FR: Folded Rationalization with a Unified Encoder
Authors: Wei Liu
Haozhao Wang
Jun Wang
Ruixuan Li
Chao Yue
YuanKai Zhang
Venue: NeurIPS 2022 Accept (2022)
Keywords: Interpretability
NLP
cooperative game

Abstract: Rationalization aims to strengthen the interpretability of NLP models by extracting a subset of human-intelligible pieces of their inputting texts. Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected pieces. However, such a two-phase model may incur the degeneration problem where the predictor overfits to the noise generated by a not yet well-trained generator and in turn, leads the generator to converge to a suboptimal model that tends to select senseless pieces. To tackle this challenge, we propose Folded Rationalization (FR) that folds the two phases of the rationale model into one from the perspective of text semantic extraction. The key idea of FR is to employ a unified encoder between the generator and predictor, based on which FR can facilitate a better predictor by access to valuable information blocked by the generator in the traditional two-phase model and thus bring a better generator. Empirically, we show that FR improves the F1 score by up to 10.3% as compared to state-of-the-art methods.

FR: Folded Rationalization with a Unified Encoder
Wei Liu' Haozhao Wang Jun Wang Ruixuan Li Chao Yuel Yuankai Zhang'
School of Computer Science and Technology, Huazhong University Science and Technology PiWudoo Tech
[idc_lw, hz_wang, rxli, yuechao, yuankai yuankai_zhang) @hust. edu. Rjsang@imudao. tech
Abstract
Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by predictor that makes predictions based on the selected pieces. However, such a two-phase model may incur the degeneration problem where the predictor overfits the noise generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces. To tackle this challenge, we propose Folded Rationalization (FR) that folds the two phases of the rationale model into one from the perspective of text semantic extraction. The key idea of FR is to employ unified encoder between the generator and predictor, based on which FR can facilitate a better predictor by access to valuable information blocked by the generator in the traditional two-phase model and thus bring better generator. Empirically, we show that FR improves the Fl score by up 10.3% compared state-of-the- art methods. Our codes are available at httt:://ghhubbcoo///ggceenngg
Introduction
There are growing concerns over the interpretability of NLP models, especially when language models are being rapidly applied on various critical fields (Lipton) 2016 Du 2019 Xiang
et al. 2019 Miller 2019; Sun et al. 2021). Rationalization, using cooperative game between generator and predictor in which the generator selects distinguishable and -ittannntellligbbee pieces of the inputting text (i.e., rationale) to the followed predictor that maximizes the predictive accuracy, has become one the mainstream approaches to improve the interpretability of NLP models. The standard rationalization method named RNP (Lei et al. 2016) organizes the generator and predictor with two-phase framework (see Figure 2(a) However, as illustrated in Table such two-phase model suffers from the degeneration problem where the predictor may overfit to meaningless but distinguishable rationales generated by the not yet well-trained generator (Yu et al. 2019). leading
the generator to converge to the sub-optimal model that tends to select these uninformative rationales.
Many approaches have been proposed to address the degeneration issue. The basic idea of these approaches is to regularize the predictor using supplementary modules that make use of the full text such that the predictor does not rely entirely on the rationale provided by the generator. For example, as shown in the Figure 2 3PLAYER (Yu et al. 2019) adopts an extra predictor to squeeze informative parts from the unselected text pieces into the rationale; DMR (Huang et al. 2021) additionally aligns with prediction distribution and feature distribution of of the full text; A2R Yu et al., 202T) combines binary selection with soft selection in which every token in the inputting text is partly contained.
"Corresponding authors. This paper a collaboration between Intelligent and Distributed Computing Lahoratory, Huazhong University of Science and Technology and Wuddoo Tech
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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Table 1: An example of RNP making the right prediction using the uninformative rationale. The underlined piece of the text is the numan--nnotaane rationale. Pieces of the text in red and blue represent the rationales from RNP and our method respectively. Initially, the generator may randomly select some uninformative rationales such as selecting "." for the negative text. The predictor of RNP overfits to these uninformative rationales and recognizes the category according to whether "-" is included in the rationale. Guided by such a spoiled predictor, the generator turn tends to select these uninformative rationales.
Label( Aroma): Negative
Input text: 12 oz bottle poured into a pint glass a pours a transparent, pale golden color. the head is pale white with no cream, one finger height, and abysmal retention looked away for a few seconds and the head was gone s stale cereal grains dominate.. hardly any other notes to speak of very mild in strength t- sharp corn/grainy notes throughout it entirety watery and has hops characters esters to be found, very simple not surprisingly drinkable. but there are certainly better choices, even in the cheap american adjunct beer category hell least drink original coors
highly carbonated and crisp on the front with smonth finish d yes, it
Rationale from RNP: ["-"]
Pred: Negative
Rationale from FR: ["stale cereal grains dominate. hardly any other notes to"]] Pred: Negative
Although these regularized methods can greatly al- leviate the degeneration problem, they still maintain the two fragmented phases in which the spoiled pre- dictor can only be partially calibrated by the extra modules. More specifically, we identify that the pre- dictor has notably higher learning speed than the generator under the two-phase framework, indicating that regularized predictor will not immediately lead to a well-trained generator and has to continuously learn from generated uninformative rationales until convergence. To show this, we consider the RNP method with different learning rates for the genera- tor and predictor. We defer the experimental details to Appendix BB2 and present the results of rationale quality (F1 score) in Figure [] As can be seen, the results usually get better when the learning rate of the predictor is smaller than the generator. In partic- ular, the result achieves the best when the learning rate of the predictor is approximately 1/5 that of the generator, where the learning speed of the predictor is exceptionally limited.
