6+0..
31,6
64.2
0.4
0.3
5.1
44.5
45.0
0.3
26.0
0.3
WSEE
89.9+00.4
33.4
+ 0.3
65.3
+ 0.1
55.7
0.3
6.2
45.5
+ 0.3
50.0
0.3
18.7 + 0.5
Vanilla Sup-RAT
0.4
39.1
0.6
60.
0.
51.
52.8
18.5
AT AI-BMC
9.1
40.4
0.3
66.6
0.6
6.5
6..0:0.11
52.6
49.5
19.4 0.6
SSRanis SSR,irt
0.3
0.1
63.6
56.1
48.3 0.7
46.0
0.1 0.2
92.8 0.2
36.7
65.4 0.2
58.3 4.
47.7
46.5
3.2
Table 8: Task Fl and Token Fl of selected rationales for the five datasets with semantic DA.
Methods
Movies
MultiRC
BoolQ
Evidence Inference
semantic DA
Task
Token-Fl
Task
Token-Fl
Task
Token-Fl
Task
Token-FI
Vanilla Un-RAT
88.3 + 0.2
28.7
+ 0.5
59.0 + 0.3
25.1 0.4
61.9 $1,9=00 0.6
23.7 + 0.4
47.3
0.3
11.7
+ 0.4
Vanilla Semii-AT
90.1
31.3
64.4
0.1
56.6
59.1
0.4
0.2
26.9
WSEE
0.7
33.1
0.5
64.9
= 0.3
1.3
0.
16.6000
49.7
0.3
20.9
1 0.4
Vanilla Sup-RAT RAT
0.3
39.9
0.3
65.1
0.4
0.7
62.2
0.3
52.3
0.5
53.1
0.1
19.9
+.3
AT-BMC
93.2
40.7
+ 0.5
66.0
0.6
60.9
0.4
62.2
0.3
52.0
50.0
22.3
SSRunij SSRwirt
9.2
35.6
0.2
64.7
= 0.7
0.
50.2 0.3
48.7
0.2
33,5
0.4
8.3
36.9
1 0.1
66.2
0.5
0.44
61.1 0.3
48.8 0.2
46.5
31.1 0.2
D.4 SUBJECTIVE EVALUATION
Following Sha et al. (2021 Yue et al. 2022a), we make human evaluation evaluate the rationales with three metrics: usefulness, completeness, and fluency. Specifically, we randomly select 100 samples from the Movie dataset by comparing SSRpirt with Inter_RAT RAT and WSEE. From the observation on Table 9
we can find SSRri outperforms Mer__RTT and WSEE in all metrics, illustrating the effectiveness of SSR
Table Human evaluation on Movies dataset.
Methods
Usefulness
Completeness
Fluency
Inter_RAT
3.69
3.53
3.88
WSEE
3.82
3.78
4.05
SSR
3,90
3.88
4.20
Considering the remarkable success of Large Language Models, we employ ChatGPT (OpenAl 2023) as alternative to humans for rationale evaluation. From the observation on Table 10 we find that our model achieves a similar partial order relationship between human evaluation and ChatGPT evaluation. This further illustrates the effectiveness of our method.
Table 10: ChatGPT evaluation on Movies dataset.
Methods
Usefulness
Completeness
Fluency
Inter_RAT
3.80
3.76
4.02
WSEE
3.86
3.88
3.96
SSR
4.06
3.94
4.13
This is our prompt ChatGPT:
Now, you are an annotator. First, I'll give you some original movie review text with labels to the text (i.e., positive or negative). After that, will give you some rationales (i.e., more
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consecutive sentences and tokens extracted from the text) generated by neural network, well as its predicted labels that judge whether the movie review is positive or negative. Finally, you are asked to evaluate these extracted rationales from three metrics including Usefulness, Completeness and Fluency. Among them, each metric is from (lowest) to (e.g. 3.4 and 4.0).
Detailed standards for annotators:
Usefulness:
Q: Do you think the selected rationales can be useful for explaining the predicted labels?
5: Exactly. Selected rationales are useful for me to get the correct label.
4: Highly useful. Although several tokens have relevance to correct label, most selected tokens are useful to explain the labels.
3: Half of them useful. About half of the tokens are useful for getting labels.