Learning Rate of Generator (10-)
1.00
62.0
60.6
59.1
52.6
48.1
0.75
72.1
67.6
58.5
58.1
50.5
0.50
72.9
64.8
55.7
54.5
50.5
0.25
55.4
46.0
41.5
49.0
47.3
0.10
46.9
39.0
43.1
41.7
34.5
0.10
0.25
0.50
0.75
1.00
Learning Rate of Predictor (10-3)
Figure 1: The rationale quality of RNP with different learning rates for the generator and the predictor.
Although balancing the learning speed between the generator and predictor can effectively improve the performance, tuning the learning rates involves careful coordination between the two phases and requires much human effort, especially when some existing methods designed with more modules. To tackle this challenge, we in this work propose a frustratingly simple but effective method, Folded Rationalization (FR), which folds the two phases of the current rationalization method into one using a unified encoding mechanism. Specifically, FR shares a unified text encoder between the generator and the predictor. The predictor is enforced with the same learning speed the generator and has significant chances of learning informative rationales from a well-trained generator. On the other hand, the generator can also get a higher learning speed with the auxiliary of the predictor. Additionally, as theoretically analyzed, the encoder of the generator can also be seen as special regularizer for the predictor, which helps it get a global view of all the possible rationale candidates from the full text and thus prohibits it from overfitting to the uninformative ones. Furthermore, we evaluate our approach on two widely used rationalization benchmarks, i.e., the Beer Reviews dataset (McAuley et al., 2012) and the Hotel Reviews dataset (Wang et al.] (0100). of which the empirical results show that FR outperforms all state-of-the-art methods in terms of the rationale quality. Our contributions are:
To the best of our knowledge, this paper is the first to solve the degeneration problem in rational- ization from the perspective of re-organizing the two-phase framework. We propose a simple but

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Genemaer
Generatee
Encodert
Generater
Encoder
Facoddrr
Encodeer?
Lineart
Lineur
Linear?
z
Teasher Predicour
Fnooder
Staden Prodetor
Encodae
Share Pare
Enooder
Predeav
Encodler
Encodar
Predistor
Prodictor
Encoders
Prodictor
Encoder
Encoder
Predictor
Tincze
Lineaf
Fealennalty
h
Lincest
Lineze
Linear?
Lincare
Linear
ya
V
OutPenalty
min.5
(a) RNP
(b) 3PLAYER
(c) DMR
(d) A2R
Figure 2: Standard method (a) RNP Lei et al. 2016) with other recent methods with additional
regularized modules. (b) 3PLAYER Yu et 2019). (c) DMR (Huang et al. 2021). (d) A2R (Yu
et al. 2021 X, Y, represent the input text, selected rationale, and predictive result, respectively.
effective method, namely, FR, that folds the two phases into one by sharing a unified encoder between the generator and the predictor.
The degeneration problem is mainly because the predictor can make correct predictions using uninformative rationales, leading to a sub-optimal generator. In this paper, we theoretically prove that the predictor of FR will not make correct predictions using these uninformative ones and thus mitigate the degeneration problem.
We conduct extensive experiments over various datasets. The performance superiority of FR is demonstrated as compared traditional rationalization methods. In particular, the Fl score of FR can be up to 10.3% higher than the state-of-the-ee method.
Related work
Optimization for neural rationales. Most current rationalization methods adopt cooperative framework between generator and predictor (Lei et al.) 2016)). which requires careful optimization to coordinate and is hard to train. Considering this challenge, scries of research efforts focus on refining the optimization process improve the rationalization. For instance, |Boo al. (2018) used Gumbel softmax to do the reparameterivation. [Bastings et (2019) replaced the Bernoulli sampling distributions with rectified Kumaraswamy distributions. Chang et al. (2019) introduced a adversarial game and produces both positive and negative rationales. Jain et al. (2020) disconnected the training regimes of the generator and predictor networks using saliency threshold. Chang al. (2020) tried to learn the invariance in rationalization. Jiang et al. (202T) used post-hoc local approach to generate rationales for well-trained models. These methods are orthogonal to our proposed method FR that shares an encoder between the generator and predictor.
Regularization for neural rationales. Another series of efforts seek to regularize the predictor using supplementary modules which have access to the information of the full text (Yu et al. 2019 Sha et al. 2021]; Huang al. 2021 Yu et al., 2021). Figure 2 shows comparison of some representative methods, where X, Z. represent the input text, the generated rationale, and the predictive output, respectively. Yu et al. (2019) took the unselected text Ze into consideration by employing a supplementary predictor Predictor as shown in Figure 2 The generator and the supplementary predictor play adversarial game. Predictor tries maximize the predictive accuracy while the Generator seeks to minimize the predictive accuracy of Predictor" such that all the informative pieces is squeezed into Z from Z [Huagg et al. (202I) tried to align the distributions of rationale with the full input text in both the output space and feature space, as shown in (ic) To make alignment in the output space, they introduce teacher predictor Predictor pre-trained with the full text and minimize the cross- cross-entropy between the distributions of Y and Y (OutPenalry). In feature space, they minimize the central moment discrepancy (FeaPenalty) (Zellinger et al., (017)) between the representation of X and Z. Yu et al. (021)) endowed the predictor with the information of full text by introducing soft rationale, which shown in Figure 2(d) Specifically, the supplementary predictor takes the masked text by soft attention Zwott as the input. To convey the formation of full text to the predictor, the model minimizes the JS- JS-divergence between the output of the original predictor and Y of the supplementary predictor. These methods are mostly related to work.
3

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However, different from these methods, we in this paper solve the degeneration problem from new perspective. We fold the two phases of the rationalization framework into one such that the predictor gets direct access to the original full text.
3 Problem definition
Notation In the following sections, we use P(.) and p(-) to represent distribution and probability, respectively. gen(.) and pred(.) represent the generator and predictor, respectively. 0g and Op represent the parameters of the generator and predictor, respectively. 'D represents the distribution of dataset. We consider the classification problem, where the input is text sequence X=[x11, 22, 22. , with zi being the i-th token and being the number of tokens. The label of X is a one-hot vector Y E {0, 13S, where is the number of categories.
Cooperative rationalization Cooperative rationalization framework consists of generator and predictor. The goal of the generator is to find the most informative pieces containing several tokens in the original input text X. For each sample (X, Y) D, the generator firstly outputs a sequence of binary mask M [m1,- 1 mm] E {0, 13'. Then, it forms the rationale Z by the element-wise product of X and M:
Z M X [m ,mrr].
(1)
In cooperative rationalization, the informativeness of the rationale Z provided by the generator is measured by the negative cross entropy - (Y, Yz), where Y, is the output of the predictor with the input being Z. Consequently, the generator and the predictor are usually optimized cooperatively:
min Og.0p (XY))
Σ
H(Y, p(expred(gen(())),
Regularizer of shortness and coherence Instead of using more complicated regularizers in the other models trying to mitigate degeneration Yuu et al.] 2019 Huang et al., 202T), our model only needs basic regularizer (Chang al.) 2019) tollows, which similar to RNP (Lei et al. 2016). to effectively control the sparsity and coherence of the selected rationales:
((M) >11 x11 ||M al + +22 Σ m me-1l,
1
where denotes the number of tokens in the input text. The first term encourages that the percentage of the tokens being selected as rationales is close to pre-defined level The second term encourages the rationales to be coherent.
4
Folded rationalization using unified encoder
In this section, we specify FR and analyze why it works both intuitively and formally.
4.1 Architecture of folded rationalization
Based on the framework of RNP, the architecture of FR to share the encoder between the generator and the predictor, as shown in Figure 3 It is worthwhile to note that we have no constraints on the types of the encoder and it can be any models such as RNN and Transformer (Vaswani et al. 2017). For the convenience of comparing with current methods in experiments, in this paper, we adopt the bidirectional gated recurrent units (GRU) (Cho et al. 2014) as the encoder which has been adopted by most previous works (Huang et al. 2021 Yu et al. (021). In FR, the full text X is first sent to the unified Encoder in the generator and it outputs a representation for each token in X. Then, the representation of each token is independently sent to Linear which is linear layer acting as the generator head. Linear outputs a value of probability following the Bernoulli distribution from which the mask of each token is independently sampled using softel-softmax (Jang al.) 2016). After that, the generator outputs Z using equation[] [aaiiio[[andd and further sends it to the unified encoder Encoder in the predictor. By pre-processing the output of Encoder with max-pooling, the predictor uses the linear layer Lineare to make the prediction. We adopt the similar objective function as RNP, i... equation and to optimize the generator and predictor cooperatively.

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Intuition for guaranteed predictive accuracy By taking the classification problem as example, here discuss why FR will not reduce the predictive accu- racy of the predictor. In principle, the generator and the predictor seek to extract the same semantic in- formation from the similar input text. Specifically, to select the most informative pieces from the orig- inal text, the generator has to extract the semantic information of the text which is usually specific and distinguishable from other texts. On the other hand, to make the correct prediction, the predictor needs to transform the selected pieces mostly relative to the input text into the semantic features which are distinguishable and separable. Therefore, a unified encoder will not degrade the performance of the predictor, but will encourage them benefit from each other.
Generator
Predictor
Z
1
Shared
Encoder
Encoder
Param.
Linear
LinearP
Figure 3:
The architecture of FR. X is the original full text. Z is the rationale and Y is the predictor's output.
Mutually reinforcing generator and predictor The main problem in RNP is that both the generator and the predictor only have access to partial information of the original data pairs, which requires careful coordination to obtain the missed information from each other. FR with unified encoder provides inherent benefits for the coordination and promotes the information sharing. To be more specific, the generator has access to the input text but true labels, while the predictor is the contrary. To achieve good results, they have to carefully make a coordination such that receive valuable information from one another. However, there are 2 candidate rationales for each X with length 7, which is exceptionally difficult for not well trained generator to select informative ones from such a large space. As a consequence, the predictor of RNP has high risks in overfitting the uninformative noise provided by the generator. There is a similar case for the generator as obtaining backward information from the predictor. FR addresses this issue by allowing the generator and predictor to directly access to the full information of the original data. Specifically, the predictor with the unified encoder has a global view of all the rationale candidates by direct access to X, which prevents itself from overfitting to uninformative ones. In turn, with the encoder of the predictor, the generator's s ability of summarizing and extracting information from the original text is greatly enhanced.
4.2 Theoretical analysis
In the cooperative rationalization, the generator uses the predictor to evaluate the informativeness of rationales. If the predictor can always classify uninformative rationales into true labels, then the generator may be fooled and converge to be sub-optimal. Here we will show that the predictor in FR regularized by the generator through the unified encoder and not stably classify uninformative rationales into true labels any more. In this way, only informative rationales can promise high predic- tive accuracy. To this end, once the equation i optimized maximize the final predictive accuracy, the selected rationales is guaranteed to be informative ones. To quantify the uninformativeness, we first give a definition of uninformative tokens:
Definition For X ~ D, X, is a subset of X, for a given token 1, we say it is uninformative for X if the following equation holds:
(Y = (),,,, s.t., VX, C
means that token is independent of Y given X.. This definition comes from how the Shapley value is calculated for no contribution, i.e., all marginal contributions are zero. In this situation, we can say is uninformative Y for X naturally. When equation 2 gets the optimal solution, we have pred(gen(X)) (X)) = X(YlX) and pred[gen([X, t))) p(Y X, t), where "[X, ff" denotes the result of adding token to any position of X. Then, we derive the following lemma.