2: Almost useless. Almost all of tokens are useless.
I: No Use. The selected rationales are useless for identifying labels.
Completeness:
Q: Do you think the selected rationales are enough for explaining the predicted labels?
5: Exactly. Selected rationales are 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 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.
Fluency:
Q: Do you think the selected rationales are fluent?
5: Very fluent.
4: Highly fluent.
3: Partial fluent.
2: Very unfluent.
1: Nonsense.
D.5 OTHER METRICS
We use the comprehensiveness and sufficiency metrics from ERASER De Young et al. 2020). and
report the corresponding results in the table. As shown in Table 11 SS RSRin and S S.rrt still performs better than baselines.
Table 11: Comprehensiveness and sufficiency of selected rationales.
IMDB
SST-2
Methods
suff ())
com (*)
suff (1) com (t)
Vanilla Un-RAT
0.22
0.18
Vanilla Semi-RAT
0.15
0.40
0.24
0.25
WSEE
0.13
0.42
0.20
0.26
SSR SSRart
0.10
0.41
0.14
0.32
0.07
0.45
0.11
0.35
D.6 VISUALIZED SELECTIVE RATIONALES
In this section, we provide more visualization cases in Figure to show the performance of select rationales. From the observation, find that SSR select faithful rationales.
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Specifically, we show three examples of rationales selected by Vanilla Un-RAT and our SSRunis on both Movies and SST-2, where the label is Negative or Positive. Among them, the examples in Figure comes from SST-2. The underlined tokens represent the ground truth rationales, and the blue is the predicted rationales.
6(a) and 6(b) come from Movies and the example in Figure [(c)
Model
Visualized Exomple
Predicted Label
The film has received a lukewarm response on review sites, What I was in for was disappuintiig and overlong film which anything but the best picture 1995. What drags It down is sercenplay. It abounds with high production values...
Vanilla Un-RAT
Negotive
The film has repeived a lukewarm response review sites, What I was for g disappointing and overlong film was anything but the hest picture 1995, What drags down sereenplay.
SSRung
Negotive
abounds with high production values...
(a) Visualized selective rationales on Movies. The real label in this case is Negative.
Model
Visualized Example
Predicted Label
Mozart famous musician amadeus biographical film him. amadeus is a true work of art is one of those few movies of the 80 is that will he known for lts class its stvle and its
Vanilla Us-RAT
Positive
getelligence why this such good film...
Mozart is famous musician and amadeus is biographical film about him, amadeus is a true work of one of those few movies of the 80 "s that will be known for its class style and its
Posiiiv
intell sence, why this such a good film...
(b) Visualized selective rationales on Movies. The real label in this case Positive.
Model
Visualized Example
Predicted Label
Moonlight mile is replete with acclaimed actors and actresses and tackles subject that potentially moving. the movie tno prodictable and too self-corscious to reach a level high drama.
Vanilla Ua-RAT
Positive
Moonlight mile is replete with acclaimed actoes and actresses and tackles subject that 's potenrially moving, the movie is predictable and self-conscious
level of drama
Negatiiv
(c) Visualized selective rationales on SST-2, The real label in this case is Negative.
Figure 6: A visualized performance of extracted rationales with different methods.
In Figure 6(a) where the label is Negative, we find although both Vanilla Un-RAT and SSRanis pre- dict the label as Negative correctly, Vanilla Un-RAT still extracts shortcuts as rationales. Specifically, "received lukewarm response" the shorteuts, where human being judges a movie is not influenced by other reviews. SSRunif avoids these shortcuts, but Vanilla Un-RAT extracts these as rationales.
In Figure 6(b) where the label is Positive, we observe find although both Vanilla Un-RAT and SSR predict the label as Positive correctly, Vanilla Un-RAT still extracts shortcuts rationales. Specifically, "Mozart is a famous musician" is the shortcuts. Although Mozart was a great musician, it has no relevance to how good his biographical film is. Our SSRunij avoids these shortcuts, but Vanilla Un-RAT extracts these as rationales.