Lemma When an uninformative token t added to the original text X. the predictive results will hardly change;
pred(gen(X)) = pen t()) + s.t., VX, C. CX, P( =
(5)
where is an error depending on the learning error of the neural network. When equation 2 gets the optimal solution, the absolute value of is O.
5

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Without losing generality, we consider the case when equation 2 Jaciives the optimal solution for sim-
plicity. For ease of exposition, we here denote gen (X) and gen( ([X, z)) as ( and [Z (X), (2))
respectively, where Z1 X)) represents the binary masks of tokens in X and (2((() represents the mask of the new token i. Using equation and
we have pred(Z(()) (X)) = pred((Z)()) Z2())). To
promise the same prediction, we get ((x) = Z1 (X), which indicates
gen(X) gen([X, t]), s.t., vX,, C X, P)
t),
(6)
where (en( denotes the first item of generator's output, c.g., Zi (x)). For convenience of analyzing the functionality of the unified encoder, we decompose the generator and the predictor as
gen(.) fg hg: pred(.) fp hip
(7)
where " stands for functional composition, i.e., (f o ()) = f(h(X)). and f represent the encoder and linear layer, respectively. Obviously, we have hg = hp for FR. We rewrite equation as fg hgg (X) fg hy ([), Since fy is linear function, and the mask is selected independently for each token in practice, we get h (X) = (tg((X,)) Now, we have the following lemma.
Lemma If equation 2 gets the optimal solution, adding an uninformative token to the text X does not change the representation of the original tokens in X output by the encoder of the generator, i.e. (gg()zz, = h ()).., Va, E EX;
(8)
where (X),, represents the loken x; 's representation with X being the inpul of the encoder lig
In the following, we consider hg as general one-way RNN network. Note that bidirectional RNN is almost the same. Besides, our analysis also holds for Transformer encoder which leave its proof in the Appendix A.2 For the i-th token in X, the representation can be expressed as:
(X)x)., )x, (8a); 8, 12 (c,; 81-1);
where e;, 8, are the word embedding and hidden state of the i-th token. (fgs t%g are two functions corresponding to the type of RNN units. When uninformative token t is added between x; and I;-1, we have
8t Vylet, 8i-1); 84 thy sc),
(10)
where sf is the hidden state of the original x, in this new text and we denote it by (g f)) (s)). From Lemma 2, we have hg (X); (x( ())=)- Using the first term of of the first
equation 9 we get
(s(()) hg xx; hy (xx, A)2 (8))
(11)
Then we easily get 8, = 84. Using the second term of equation 10 we have
1i (e, 8t) sf s; (e $;-1).
(12)
Then we have 8, = 8 very smoothly, which is specified in the following lemma.
Lemma If the i-th token in X is uninformative, the hidden state of it through the encoder of generator is equal to that of the token preceding it:
8; S;-1, s.t., VX, C X, P P(:).
(13)
Then, let us consider two different uninformative rationales, i.c., Zuninf and Z unin which contain only uninformative tokens and come from two different data pairs (X, Y), "X,Y").
Lemma 4 eqqatioon 13 holds, any two different uninformative rationales have the same represen- tation through the encoder of generator for each token, i.e., hgg (Zuninf)z. hg ( (aannn))y where hg (Zainn)) 2; denotes the representation for the i-th token in Zuninf-
The proof can be seen in Appendix A.I Now, we immediately get the following theorem.
Theorem FR gets the optimal solution of equation 2 i.e. P(Y]X) = P(pred(gen(X)) and
Z Zuninf: Z' J are two different rationales that contain only uninformative tokens and are selected from two different data pairs (X,Y), XXY)).X, (X', Y'), we have
(fp hg) (Zanin)) = (fy o hh (z)
(14)
Theorem 1 indicates that the predictor in FR will give the same output for any uninformative rationales. Hence, when we optimize the generator and the predictor by maximizing the final predictive accuracy using equation 2 the predictor only recognizes the informative rationales and ignores the uninformative ones. In this way, our FR greatly mitigates the degeneration problem.

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Table 2: Results on Beer Reviews and Hotel Reviews. Each aspect is trained independently.
(a) Beer Reviews
Appearance
Aroma
Palate
Methods
S
Acc
p
R
F1
Acc
P
R
F1
S
Acc
P
R
Fi
RNP
18.7
84.0
72.1)
12.7
72.3
15.1
85.2
59.0
51.2
58.1
13.4
90.0
63.1
68.2
65.5
71.1
70.7
58.9
11.9
53.2
50,9
52.0
A2R
18.4
83.9
72.7
72.3
72.5
15.4
86.3
63.6
62.9
63.2
12.4
81.2
57.4
57.3
57.4
FR(ours)
18.4
87.2
82.9
82.6
82.8
15.0
88.6
74.7
72.1
73.4
12.1
89.7
67.8
66.2
67.0
Hotel Reviews
Methods
Location
Service
Cleanliness p
S
Acc
P
R
11
S
Acc
P
R
F1
Acc
R
FT
RNP
8.8
97.3
46.2
48.2
47.1
11.0
97.5
34.2
32.9
33.5
10.5
96.0
29.1
34.6
31.6
DMR
10.7
47.5
60.1
53.1
11.6
43.0
43.6
43.3
31.4
36.4
33.7
A2R
8.5
87.5
43.1
43.2
43.1
11.4
96.5
37.3
37.2
37.2
94.5
33.2
33.3
33.3
FR(ours)
9.0
93.5
55,5
58.9
57.1
11.5
94.5
44.8
44.7
44.8
11.0
96.0
34,9
43.4
38.7
Experiments
5.1 Experimental setup
Datasets and Models Following (Huang al., 2021). we consider two widely used rationalization datasets. To the best of our knowledge, none of dataset contains personally identifiable information
or offensive content. The details of the two dataset can be found in Appendix B.1 1) Beer Reviews
(McAuley et al. 2012) is multi-aspect sentiment prediction dataset widely used rationalization
(Lei et al. 2016 Yu et al. 2019 Chang al. 2019 Huang et al. 2021 Yu al., 202I). There is
high correlation among the rating scores of different aspects the same review, making difficult to directly learn rationalization model from the original data. Following the previous work (Lei) cet al. 2016 Huang et al. 2021| Yu et al. 2021), we use the subsets decorrelated by Lei et al. (2016) and binarize the labels Bao et al. (2018) did. 2) Hotel Reviews (Wang et al., 2010 another mentimnn
sentiment classification dataset. The dataset contains reviews of hotels from three aspects including location, cleanliness, and service. Each review has rating
scale of 0-5 stars. We
binarize the labels as [ao et al.] (2018) did. On both two dataset, we adopt the GRU (Cho et al. 2014) the encoder which has been adopted most previous works (Huang et al.) [021]] Yu et al. 2021
Baselines and implementation details We compare FR to the cooperative rationalization framework RNP (Lei et al., (016) and two latest published models that achieve state-of-the-at results: DMR (Huang et al. (021)) and A2R (Yu et al. 202T), both of which have been specified section T For DMR, adopt its source code and adjust its sparsity constraint get sparsity similar to the annotated rationales. For A2R, we re-implement it to do token level selection as other models do. Since DMR and A2R have been validated outperform 3PLAYER (Huang et al., I Yu et al. 2021), we in this paper ignore its results for clarity of presentation. Following DMR and A2R, we the 100-dimension Glove (Pennington et al., 2014) the word embedding and set the hidden dimension of GRU to be 200. We use Adam (Kingma and Ba, 2014) as the optimizer. All the baselines are tuned lot of times find the best hyperparameters. All of the models implemented with PyTorch and trained on RTX3090 GPU.
Metrics All the methods get similar predictive accuracy. Following (Huang et al., 2021 Yu et al. 202T). we mainly focus on the quality of rationales, which is measured by the overlap between the model selected tokens and score, respectively. S indicates the average sparsity of the selected rationales, i.c., the percentage of selected tokens to the whole texts. Acc indicates the predictive accuracy the test set.
annotated tokens. P. R. F1 indicate the precision, recall, and Fl
5.2 Results
Comparison with baselines on two datasets Table 2(a) and 2(b) show the results of selected rationales on two datasets. All the methods choose similar percentage tokens that close to human-annotate sparsity by adjusting the sparsity regularization term in equation 3 Obviously, we gain significant improvements in all six aspects. particular, the Fl score of our method FR 82.8% in the Beer- Peer-Appearance dataset, which improves the offte-of-hh-aaa result by up 10.3%.

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Table 3: Examples of generated rationales.
rationales are underlined. Rationales from RNP and FR are highlighted in red and blue respectively.
RNP
FR
Aspect: Beer- Appearance
Aspect: Beer- Appearance
Label: Negative, Pred: Negative
Label: Negative, Pred: Negative
Text: appearance: light yellow to almost clear smell slight hops but barely smelled like beer taste liutle to none, like rice lager, zero taste mouthfeel wateryy and tasteless drinkability very easy goes down easier than water. good for drinking games.
Text: appearance light yellow to almost clear smell slight hops but barely smelled like beer taste liule to none, like rice lager, zero taste mouthfeel watery and tasteless drinkability very easy goes down easier than water, . good for drinking games.
Aspect: Beer-Aroma
Aspect: Beer-Aroma
Label: Positive, Pred: Positive
Label: Positive, Pred: Positive
Text: the appearance was nice. dark gold with not much of a head but nice lacing when it started to dissipate.. the smell was ever so hoppy with hint of the grapefruit flavor that contained within, the taste was interesting. up front tart grapefruit not sweet in the least more like grapefruit rind even slight hint of hops and seemingly no malt. the mouth feel was crisp. with some biting carbonation drinkability was easily above average due to the crispness and lack of sweetness. not the usual taste you expect when drinking fruit beer. in fact this is my favorite fruit beer ever.
Text: the appearance was nice. dark gold with not much of a head but nice lacing when it started to dissipate the smell was ever so hoppy with hint of the grapefruit flavor that contained within the taste was interesting up front tart grapefruit not sweet in the least more like grapefruit rind even slight hint of hops and seemingly no malt. the mouth feel was crisp. with some biting carbonation drinkability was easily above average due to the crispness and lack of sweetness. not the usual taste you expect when drinking fruit beer. in fact this is my favorite fruit beer ever.
Aspect: -lean-CCessssess
Aspect: Hotel-Cleaniness
Label: Negative, Pred: Negative
Label: Negative. Pred: Negative
Text: we stayed at the holiday inn new orleans french quarter in late april 2012. the rooms and hotel did not meet my expectations the property tired and worn out my room had trash behind the furniture . rips in the carpet, , wallpaper coming off the wall no toilet lid, dirty tub and sink and 110 volt out in functional, i noticed a piece of apple in the corner of the elevator the day checked in and it was still there days later when checked out this property is a cash cow and is always rented they really do n't have to improve it should have spent little extra money to stay the weston, sheraton or the hilton. . live and learn.