In Figure 6(C) where the label is Negative, we can find SSR predicts the label Negative correctly but Vanilla Un-RAT fails. Specifically, since Vanilla Un-RAT relies on shortcuts in the data for prediction, when Vanilla Un-RAT is generalized to the OOD dataset (i.e., SST-2), the task performance decreases due to the changed data distribution. And some wrong rationales are extracted. Our SSR,nif. on the other hand, extracts the rationales accurately and predicts the task results correctly, indicating the effectiveness of exploring shortcuts to predict task results.
E DISCUSSIONS OF SSR AND LLMS
Since SSR can mitigate the problem of utilizing shorteuts to compose rationales, our work can be applied to certain decision-making domains such as the judicial domain and the medical domain. In addition, although Large Language Models (LLMs) has achieved remarkable results recently, most LLMs (e.g. Chatgpt (Ouyang et al.) 2022 OpenAl 2023) and Claude (Anthropic) 2023)) need to be called as APIs. It cannot be used in privacy and security critical systems such as the judicial system. Our approach is much easier to deploy locally, ensuring privacy and improving explainability. Meanwhile, SSR is a model-agnostic agnostic approach, and we can replace BERT in SSR with opened LLMs (e.g., LLLMA (Touvron et al. 2023)) to achieve high performance.
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SSR offers a solution for offering explanations in text classification tasks. For LLMs, in the autore- gressive generation, we can view the prediction each token as a text classification task, with the number of categories matching the vocabulary size. By generating explanation for each token, can gradually explain the output of L.M.M Nevertheless, effectively applying SSR to explain LLMs remains formidable challenge. We are committed to further researching and exploring this area primary focus of our future work.
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Reviews:
[{"review_id": "rtG4PUrBnZ", "reviewer": "Reviewer_2gye", "summary": "The paper solves selective rationalization, an NLP problem where the aim is to find a piece of text from the input called \"rationale\" that directly justifies the label (selector problem), then use the rationale as input to do classification (predictor problem). Unlike \"rationales\", there are are pieces of text called \"shortcuts\" that can result in correct prediction but are not proper justifications of the label. The exact difference between rationales and shortcuts is not clearly defined in the paper, so it seems that the distinction is problem-specific and ultimately something that is left to the practitioner. Previous selective rationalization methods can be categorized into supervised, where the rationales are provided during training, and unsupervised, where they have to be inferred during training process. The paper proposes a semi-supervised approach (an extension of a couple of recent papers) where a selector model trained in the unsupervised way is applied to smaller supervised data to identify the shortcuts and retrain the selector model. In addition, two data augmentation methods were proposed to enrich the shortcut discovery. The proposed approach is evaluated in 4 datasets. The model outperforms previous SOTA unsupervised and semi-supervised methods on both rationale prediction and label prediction tasks. The performance is also\ncomparable to the supervised baselines", "questions": "* Elaborating on data augmentation might be beneficial. For instance, details about the number of augmented instances introduced and the ideal quantity would be insightful.\n* The term datastore can be confusing when discussing random data augmentation since this word is also used when describing the semantic key-value pair in semantic data augmentation.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The paper introduces innovative techniques to prevent the model from erroneously considering shortcuts as rationales during predictions. Since the objective is to answer the question “Which text span leads to the final conclusion”, this approach can be beneficial when there are misleading shortcuts in the text. Such predictions can help us analyze the reasoning ability of large language models (LLM).\n- The authors conducted comprehensive experiments on more than 10 variants of the proposed approach. Informative discussions are also presented. Tests are performed on four datasets from diverse domains and results are compared with SOTA baselines from all 3 groups of previous methods. Such rich experiments clearly show how each add-on piece affects the overall model performance. The limitations and potential reasons for certain outcomes are also deeply analyzed and discussed.\n- The data augmentation opens up a new approach to enriching labeled data in this area. Instead of using LLMs to augment new instances, which can be inefficient in both computing source and cost, the authors propose to use random/similar tokens to replace shortcuts. Such simple approaches, especially the random one, surprisingly yield promising results.", "weaknesses": "- The task of finding shortcuts remains ambiguous. It's unclear whether shortcuts always exist in the text. If they aren’t, what would the model predict? The usage of this system seems limited.