Text: we stayed at the holiday inn new orleans french quarter in late april 2012. the rooms and hotel did not meet my expectations . the property tired and worn out my room had trash behind the furniture : rips in the carpet, , wallpaper coming off the wall, no toilet lid, dirty tub and sink and 110 volt out in the bath that was apple in the corner of the elevator the day checked in and it was still there days later when checked out this property is a cash cow and is always rented they really do n't have to improve it should have spent a little extra money to stay at the weston, sheraton or the hilton. live and learn.
the bath that was
functional. i noticed a piece of
ie.. 72.5 of A2R. Table 3 shows some visualized examples of rationales from RNP and FR. More rationale examples of DMR and A2R are in Appendix B.4
Comparison with baselines in low sparsity To show the robustness of our FR, we also conduct an experiment where the sparsity of selected rationales is very low, which is the same as CAR (Chang al. 2019) and DMR. The results are shown in Table The results of RNP and CAR are obtained from (Chagg et al. 2019), and the results of DMR are obtained from (Huang et al.) 2021). For the aspects of Aroma and Palate, we still achieve great improvement over other methods. For Appearance, our performance is a little bit worse than DMR. The reason may be that when the sparsity is far from the ground truth, many informative tokens are lost and the predictor can not make the right prediction. While DMR uses an adversarial framework same as CAR in practice and the label is used as an additional input to the generator, so it may work better when the sparsity is extremely low.
Skewed predictor To show that our FR does not suffer from the degeneration problem, we conduct the same synthetic experiment that deliberately induces degeneration as Yu et al.) (202T) did. The details of the experimental setup can be found in Appendix B.3 The results are shown in Table The results of RNP and A2R are obtained from (Yu et al. 2021). For all the settings, we outperform both RNP and A2R. Especially, for Palate-skew20, RNP and A2R can not work at all while our FR is still effective. can be concluded that FR is much more robust than the other two methods this situation.
8

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Table 4: Results of methods with low sparsity Beer Reviews. The results of RNP and CAR
obtained from (Chang et al. 2219), and the results of DMR are from (Huang et al.) (021).
Methods
Appearance
Aroma
Plate
S
Acc
p
R
F1
S
Acc
P
F1
Acc
R
F1
RNP
11.9
72.0
46.1
56.2
10.7
70.5
48.3
57.3
10.0
42.8
47.5
CAR
11.9
76.2
49.3
59.9
10.3
50.3
33.3
40.1
10.2
56.6
46.2
50.9
DMR
11.7
83.6
52.8
64,7
11.7
63.1
47.6
54.3
10.7
55.8
48.1
51.7
(R(uu)s)
12.7
83.9
77.6
53.3
63.2
10.8
87.6
82.9
$7.9
68.2
10.0
84.5
69.3
55.8
61.8
Table 5: Results of skewed predictor that induces degeneration on Beer Reviews,
RNP
A2R
PR(ours)
Aspect
Setting
Acc
P
Acc
R
F1
Acc
P
skew 10
82.6
68.5
63.7
61.5
84.5
78.3
70.6
69.2
87.1
73.9
71.7
72.8
Aroma
skew15 15
80.4
54,5
51.6
49.3
81.8
58.1
53.3
51.7
86.7
71.3
68.0
69.6
skew20 20
76.8
10.8
14.1
11.0
80.0
51.7
47.9
46.3
85.5
72.3
69.0
70.6
skew 10
77.3
5.6
7.4
5.5
82.8
50.3
48.0
45.5
75.8
54.6
61.2
57.7
Palate
skew15
77.1
1.2
2.5
1.3
80.9
30.2
29.9
27.7
81.7
51.0
58.4
54.5
skew20
75.6
0.4
1.4
0.6
76.7
0.4
1.6
0.6
83.1
48.0
58.9
52.9
Skewed generator: To further show that method is not susceptible to degeneration, design new synthetic experiment with special initialization that induces degeneration from the perspective of skewed generator. We first pretrain the generator separately using the text classification label as the mask label of the first token. As a result, the predictor will be able to know the category of a rationale merely through whether the first token is selected as part of the rationale or not and may overfit to this positional bias. So before we cooperatively train the generator and predictor together, the generator is initialized with the intentionally misleading pretrained parameters and the predictor is initialized randomly.
We compare the performance of RNP and FR in the dataset Beer- Palate, because Palate is relatively hard to learn as compared to other aspects (Yu et al. 2021). Besides, the performance of RNP and FR is similar in this aspect when the generator is not deliberately skewed. All the hyperparameters we use here are the same as that of the best results Table ((a)e The results are shown in Table 6 The i in "skewk" denotes the threshold of the skew: pretrain the generator special classifier of the first token for a few epochs until its predictive accuracy is higher than k. Since the accuracy increases rapidly in the first a few epochs, obtaining a model that precisely achieves the pre-defined accuracy is almost impossible. Therefore, we use "Pre_acc" to denote the actual predictive accuracy of the generator-classfieer when the pre-training process stops. Higher "Pre_ace means easier to degenerate. We find, in this case, both methods get high predictive accuracy while RNP fails to find the tuman-annotated rationales.
Failure examples Table 7 shows some failure cases of our method. Typically, the failures can be summarized as follows. First, the model fails to grasp high-level language phenomena and world background knowledge. For example, in the case of Beer-Palate, the model fails to understand the analogy statement of "almost feels like drinking champagne" and makes the prediction negative. Second, the model fails to make logical reasoning. The ground truth of hotel cleanliness should be inferred from the word "comfortable". However, our model makes judgments by selecting the rationales such as "breakfast, staff was friendly.... stay", which have little relevance to the cleanliness but are positive. A similar failure also occurs in the case of Beer-Aroma, where the model selects texts with strong sentiment but uncorrelated with the predefined ground truth.
6 Conclusion, limitations, and future work
In this paper, we analyze the relationship between generator and predictor in terms of working mechanism and propose a simple but effective framework to solve the degeneration problem in the two-phase rationalization. The experimental results show that proposed methods significantly outperform state-of -hethhesrts in terms of the rationale quality.
One limitation may be that our theoretical analysis mainly focuses on the idealized models which have perfectly fitted the distribution of the dataset. Besides, although sharing the encoder between the generator and predictor is simple, it is limited to the basic RNP which has no extra modules. Existing

---

Table 6: Results of skewed generator that induces degeneration in the Palate aspect of Beer Reviews.
RNP
(R(us
Setting
Pre_acc
5
Acc
R
F1
Pre acc
S
Acc
P
FI
skew60.0
63.1
13.1
87.2
42.8
45.1
43,9
62.2
12.5
84.0
59.3
59.8
59.6
skew65.0
66.6
14.0
83.9
40.3
45.4
42.7
66.3
14.2
81.5
59.5
67.9
63.4
70.
71.3
14.7
84.1
10.0
11.7
10.8
70.8
14.1
88.3
54.7
62.1
58.1
skew75.0
75.5
14.7
87.6
8.1
9.6
8.8
75.6
13.1
84.8
49.7
52.2
51.0
Table 7: Some failure cases FR. The underlined piece of the text is the human-annotated rationale. Pieces of the text in blue represent the rationales from FR.
Beer-Palate
Label:positive, Prediction:negative Input text: pours slight tangerine orange and yellow the head nice and bubbly but fades very quickly with little lacing, smells like wheat european hops a little yeast there too there some fruit in there too hut you have to take good whiff to get it the taste is of wheat, bit of malt, and a little fruit flavour in there too almost feels like drinking champagne. medium mouthful otherwise easy to drink. but not somthinf 'd be trying every night
Motel-Cleanliness
Label: positive, Prediction: positive
Input text: my husband and just spent two nights at the grand hotel francais, and we could not have been happier with our choice in many ways, the hotel has exceeded our expectation the price was within our budget. breakfast was included, and the staff was friendly helpful and fluent in english. as other travelers have mentioned,
hotel close to nation metro station which makes easy to get around the room size was just enough fit two people. but we a comfortable stay throughout overall the hotel lives up to its high trip advisor rating. we would love to stay here again anytime.
Beer-Aroma
Label: positive, Prediction: positive
Input text: amber gold with solid two maybe even three finger head looks absolutely delicious i dare say it one
best looking beers 've had s- light citrus and hops not very strong aroma t-wow the hops, citrus and pine hlow out the taste huds, very tangy in taste, yct pertectly balanced, leaving crisp dry taste to the palate. m-light and crisp feel with nice tanginess thrown in the mix d- could drink this all of this brew. notes one of the best balanced and best tasting ipa 's 've had date. ipa fans you have try this one.
night, too bad i only have
works have proposed many extra modules to solve the degeneration problem, which demonstrate great effectiveness. Leveraging these extra modules can predictably achieve performance improvement. However, the relationship among multiple modules will become much more sophisticated, simply sharing the generator and predictor may not be the optimal. How to effectively incorporate existing optimized techniques is challenge for FR.
Besides, the sharing encoder of FR is similar to multi-task learning when training the generator and predictor are viewed two cooperative tasks. However, existing multi-task learning methods are usually designed for the case where there are multiple output objectives but only one single input sample. How to bridge FR and multi-task learning both intuitively and theoretically, and then improve the rationalization with techniques in multi-task learning, is still unknown. We leave them as future work.
7 Acknowledgements
This work supported by National Natural Science Foundation of China under grants U1836204, U1936108, 62206102, and Science and Technology Support Program of Hubei Province under grant 2022BAA046.
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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Section 6
(c) Did you discuss any potential negative societal impacts of your work? [Yes] See
Section 6
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full of assumptions of all theoretical results? [Yes] See Section 4.2
(b) Did you include complete proof' all theoretical results? [Yes] See Section 4.2 and Appendix A
3. If you experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as URL)? [Yes]
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5.1
(c) Did you report error bars (e-g., with respect the random seed after running experi- ments multiple times)? [No] The experiments of most methods for rationalization do not have this item (Le et al., 2016: Yu et al.) 2019 Chang et al.) 20199] Huang et al., 2021 because experiments with multiple aspects of multiple datasets under the same random seed are sufficient to verify the stability of the models. All of our experiments and re- re-implemented baselines with different settings and datasets are under one same random seed to make a fair comparison. We have also tried five different random seeds for Beer-Appearance and Beer-Aroma in Table 2(a) and the standard deviations are 1.198 and 0.657 respectively.