\n- There's a need for a more robust analysis of prior methodologies, particularly the unsupervised ones. The author asserts that unsupervised methods frequently identify shortcuts as inefficient rationales and provides examples. Yet, the reader would be curious about how often that happens, and if the model already yields good results, why do we have to give up shortcuts? I suggest a stronger argument for why finding a good rationale is significant, such as it can be helpful for other reasoning tasks.\n- The manuscript's writing style can be perplexing in several sections, notably in Methodology (Section 3). Mathematical expressions should be consistent, straightforward, and clear, and should only be included when indispensable. For example, instead of writing an algorithm for the semantic data augmentation, the reader might be more interested in seeing how specifically you\ndo the semanticly similar word retrieval in Appendix A.\n- It is hard to understand why sharing selector parameters in both supervised/unsupervised phases is not the default setting. Since the claim is that this is a specific setting in the proposed approach, it would be important to learn why the previous methods are not doing so.", "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": "", "extra_scores": {}}, {"review_id": "LXKnS89ByG", "reviewer": "Reviewer_cE5E", "summary": "The paper \"Boosting selective rationalization with shortcuts discovery\" proposes an extension to selective text rationalisation methods by using so-called shortcuts in analysis and prediction for text. Here, rationalisation is an attempt to find fragments that influence the final classification The authors note that frequently, in unsupervised approaches, algorithms search for so-called shortcuts, which, while they may be strongly, but spuriously, correlated with the final classification, do not in any way explain the real reasons for a given classification. They therefore suggest a combination of supervised algorithms, where the true rationales (input elements influencing the classifications) are predefined by experts, and unsupervised methods, where the rationales are searched for. The proposal is thus to exclude those unsupervised rationalizations found that are not defined by experts. By excluding these unnecessary shortcuts, the proposed SSR algorithm achieves results that are similar to SOTA approaches, beating many other approaches.\n\nThe use of ChatGPT in appendix C.2 where it, essentially, selects the proposed approach over other methods, is nice and might be entertaining, but it does not introduce anything to the problem at hand. I would remove it, if I were you. But you, naturally, may do as you please.\n\nThe work on this subject is clearly very much needed these days. On the other hand, I think that this paper does not introduce new ideas. The use, or rather the exclusion of the “shortcuts” in a model, does not introduce enough new knowledge to push the model prediction understanding much.", "questions": "1. The definition and suggested algorithm for selection (as described above by rejecting the undefined) is too simplistic. The authors use only one example ('received a lukewarm approach' in the film review) throughout the article. Doesn't such a solution reduce the proposal to a supervised approach? Please use another example of a shortcut.\n2 The entire article is written in a way that is difficult to understand. There is no clearly stated hypothesis at the beginning of the text. The approach may be obvious to the author, but readers will not understand and will abandon reading before the end. Could you clearly state your hypothesis in the introduction?\n3. Is the whole of section 2 your proposition, or the definition of possible solutions used now? It is not clear.\n3. A number of cost functions are given that are to be utilized in different configurations.  Since the background models are quite complex (Transformer, both as encoder and encoder/predictor, as well as other generative models) these loss functions tend to be complex too. Some might be removed with more intuition on the more important in exchange.\n4. What actually is the “shortcut imitator” (pages 5 and 6 and later), what is it used for?\n5. Please explain what is the MixedDA augmentation in subsection 3.3.\n6. In the results tables, these with the best value are bold-faced as the best. But the mean differences may be as low as 0.1%, which statistically are not, in any way, significant. The authors should perform some statistical analysis and group the algorithms into groups of statistical equivalence (see e.g. Demsar, Statistical comparisons of classifiers over multiple data sets, JMLR 7, 2006; open software for that approach is available).\n\nLess essential:\n1. Difficulty in reading may come from poor English. I suggest the involvement of a native speaker.\n2. Instead of very many formulas, drawing diagrams of the methods should be shown, which would explain much more. Also, nothing is contributed by the detailed descriptions of all cost functions in the description of the general methods as well as the own proposal.\n3. in table 3 the result 90.7 for SSR_unif with DA is chosen as best, even though that of WSEE with DA of 91.0 mean seems better. That is perhaps a typing error, or is it?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The authors consider an important problem of rationalisation, understood as the selection of the parts of the classified input that have the greatest impact on the classification of the problem. The issue considered is of the natural language processing tasks. The solution is described in great detail in the form of a mathematical derivation, which is a strength, but in too much detail makes the article hard to read sometimes. It has the advantage of attempting to combine supervised and unsupervised approaches in order to exclude as found rationalisations those fragments that only have spurious correlations with the output, but do not explain anything.", "weaknesses": "1. The concept of a shortcut itself is very vague, and the definition and proposed selection algorithm (as described above by discarding the undefined) is too simplistic. The authors use one example of such a spurious correlation that does not describe much of the decision much, throughout all the article.\n2 The entire article is written in a way that is difficult to understand. Lots of equations, with several variables with stacked indices, reduces the readability and the clearness of what the authors want to achieve. \n3. There is no clearly stated hypothesis at the beginning of the text. The approach may be obvious to the author, but readers will not understand and will abandon reading before the end.\n4. The authors introduce a number of loss functions which can be used in different configurations, with no clear intuition which should be used and why.\n5. The authors introduce “data augmentation” DA approaches into their model. However, they seem to have forgotten the MixedDA solution, which is in the tables but not in the description (section 3.3). The models with and without augmentation are compared, and in some cases of algorithms or data one type of augmentation gives better results, but these results are not consecutive (see table 1). It seems to me that the augmentation ideas not really matter. The differences are small, in any case.\n6. The text introduces a great deal of patterning, both when describing existing methods and the author's own proposal. This does not make it easy to read, as many of them do not explain the next steps in any way, such as the definition of Gumbel-softmax on page 3.", "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": "", "extra_scores": {}}, {"review_id": "AVaLQ9f02P", "reviewer": "Reviewer_LMAN", "summary": "This paper studies selective rationalization. Existing methods suffer at some extent from spurious correlation (i.e., shortcuts). The authors propose shortcuts-fused selective rationalization (SSR) to mitigate spurious correlation. More specifically, they employ semi-supervised rationalization: given an annotated datasets of rationals and labels, they train SSR on it. Then, they train an unsupervised rationalization method on the same data and use the previous model to identify spurious tokens. This new knowledge is then transferred to the unsupervised setup. Since the main method relies on annotated data, the authors propose two data augmentation techniques to mitigate the low-amount of available data.\n\nThe method where one exploits a supervised rationalization model to identify spurious rational tokens from an unsupervised model is interesting, but relies on a \"large\" amount of available rationales. Overall, a supervised rationalization model has to be trained to improve an unsupervised one, which greatly limits the applicability of the method, even though a data augmentation approach is proposed. I would be curious whether transferring the knowledge from one task to another could be possible to some extent (not necessary from movie-dataset-1 to movie-dataset-2).\n\nThe experiment section is lacking unsupervised baselines and standard datasets used in selective rationalization [1-6, to cite a few but more are missing] (should also be included in the related work section). In terms of dataset: beers, hotels, amazon, and the other tasks of ERASER. Moreover, I would highly encourage the authors to conduct a human evaluation regarding the produces rationales. The relationship between the number of augmented data vs task/rational performance is currently unclear. I would appreciate having a graph showing how the performance evolve according the number of added data.\n\n1 Bao et al. 2018, Deriving machine attention from human rationales (EMNLP) 2 Chan et al. 2022, UNIREX: A Unified Learning Framework for Language Model Rationale Extraction (ICML) 3 Antognini et al. 2021, Multi-Dimensional Explanation of Target Variables from Documents (AAAI) 4 Chang et al. 2019, A Game Theoretic Approach to Class-wise Selective Rationalization (NeurIPS) 5 Antognini and Faltings 2021, Rationalization through Concepts (ACL) 6 Yu et al. 2019, Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (EMNLP)\n\nPOST-REBUTTAL:\nThank you for your additional detailed experiments. I am satisfied with your answer and will increase my score.", "questions": "- How would perform sup-rat with data augmentation?\n- Could you also report metrics regarding comprehensiveness and sufficiency to assess the improvement of the proposed approach to decrease spurious correlation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Interesting framework to leverage supervised and unsupervised rationalization models\n- The performance (although not the same configuration each time) is closed to supervised baselines", "weaknesses": "- the clarity of the paper could be improved, especially section 3\n- weak experiment section: more baselines, datasets, and analysis would be required\n- lack of human evaluation", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)