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Section 5.1
4. If you using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [Yes] See Appendix B.J
(c) Did you include any new assets either in the supplemental material or as a URL? [N/A]
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(d) Did you discuss whether and how consent was obtained from people whose data you' re using/curating? [Yes] See Appendix [B.1
(c) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [Yes] See Section 5.1
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, applicable? [N/A]
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A]
(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A]
13


Reviews:
[{"review_id": "K__AorBDpnr", "reviewer": "Reviewer_iQBE", "summary": "This paper investigates the rationalization for NLP models. The authors analyze the relationship between generator and predictor in terms of working mechanism and propose folded rationalization that folds two phases of the rationalization into one via a unified encoder. Results show that FR improves over the existing baselines on beer and hotel review datasets.", "questions": "N/A", "limitations": "Yes", "rating": 7, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Pros:\n-\tThe proposed approach is simple but effective for rationalization in NLP.\n-\tThe authors conduct in-depth analysis to show that the predictor in FR is regularized by the generator through the unified encoder.\n-\tExperiments show that the proposed approach outperforms baselines by a large margin.\n\n\nCons:\n-\tAll experiments are conducted on two review datasets. It would be better to show the effectiveness of FR on other tasks.\n", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "B2U1H-g3xT", "reviewer": "Reviewer_7cgR", "summary": "This paper proposes a new method for the task of rationalized classification, where a (latent, discrete) rationale is extracted and then used in a prediction module to render a classification decision. The core idea of this paper is to share the encoders for both the generator (= selector) and the predictor modules. This model is trained using a sparsity and coherence regularizer from prior work. The paper presents some theoretical results on why this rationalizer structure should be effective.  Results on two rationalized classification datasets, BeerAdvocate and Hotel Reviews, show that the proposed approach produces rationales that agree more closely with human-labeled gold standards.", "questions": "My main questions are around the theoretical questions above, specifically my objections about Definition 1 / Lemma 1 / Lemma 3. Please clarify these points.", "limitations": "The conclusion and limitations section is quite vague, with claims like \"Existing works have proposed many extra modules to solve the degeneration problem, which demonstrate great effectiveness.\" The limitations listed here are largely conceptual things that haven't been done yet, as opposed to the (in my opinion quite real) limitations of the present technique: what datasets it can apply to, the assumptions it makes about classification, the limited scope of models considered, etc.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "STRENGTHS\n\n- The problem of rationalizing predictions is an important one. Being able to do this without any supervision is a worthy goal.\n\n- The paper compares against results from several pieces of recent prior work and shows strong improvements over these on the automatic evaluation of rationale extraction.\n\n- The proposed method is simple and could be widely adopted for this task as a result.\n\nWEAKNESSES\n\n- I'm not convinced of the correctness of the theory in this work.\n\nLet's start with the definition of uninformativeness. This is defined in terms of the conditional probability of the label given a particular token.  However, I think Definition 1 makes a very specific assumption about the structure of language that really doesn't hold.  What about the word \"not\"? We can imagine that \"not\" is uninformative by the definition here; perhaps it occurs in equal measure with positive and negative labels in the dataset. But \"not good\" and \"not bad\" might be meaningful collocations that the encoder has to account for.\n\nI believe this case breaks Lemma 1: the behavior of the model is changed by inserting \"not\". Moreover, I don't see how Lemma 3 holds for cases like this with a well-trained encoder. (I don't see derivations of either lemma from first principles; these are simply asserted, and I disagree with them as they are asserted).\n\nThis state of affairs is worse for pre-trained Transformer models, where the holdover from the pre-training objective makes much of the reasoning about the objective for this particular problem moot anyway, as the encoder's behavior reflects the base language modeling objective as well.\n\nEven setting aside collocations, I am also skeptical of Lemma 3 based on the mathematics of the models themselves. I would believe there are some idealized GRUs that this can be true for, but has this been verified on GRUs for real problems? I think the burden of proof is on the authors to show that there isn't some small latent state update (e.g., counting \"uninformative\" words) going on at each timestep. Otherwise, if the forget gates are nonzero even for some positions in the latent state vector, this lemma won't hold.\n\nAs a result, I don't buy the theoretical results in this paper.\n\n- Setting aside the theory, the core contribution of this paper is pretty minor, largely an engineering one of sharing an encoder. And given that most neural modes are overparameterized, I'm not sure how much sharing an encoder actually constrains the representations that get learned even empirically. The model does seem to work better, possibly due to a multitasking effect, but again, I'm not convinced that it's for the reasons the paper states.\n\n- The models used in this paper are very weak. 200-dimensional GRUs on 100-dimensional GloVe embeddings are simply not representative of the state-of-the-art in 2022 -- RoBERTa/ELECTRA/DeBERTa/etc. encoders should be considered. I think the empirical results about multitask learning of the predictor and generator actually depend on decisions like this, and I could imagine the outcomes being different with these models.\n\n- The paper uses word overlap as an automatic metric but does not do any human evaluation of the produced rationales -- I could imagine that rationales from other methods may disagree with human ground truths but still illustrate the prediction in a meaningful way. I think human evaluation would strengthen the empirical claims.", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "473JdcfMAQ", "reviewer": "Reviewer_oaM2", "summary": "This paper addresses the degeneration problem occurring in the two-phase models for rationalization. The authors identified that the learning speeds of predictor and generator are out of sync. Thus the predictor overfits to the uninformative pieces selected by the not yet well-trained generator, leading to a sub-optimal model. The authors propose FR that uses a shared encoder to fold the two phases into one such that the learning speeds of two components can be aligned. The authors’ claims are supported empirically and theoretically. ", "questions": "Have you considered adding specialized layers in each component’s encoder such that the encoders are partly shared but not identical?\n\nIt may be worthy if you can present some failure cases where the model failed to select reasonable rationales. This can pave ways to gain better insights of the model and be helpful to this line of research.\n", "limitations": "Yes, the paper discusses the limitations of their work.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n(1) The observation and the idea presented in the paper is interesting. Folding two phases of training into one enforces that the learning processes of two components are compatible, addressing the degeneration problem.\n(2) The claims are well supported by both theoretical analysis and solid empirical results.\n(3) The paper is generally well written and easy to follow. The idea and motivation are presented clearly.\n(4) The topic discussed in the paper is valuable to the broader community, as it aims to improve the interpretability of NLP models.\n\nWeaknesses:\n\nThe paper did not thoroughly discuss the discrepancy when using a unified encoder. The functions of two components are related but not identical. While parts of the encoder can be shared indeed, I cannot see why the unified encoder can work well if there are no other specialized layers in each component’s encoder.", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept