Text Style Transfer with Confounders
Authors: Tianxiao Shen
Regina Barzilay
Tommi S. Jaakkola
Venue: ICLR 2022 Submitted (2022)
Keywords: style transfer
confounder
invariance

Abstract: Existing methods for style transfer operate either with paired sentences or distributionally matched corpora which differ only in the desired style. In this paper, we relax this restriction and consider data sources with additional confounding differences, from which the desired style needs to be inferred. Specifically, we first learn an invariant style classifier that takes out nuisance variation, and then introduce an orthogonal classifier that highlights the confounding cues. The resulting pair of classifiers guide us to transfer text in the specified direction, creating sentences of the type not seen during training. Experiments show that using positive and negative review datasets from different categories, we can successfully transfer the sentiment without changing the category.

Under review as a conference paper at ICLR 2022
TEXT STYLE TRANSFER WITH CONFOUNDERS
Anonymous authors
Paper under double-blind review
ABSTRACT
Existing methods for style transfer operate either with paired sentences or distribu- tionally matched corpora which differ only in the desired style. In this paper, we relax this restriction and consider data sources with additional confounding differ- ences, from which the desired style needs to be inferred. Specifically, we first learn an invariant style classifier that takes out nuisance variation, and then introduce orthogonal classifier that highlights the confounding cues. The resulting pair classifiers guide us to transfer text in the specified direction, creating sentences of the type not seen during training. Experiments show that using positive and negative review datasets from different categories, we can successfully transfer the sentiment without changing the category."
INTRODUCTION
Despite advances in neural text generation (Brown et al., 2020), fine-grained control over generated outputs remains challenging. Indeed, the ability to easily transfer output style by altering attributes such as sentiment, formality, genre, and personal styles would make text generation tools more appealing (Hu et al., 2017; Shen et al., 2017: Rao & Tetreault, 2018; Xu et al., 2012).
Solving style transfer typically requires the method to be able disentangle what should be transferred from orthogonal aspects of sentences that ought to be kept intact. This disentanglement problem can be largely avoided in simple supervised scenarios with access to parallel sentences differing only in style (e.g., sentiment transfer with parallel negative and positive sentences, Figure 1.a). Recent approaches address more difficult version of the task by dispensing with parallel sentences (Shen et al., 2017). Nevertheless, they assume that the corpora differing in style remain distributionally matched in other ways (c.g., sentiment transfer with negative and positive reviews from the same category of products, Figure 1.b).
However, data available for training style transfer models rarely distributionally matched, and often involves changes other than style as well (e.g., sentiment transfer where negative and positive reviews come from different product categories, Figure 1.c). This means that the desired style difference no longer illustrated directly as the difference between the two corpora, making the task substantially more challenging. More subtly, the model is asked to generate sentences it has not seen during training, since the generated sentences in new style should not also reproduce those confounding differences present in the training data.
Solving style transfer with confounding cues requires to also learn what the desired style difference is. This can be facilitated by dividing the data into two groups of different styles, while the sets within each group illustrate variation we do not wish to alter. In the example in Figure 1.c, reviews are divided into two groups according to their sentiment. Within a group, each dataset corresponds to different product category which should be preserved. Besides sentiment transfer, this setup easily generalizes to other style transfer tasks, c.g., dialectal transfer. In this case, the groups will be divided according to the dialects, and sets within each group will represent different speakers whose personal style should be preserved.
Our model builds on invariant risk minimization (Arjovsky et al., 2019) to infer the style as an invariant distinction across different datasets from the two groups The resulting style classifier leaves complementary aspects of sentences to be controlled (preserved). We can illustrate aspects orthogonal to style with new sets of environments and learn another invariant classifier. Together,
Our code will be publicly released after the review process.

---

Under review as a conference paper at ICLR 2022
training sentence
senteecee
Parallel
negative
style
positive
Not Distributionally Matched
negative
posttive
and Kichen
Home
Kichen
style
Kitcheri
Non-Parailel, Distribationally Matched
Clathing, Showw Jawei
confounding difference
negative
positive
Electronid
Kitchen
style
Home Witchen
Gall Phoro and
Figure 1: Different learning scenarios for style transfer. a) With parallel examples, learning of the transfer mapping is supervised. b) With non-parallel, distributionally matched datasets, learning of the transfer mapping is unsupervised. Nevertheless, style given as the dataset difference, and generation takes place in-distribution. style needs to be inferred by excluding confounding differences. Not only is learning of the transfer mapping unsupervised, but generation is also out-of-distribution.
With non-paralle,, not distributionally matched datasets,
the two classifiers are used to guide sentence generation in a style transfer model along the desired direction. Combining with bock-translation (Sennrich et al., 2015; Zhu et al., 2017) and language model regularization (He et al., 2020) techniques, we can generate new types of sentences that have not been seen during training.
We empirically evaluate our proposed model in two sentiment transfer settings that involve con- founding factors. In the first setting. we augment original review data with special tokens, creating a spurious correlation with sentiment. In second setting, we consider sentiment transfer where negative and positive reviews come from non-overlapping product categories. In both cases, we assess the ability of the model to transfer the sentiment, while preserving other aspects special tokens in the first case, and product category in the second case. Our experiments demonstrate that our model successfully achieves both tasks, bringing significant gains over baselines that do not consider confounding factors. For instance, the the task of sentiment transfer from different product categories, the model yields 28.4% increase category preservation; according to human evaluation, its success rate is 6.2% higher than the best previous system.
2 RELATED WORK
The task of style transfer is related to paraphrasing, whose goal is to generate multiple linguistic realizations of the same underlying content (Androutsopoulos & Malakasiotis, 2010). However, style transfer adds additional complexity the requirement control for a specific realization characteristic during rewriting. While paraphrasing models have been used in the past for the task of style transfer, these models are impacted by arbitrary variations present in paraphrasing datasets (Preotiuc- Preotiuc-Pieero et al., 2016; Gröndahl & Asokan, 2019; Krishna et al., 2020). We instead take data-driven approach for discerning style from content, where the style is not strictly limited to paraphrasing and can involve more general attribute transfer such as sentiment and political slant transfer (Prabhumoye al., 2018).
Recent work in non-parallel style transfer proposes different techniques such as cross-alignment (Shen et al., 2017). delete and retrieval (Li al., 2018), or parallel latent sequences (He et al., 2020). The generation of these approaches all takes place "in-distribution", i.e., realizing sentences of the type already seen during training. The sentences we generate are by design not seen during training (Figure 1.c). Subramanian et al. (2018) proposed multi-attribute style transfer that controls both the sentiment and the category of a sentence. However, similar to other prior work, they assumed access to all sentiment-category combinations for training. In contrast, assume that negative and positive sentences come from non-overlapping categories.
Since we have to infer style from datasets with confounding cues, make of invariant learning (Peters et al., 2016; Arjovsky et al., 2019) to estimate style as an invariant direction. Moreover, our classifiers need to perform well across different combinations of source/target datasets and generalize
2

---

Under review as a conference paper at ICLR 2022
to generated text (out-of-distribuut samples) to properly guide the transfer model. Therefore, problem is also related to domain adaptation (Ben-David et al., 2010; Ganin & Lempitsky, 2015; Ganin al., 2016) and domain generalization (Blanchard et al., 2011; Muandet et al., 2013). Our main contribution is the utilization of invariance for guiding the generation process.
STYLE TRANSFER WITH CONFOUNDERS
We consider two groups of datasets: GA GA=(AI.....
{AI...., An where each A, (i 1,... , is dataset consisting of sentences with style 8A (e.g., negative sentiment), and GB = { (B.+1...... Ba+m where each B, (j n + 1, ,....7 + m) similarly conforms to style 8 (e-g. positive sentiment). In addition to style, a dataset has its own characteristics different from each other (e.g., category). Our goal is to transfer a sentence I of style A into style s (and vice versa) without changing its content or other characteristics.
One attempt is to aggregate collections of sentences in each group A U..... An, B U...U U Ba+m and perform style transfer between them. However, the specific characteristics of A, and Bj will become confounding factors and will be changed along with style. Instead, we notice that style is an invariant distinction between group GA and group GB. In other words, the style difference stable across different A; and By. Therefore, we can learn to isolate by taking out intra-group variations. Once we have access to style, we can learn to transfer sentences along this direction while preserving other sentence characteristics.
We take two-step procedure accomplish this task. We first learn pair of invariant classifiers to detect style and orthogonal characteristics. Then we use the classifiers to guide the learning of a style transfer model. Each part of the model is described in detail below.
3.1 INVARIANT CLASSIFERS
We make use of Invariant Risk Minimization (Arjovsky et al., 2019, IRM) to learn our style and orthogonal classifiers. IRM requires us to specify a set of environments & = (e...... egg. where each E E represents data {(ff.)f)) } collected under a certain environment. The different environments account for nuisance variation that the classifier should not pay attention to. The IRM objective is to learn a feature representation that enables a classifier to be simultaneously optimal for all environments. The rationale is that such representation likely involves primarily causal features that remain stable regardless of the nuisance variation. Therefore, the classifier can better generalize to new, unseen test environments compared the standard Empirical Risk Minimization (ERM) classifier trained on the pooled data from all environments.
We adopt the IRMv1 formulation, which does not explicitly separate the representation from the classifier but treats the classifier output itself as the representation. In this vein, the objective becomes to minimize the classifier loss across all the data while penalizing per-environment gradients with respect to any multiplier of the classifier output:
min 3EE
R (+) + V/ ( D)1
where (f) == Ex-X- YO) is the risk of ff under environment (L can be any loss function), and À is hyperparameter weighting the gradient penalty term. Gradients would be zero & is per-environment optimal. It remains to define suitable environments for the invariant classifiers to separate style and confounding factors in our task.
3.1.1 STYLE CLASSIFIER
To learn a classifier to distinguish between styles A and s without relying the specific characteristies of each dataset, we pair each A, and each B, to form environments:
ejjj {11 0) x E A, U {(x,y= = 1) x B,}
(2)
and learn IRM classifier C. X + y to predict the group label across environments {Cr.s Ses.hhssnn.ttsssstt (cf. Figure 2.a). Since all A, datasets share style S.. and all B, datasets share style sB, style feature representation that elicits an invariant classifier across different environments. Conversely, if the classifier uses any features specific to A; , will not be optimal in another environment consisting of different pair Av/ By,, thus violating the IRM constraint.
3

---

Under review as a conference paper at ICLR 2022
Style Classifier C
Orthogonal Classifier Co
negative
positive
Home and Kitzhen
0 =
Electronics
.y
16112)
Home and Kitchen
0
Col Phone and Accassories
.y
Clothing, Shows. Jawaly
0
Electronics
y
Invarient (1,) with respect to Clyla) (x)
Ciylx 2.25
Clothing Shoes. Jewelry
Gel Phone and 0
yy=1
Accessorias
Figure 2: Illustration of the learning of the invariant classifiers. a) The style classifier C2 is trained to be invariant across different pairs of A; and By datasets. b) The orthogonal classifier C. is trained to highlight changes in sentences other than the style identified by C..
3.1.2 ORTHOGONAL CLASSIFIER
In addition to requiring the transferred output to have a different style from the input, we also require to retain other characteristics the input. To detect style-independent characteristics, we construct environments based on predictions of the style classifier C.. These style-dependent environments serve to illustrate to the second invariant classifier C, what it should not rely on.
Let D {(x, y 0) æ E A} J {(x, 1) B} be the entire dataset, aggregate of all corpora. We create two environments
{1,,)) D C.(ya)) 0.5)
62 = (1,,)) E D C. x) S 0.5)
and learn IRM classifier C, X y across fei, e2) In this way, C. cannot depend the direction quantified by C. (i.e., the inferred style), and must find other orthogonal features to distinguish A from B (cf. Figure 2.b).
Note that we have successfully transferred a sentence in A to style sp. then C, should predict label 1 for the output sentence because its style has been changed, while C, should continue assign label 0 as the orthogonal characteristics ought to have remained intact.
3.2 STYLE TRANSFER MODEL
Based on the pair of invariant classifiers, we can now build style transfer model to transfer sentence to different style specified by C. while preserving other characteristics controlled by Co- Formally, we have dataset D ((x, })}, where denotes the group label of sentence æ. We learn style transfer model M X' x - X' that takes source sentence x and a target group y as input, and outputs a revised sentence that conforms to the style of group y. The model learned first in a reconstruction phase and then in transfer phase, discussed in turn.
When the input sentence x is from group M should behave as an autoencoder and reconstruct r. Therefore, have the reconstruction loss:
Cree (0 x. y) - log
(4)
where frr denotes the parameters of M be learned. We first use Lie to train M on for an epoch to provide the model with a good initialization to generate realistic sentences.
After initializing M as an autoencoder, we use the pair of invariant classifiers to guide it towards appropriate style transfer. Given an example (z, y) in D, we let T ~ Pas Par (-Ix, 1 y) be the transferred output sampled from the model. successfully transferred, should have style different from according to the style classifier C., and all the other characteristics should be the same as z according
A set of more segmented environments can be created based on the confidence of C.. However, we did not observe performance gains with environments.
4

---

Under review as a conference paper at ICLR 2022
to the orthogonal classifier Co. Namely, C₂ should treat ø coming from different group, and C. should treat λ as coming from the same group. These two constraints lead to losses:
Lc, (0M; Cc. (0 (0
log PC, (1 y/
(5)
log Pc, (y)
We further use a pre-trained language model L and introduce KL divergence term DKL (Par(- (-11, 3)IPP to regularize the transfer distribution. Estimating the KL divergence with one sample of PM (-1,, y), we have: Lim(OM;a,y,3)
log + Par ( 1- 1-y)
The first term ensures the fluency of the generated sentence The second term corresponds to the negative entropy of the transfer distribution - (- Maximizing this entropy term encourages exploration and helps to avoid bad local optima of constantly generating similar sentences (He et al., 2020).
Finally, we include the back-translation loss that the original sentence x should be generated if we take input and set the target label to be y (Sennrich et al., 2015; Zhu et al., 2017):
(BMiMMi 2) log Par (x)(2,
(8)
Taken together, our overall training objective is:
(( (c..))>D,ärppar (-1x,1 W + X2Cc, + A3CLM + X4CBT]
In practice we use word-level loss for LLM and LBT (i.e., divided by the sentence length) to make
them comparable in magnitude to Lc, and Lc.. We use Gumbel-Softmax (Jang et al., 2016) to approximate the discrete sampling process of f to compute gradients. At test time, we perform greedy decoding generate the transferred output.
EXPERIMENTS
To assess the ability of our model to perform style transfer in the presence of confounders, we consider two experimental settings. In the first experiment, we compose a synthetic task by modifying sentence punctuation to create a spurious correlation with sentiment (Choe et al., 2020). The model needs to transfer the sentiment while preserving the punctuation. In the second experiment, we consider sentiment transfer across different product categories. The goal is to transfer sentiment without changing the product category.
Baselines We consider two variants of our full model: the first is guided by the style classifier C, alone without the orthogonal classifier Co: the second is guided by ERM classifier CERM trained on D. performing direct transfer between and B without taking into account confounders. We also compare with He al. (2020), whose training objective has bock-translation loss and KL divergence term similar to ours. They do not use a classifier, but rely on two language models separately trained on A and B promote the transferred sentence have the target style. Finally, compare with paraphrasing based method of Krishna et al. (2020), which again transfers between the aggregated A and B without considering confounding factors.
Model Architecture We implement the classifiers C., C. using TexICNN (Kim, 2014) with sliding windows over 3-5 words. On the synthetic task, however, we found that because the dataset is simple to classify, the training loss of the CNN classifier is close to O. This removes any guidance from IRM constraints, degenerating the resulting classifier to standard ERM version. Therefore, to ensure that we estimate invariant classifiers, we use less powerful Bag-of-Words Words classifiers in the synthetic task. The employed language model L is I-layer LSTM trained on A U B. The style transfer model M consists of a l-layer LSTM encoder and 1-layer LSTM decoder augmented with attention mechanism (Bahdanau et al., 2014). To achieve style control through y, we learn two style embeddings for y = 0, y = respectively, and add the corresponding one to each word embedding before feeding to the decoder.
5

---

Under review as a conference paper at ICLR 2022
Table Classifier accuracy in the synthetic task when the spurious correlation is reversed.
Model
Sentiment ACC
Punctuation ACC
CERM
54.4
45.6
Cs
75.3
Co
89.9
Training Regime and Hyperparameters During the training of the invariant classifiers, we lin- early anneal the gradient penalty coefficient À from Amax over the first 100K steps, and continue training with À = Amax for another 200K steps. We try Amax in {1000, 3000, 5000) and choose the one with the best performance on the validation set. In the training of the style transfer model M, we linearly anneal the temperature of Sounbel-Softmax from 1 to 0.1 1 in the first 20K steps, and then keep it at 0.1 in the next 10K steps. We try the weights of the loss terms {A1, X2, A3, Aa} in {1, 2, 4), and select a model that strikes a good balance between output perplexity. BLEU with input, and accuracies according to the style and orthogonal classifiers. We note that different weight combinations will lead to different trade-offs (Pang, 2019), and we provide more detailed results in Appendix B.
4.1 SENTIMENT TRANSFER WITH DIFFERENT PUNCTUATION
We aim to emulate the presence of confounding factors by modifying sentences in standard sentiment transfer dataset with special symbols. For a sentence æ. remove its original punctuation, and then add either an exclamation mark "!" or a period "." at the end. Let p be the probability of adding "!", and p be the probability of adding "." p varies across different corpora. This setting enables us to measure model's ability to transfer sentence sentiment without changing its punctuation.
Dataset We adapt the sentiment transfer dataset introduced by Shen et al. (2017), which has 177K negative sentences and 267K positive sentences for training. 2K negative and positive sentences for validation, and 500 negative and positive sentences for testing. We obtain new training, validation and test sets using the following procedure: (I) negative sentences are modified with p = 0 to form A1; (2) positive sentences equally divided into two sets, and modified with p p 0.8 to form B2, By respectively. By construction, the punctuation strongly correlates with the sentiment. Therefore, a direct transfer between A and B₂ U Bg is likely change the punctuation well. By observing that sentiment is invariant while is variant in B2 and B3, we aim to transfer only the sentiment, not the punctuation.
Evaluation We assess transfer accuracy by comparing change in sentiment in the input and output sentences. To automate this evaluation, we utilize separate sentiment classifier trained on negative and positive sentences both modified with p = 0.5. In this way, its prediction will not be affected by punctuation. Note that this classifier is only used for evaluation, not for training the style transfer model. We also evaluate model's ability to keep punctuation intact during transfer by directly comparing input and output punctuation. To measure fluency, we report the perplexity of the output measured by an unbiased language model trained on sentences modified with p = 0.5. Finally, we compute the BLEU score of the output with respect to human reference (Li al., 2018).
Results We first report performance of the invariant classifiers C, and C2 in the setting reflecting their intended use in our style transfer model. We reverse the correlation between punctuation and sentiment labels to simulate style transferred sentences, and assess their ability to predict the desired aspects. Specifically, we test the accuracy of classifiers on positive sentences modified with 0 and negative sentences modified with p = 1, p- = 0.8. Table shows results of C. and C, compared with the standard CeRM classifier trained on D. As expected, CERM performs poorly, mixing sentiment and punctuation clucs. In contrast, the invariant classifier successfully separating the two aspects, achieving accuracy of 75.3% % on sentiment prediction and 89.9% on punctuation prediction.
Table 2 summarizes style transfer results. The paraphrasing- based method of Krishna et al. (2020) is not suitable for sentiment transfer that requires semantic changes, as shown by its low sentiment accuracy of 28.4%. Moreover, it has the lowest BLEU score because it introduces unnecessary rewriting learned from the paraphrasing dataset. Despite reaching high sentiment accuracy, the direct

---

Under review as a conference paper at ICLR 2022
Table 2: Automatic evaluation results of the synthetic sentiment transfer task. Accuracies less than 30 are marked in red.
Model
Sentiment ACC
Punctuation ACC
PPL
BLEU
Krishna al. (2020) He et al. (2020)
28.4
44.7
53.2
10.1
82.2
4.6
27.0
20.4
M w/ CeRM
65.1
4.5
40.4
28.8
M w/ C,
70.4
5.5
43.3
27.2
M w/ C2 C. (Ours)
84.3
97.7
48.1
24.3
Input Copy
2.4
100.0
34.0
32.8
Reference
76.8
100.0
42.3
100.0
Table 3: Example outputs of the synthetic sentiment transfer task.
Input
the sales people here are terrible.
Reference
the sales people are great
Krishna et al. He et al.
the people here are absolutely terrible. the sales people here are great
M w/ CERM w/ C.
the sales people here are amazing the sales people here are fantastic the sales people here are amazing.
Ours
Input
great food but horrible staff and very very rude workers. great food and excellent staff and very very nice workers.
Reference
Krishna et al.
great food, but very poor service.
et al.
great food excellent staff and very very friendly workers great food and great staff and very very nice workers
M w/ CERM
M w/ C.
great food but great staff and very very friendly workers great food and great staff and very very friendly workers.
Ours
Input
the food delicious and plentiful
Reference
the food was tough and dry
Krishna al.
the food delicious and plentiful the food is mediocre and plentiful
He et al.
M w/ CEAM
the food was mediocre and plentiful.
M w/ C.
the food is mediocre and plentiful.
Ours
the food was mediocre. too
Input
excellent combination of flavors, very unique the flavors nothing to write home about
Reference
Krishna ct al.
very unique combination of flavors, very unique horrible customer service.
He et al.
M w/ CERM
terrible combination of flavors, very disappointing.
w/ C,
temible combination flavors not unique
Ours
temible combination flavors not outstanding
7

---

Under review as a conference paper at ICLR 2022
Table 4: Automatic evaluation results of sentiment transfer from different categories.
Model
Sentiment ACC
Category ACC
PPL.
BLEU
Krishna et al. (2020)
22.6
57.4
35.8
19.2
He et al. (2020)
77.7
22.5
44.6
47.6
M w/ CERM
89.6
14.6
42.4
47.0
M w/ C₃
78.0
36.4
45.1
59.2
M w/ Cs, C. (Ours)
79.9
50.9
49.2
57.4
Input Copy
3.1
75.3
34.5
100.0
transfer methods of He et al. (2020) and M guided by CERM have low punctuation accuracy (4.6% and 4.5%, respectively). Our full model achieves both high sentiment accuracy (84.3%) and high punctuation accuracy (97.7%). The reliance the orthogonal classifier C. is proved critical its omission results in dramatic drop of punctuation accuracy (5.5%). Output examples in Table provide multiple illustrations of these phenomena: Krishna al. (2020) often paraphrases the input without changing the sentiment, while other models consistently change punctuation; only our full model successfully transfers the sentiment without changing the punctuation.
4.2 SENTIMENT TRANSFER WITH DIFFERENT CATEGORIES
Our second experiment focuses on sentiment transfer with product category confounding factor. Previous work took negative and positive reviews from the same category (Li et al., 2018). Our setting is more challenging where negative and positive reviews belong to distinct, non-overlapping categories. The model needs to infer that it is the sentiment to be transferred, not the category.
Dataset We use the 5-core Amazon review data (Ni et al., 2019), focusing four large product categories: Clothing Shoes and Jewelry (CSJ), Home and Kitchen (HK). Electronics (E), and Cell Phone and Accessories (CPA). We further filter reviews based their length, keeping reviews of length 5-20 words. Following standard practice, reviews with rating above three are considered positive, and those below three considered negative. Reviews with rating three discarded. Specifically, A₁ consists of negative reviews from the CSJ category, A2 negative reviews from HK, B3 positive reviews from E, and B4- - positive from CPA. We create dataset of 150K sentences for each category, in which 130K are used for training, 10K for validation and 10K for testing. Note that we do not use any review data of different sentiments from the same category.
Evaluations To assess sentiment transfer accuracy automatically, we train a classifier on complete data which includes negative and positive reviews from all four categories. It has test accuracy of 96.0%. Moreover, we train a 4-way category classifier using complete data to assess whether the output preserves input product category. has test accuracy" 71.0%. As in the previous task, we report the perplexity of the output measured by an unbiased language model trained on the complete data. Note that these omniscient models are used only for evaluation and not for training the style transfer model. Since we do not have human references for this task, compute the BLEU score with respect to source sentences as a rough indicator of content preservation (Zhao et al., 2018). In addition, conduct human evaluation by asking human judges to rate each output a Likert scale from to on three criteria: sentiment transfer, content preservation, and fluency. We consider generated output successful if it is rated 4 on all three criteria. We evaluated 200 randomly sampled examples (100 negative and 100 positive) and collected two annotations for each sentence.
Results Table 4 shows automatic evaluation results for different models. Our model yields signifi- cant performance gains over the baselines, achieving 57.3% absolute increase in sentiment transfer compared to Krishna et (2020), and 28. 28.4% increase in category preservation compared to He et al. (2020). The human evaluation results in Table 5 further verify the superiority of our model. Krishna et al. (2020) has the highest score in fluency as their model is fine-tuned from GPT2-Large. Our model achieves the highest sentiment transfer score and the highest success rate.
This accuracy can be attributed to the fact that some short sentences do not have clear indicators of their product category.
8

---

Under review as a conference paper at ICLR 2022
Table 5: Human evaluation results of sentiment transfer from different categories.
Model
Sentiment
Content
Fluency
Success
Krishna et al. (2020)
1.9
4.2
4.4
11.8%
He al. (2020)
3.3
3.6
4.0
24.3%
Ours
3.4
4.1
4.1
30.5%
Table 6: Example outputs of sentiment transfer from different categories.
Clothing, Shoes and Jewelry (negative + positive)
Input
this shirt the shirt is too tight this case great the protection seems great this shirt works just perfect. the sizing seems well
tight. the sizing seems off.
Krishna et al.
He et al.
Ours
Home Kitchen (negative + positive)
Input
the containers do not lock well and made of low quality materials.
Krishna et al.
the containers do not fit securely and are made from poor quality material
He et al.
the phones work well and has made of sound quality of low quality materials.
Ours
the containers does the job well and are made of high quality materials.
Electronics (positive - negative)
Input
exactly as advertised converted molex plug into sata the molex plug was convert to sata as advertised. way too big. leaves a inaccurate cut into a bath not as advertised converted a molex plug into a sata
Krishna et al.
Hc et al.
Ours
Cell Phones and Accessories (positive negative)
Input
very sturdy and helpful to use while driving.
Krishna et al.
drives very well and is very useful
He et al.
very stiff and weak use while washing.
Ours
very thin and uncomfortable to use while driving.
Example outputs in Table 6 show that our model successfully isolates sentiment from product category, preserving the latter during transfer. In contrast, He et al. (2020) mixes up categories by changing product specific nouns, such as rewriting "shirt" to "case", "containers" to "phones", and "driving" to "washing". Table 10 in the appendix provide more examples, some of which illustrate several failure modes of the model. For instance, when transferring sentiment of the sentence "very poor quality. crooked on one end .", the model only modifies the sentiment of the first clause, leaving the second clause intact. Another failure case is the use of inappropriate adjectives, such as rewriting "the drive works as designed" "the drive is too large".
CONCLUSION
In this paper, we consider a new, challenging version of the style transfer task, where the model has to exclude confounders and infer the desired transfer direction from data. We propose to leam pair of invariant classifiers to detect style and orthogonal characteristics, and then use them to guide a style transfer model. We empirically demonstrate significant performance gains over direct style transfer in two experimental settings. While this technology shows significant promise, still requires further development to be used in practice. However, style transfer algorithms continue advance, their ability to support rapid, at scale content modification will increase. This can potentially result in dissemination of fake news and other forms of misinformation.

---

Under review as a conference paper at ICLR 2022
REFERENCES
Ion Androutsopoulos and Prodromos Malakasiotis. A survey of paraphrasing and textual entailment methods. Journal of Artificial Intelligence Research, 38:135-187, 2010.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.0289) 2019.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprini S:Kiv::449.047.. 2014.
Shai Ben-David John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. A theory of learning from different domains. Machine learning, 79(1):151-175, 2010.
Gilles Blanchard, Gyemin Lee, and Clayton Scott. Generalizing from several related classification tasks to new unlabeled sample. Advances in neural information processing systems, 24:2178- 2186, 2011.
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et Language models are few-shot learners. arXiv preprint arXiv:2005. 14165, 2020.
Yo Joong Choe, Jiyeon Ham, and Kyubyong Park. An empirical study of invariant risk minimization. arXiv preprint arXiv:2004.05007, 2020.
Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pp. 1180-1189, PMLR, 2015.
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Nomain--dversaal training of neural networks. The journal of machine learning research, 17(1):2096-200 2030, 2016.
Tommi Gröndahl and N Asokan. Effective writing style imitation via combinatorial paraphrasing. arXiv preprint 1905. 13644, 2019.
Junxian He, Xinyi Wang, Graham Neubig, and Taylor Berg-Kirkpatrick, A probabilistic formulation of unsupervised text style transfer. arXiv preprint arXiv:2002.03912 2020.
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric Xing. Toward controlled generation of text. In International Conference on Machine Learning, pp. 1587-1596. PMLR, 2017.
Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterizatioon with gumbel-softmax. arXiv preprint arXiv: 1.0/ 144, 2016.
Yoon Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746-1751, Doha, Qatar, October 2014. Association for Computational Linguistics. doi: 10.3115/v1/D14-11 1181.
URL https::t////.. / www. aclwebl org/ / ana htwp/nnooooo//eee////
11111111///1111...11. 1181..
Kalpesh Krishna, John Wieting, and Mohit lyyer. Reformulating unsupervised style transfer as paraphrase generation. arXiv preprint arXiv:2010.05700, 2020.
Juncen Li, Robin Jia, He He, and Percy Liang. Delete, retrieve, generate: A simple approach to sentiment and style transfer. arXiv preprint arXiv: 1804. 2018.
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf. Domain generalization via invariant feature representation. In International Conference on Machine Learning. pp. 10-18. PMLR, 2013.
Jianmo Ni, Jiacheng Li, and Julian McAuley. Justifying recommendations using distantly- labeled reviews and fine ine-grained aspects. In Proceedings of the 2019 Conference Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLLP), pp. 188-197, 2019.
10

---

Under review as a conference paper at ICLR 2022
Richard Yuanzhe Pang. The daunting task of real-world textual style transfer auto-evaluation. arXiv preprint arXiv:1910.03747, 2019.
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen. Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society. Series B (Statistical Methodology), pp. 947-1012, 2016.
Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. Style transfer through back-translation. arXiv preprint arXiv: 090000000, 2018.
Daniel Preotiuc-Pietro Wei Xu, and Lyle Ungar. Discovering user attribute stylistic differences via paraphrasing. In Thirtieth AAAI Conference Artificial Intelligence, 2016.
Sudha Rao and Joel Tetreault. Dear sir madam, may i introduce the gyafc dataset: Corpus, benchmarks and metries for formality style transfer. arXiv preprint arXiv:1803.06533, 2018.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Improving neural machine translation models with monolingual data, arXiv preprint arXiv: 15100009, 2015.
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. Style transfer from non-parallel text by aloss-aligmment. arXiv preprint
2017.
Sandeep Subramanian, Guillaume Lample, Eric Michael Smith, Ludovic Denoyer, Marc' Aurelio Ran- zato, and Y-Lan Boureau. Multiplc-atribute text style transfer. arXiv preprint arXiv: 1.00555,, 2018.
Wei Xu, Alan Ritter, William B Dolan, Ralph Grishman, and Colin Cherry. Paraphrasing for style. In Proceedings of COLING 2012, pp. 2899-2914, 2012.
Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, and Yann LeCun. Adversarially regularized autoencoders. In International conference on machine learning, pp. 5902-5911. PMLR, 2018.
Jun- Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pp. 2223-2232, 2017.

---

Under review as a conference paper at ICLR 2022
APPENDIX
A CLASSIFIER ACCURACY IN THE REAL TASK
Here, we report performance of the invariant classifiers C, and C, to classify based on sentiment and category respectively when their coupling is reversed (Section 4.2). We take positive reviews from CSJ and HK, and take negative reviews from E and CPA. The test accuracy in shown Table 7.
Table Classifier accuracy in the real task when sentiment-category coupling is reversed.
Model
Sentiment ACC
Category ACC
CERM
64.0
36.0
C2
80.4
Co
62.9
B TRADE-OFF OF DIFFERENT WEIGHT COMBINATIONS OF Loss TERMS
Table 8: Automatic evaluation results of sentiment transfer from different categories. Accuracies less than 30 are marked in red.
Model
Sentiment ACC
Category ACC
PPL
BLEU
Krishna al. (2020) He et al. (2020)
22.6
57.4
35.8
19.2
77.7
22.5
44.6
47.6
AEEOO ALM ABT 1, 1,1
87.8
15.8
42.8
48.0
ACraS ALM ABT = 2, 1,1
89.6
14.6
42.4
47.0
ACras ALM; ABT 1,
90.2
10.3
49.2
47.7
AC, ALM ABT 1,1,1
72.8
38.7
45.3
61.7
AC, ALM, ABT = 1,
78.0
36.4
45.1
59.2
Ac, ALMs ABT = 4, 1,
85.0
29.5
42.5
53.0
Ac,, AC., ALM, ABT = 1,1,1.1
68.9
56.6
49.8
63.4
Ac,, AC. ALM ABT 2,,1,11
73.1
53.3
51.7
61.4
AC, ALM, ABT = -,,11111
79.9
50.9
49.2
57.4
Input Copy
3.1
75.3
34.5
100.0
12

---

Under review as a conference paper at ICLR 2022
C
ADDITIONAL EXAMPLES
Table 9: Additional example outputs of the synthetic sentiment transfer task.
Input
the new management team horrible.
Reference
the management team great
Krishna et al.
the new management team terrible
He et al.
the new management team excellent !
M w/ CERM
the new management team amazing
M w/ C.
the new management team great the new management team great
Ours
Input
did n't even get response.
Reference
even got a response..
Krishna
they didn't even respond my question.
He al.
did a great job and the staff very friendly
did n't even get response M w/ C.
M w/ CerM
did and even get response
Ours
always great and always response...
Input
got my car detailed here and it looked amazing car was detailed came out bad!
Reference
Krishna al.
had car detailed and it looked incredible got my car detailed here and it looked awful got my car detailed here and it looked terrible. got my car detailed here and it looked horrible.
He et al.
M w/ CERM
M w/ C.
Ours
got my car detailed here, looked awful
Input
they super friendly very helpful, and amazingly quick! !
Reference
slow and rude, not helpful at all
Krishna et al.
they were very friendly. very helpful, and unbelievably quick !).
He et al.
they are super rude , very helpful, and amazingly slow
M w/ CraM
they are super rude very unprofessional, and amazingly quick. they extremely rude, helpful, , and amazingly quick. they incredibly rude, not helpful but amazingly quick
M w/ C2
Ours
13

---

Under review as a conference paper at ICLR 2022
Table 10: Additional example outputs sentiment transfer from different categories.
Clothing, Shocs and Jewelry (negative positive)
Input
very thin material. and too short for me i 'm 5'10" love it just too thin, too short, and too short.
Krishna al.
He et al.
very nice case and great for iphone 's. i 'm 5'100 very nice product. and fine me I'm 5'10" "
Ours
Input
too big for my face
Krishna al.
too big for my face
He et al.
great fit for my s6
Ours
works great for my face
Home and Kitchen (negative positive)
Input
does not solve the lid problem
Krishna et al.
no way to fix the problem with the lid !
He et al.
does the trick for everything problem
Ours
does the job the lid problem!
Input
very poor quality crooked one end it is poor quality.
Krishna al.
He et al.
very good quality accurate one end very good quality crooked on one end.
Ours
Electronics (positive + negative)
Input
this is great easy to use easy to use...
Krishna et al.
He et al.
this is too small had to return this was broken. it difficult to use
Ours
Input
the drive works designed the drive works designed
Krishna al.
He et al.
the sized did not work properly
Ours
the drive is too large
Cell Phones and Accessories (positive + negative)
Input
it is durable and not bulky thought not as heavy duty as thought it would be it is uncomfortable and not as bulky thought it is cheap and not as protective as thought
Krishna et al.
He et al.
Ours
Input
excellent quality and perfect fit great quality and perfect fit horrible quality and returned it
Krishna al.
He et al.
Ours
extremely small and tight fit
14


Reviews:
[{"review_id": "vEOd7O4nclI", "reviewer": "Reviewer_6xK3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths\n\n- The idea of using Invariant Risk Minimization is interesting. \n\nComments/questions on the proposed methods and evaluation\n\n- In section 3.1.2, how to learn `an IRM classifier`? Or, to be specific, how this classifier can be implemented? \n- In table 2, if the sentiment classifier (84.3) can outperform human reference (76.8), I am not sure whether we should trust it or how we should interpret its results? \n- In section 4.2, it is a good idea of using BLEU with source sentences as a way to measure content preservation, however, this is not sufficient --- when input copy can give a perfect BLEU score, then we should not read too much from 59.2. Additional evaluation methods are needed here. \n- Overall, the proposed method seems to be similar to relevant to the idea of learning discriminators for generation tasks, which has been used in prior work of text style transfer. Based on the description, I am not sure whether the proposed method can be better than learning a collection of discriminators, where each discriminator by design will tell or not tell the difference within an environment.\n- As a method that has the potentials to be used in many other problems (as described in the introduction), it is disappointing to see both evaluation tasks are sentiment transfer. Would it be possible to at least try some other text style transfer tasks? \n\n\nComments on writing\n\n- It would be great if the explanation of IRM in section 3.1 can be self-contained and based on the proposed task (text style transfer)\n- In section 3.1.1, it says \n> style is a feature representation that elicits an invariant classifier across different environment\n\nIn general, I don't think this statement is valid. By constructing the `environments`, it can eliminate other factors, but there is no guarantee that style is the only factor left. ", "summary_of_the_paper": "This paper presents a method that can work on a single text style transfer task with multiple datasets. One example scenario as exemplified in the paper is the sentiment transfer task with datasets on multiple product categories. The basic idea of this work is to design a cross alignment between datasets from different categories, so the learning algorithm can focus on the expected stylistic information and eliminate other confounding factors. The proposed methods were evaluated on the sentiment transfer task with two different setups to demonstrate the value. ", "summary_of_the_review": "The major concerns of this work are the novelty of the proposed method and the sufficiency of evaluation. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "ZToqdSpoVUT", "reviewer": "Reviewer_Lb6o", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Pros:\n\n+ interesting application and synthetic dataset\n\nCons:\n\n- outdated encoder and text generator\n\n- less general framework than [Subramanian et al., 2018] \n\n==\n\nI don't understand the synthetic noise addition by punctuation change: what are the changes?\n\nWhy relying on a so old model for text modeling? What would be the performance of BERT or T5 on the same task?\nGao, Z., Feng, A., Song, X., & Wu, X. (2019). Target-dependent sentiment classification with BERT. IEEE Access, 7, 154290-154299.\n\nThis framework is very reductive compared to [Subramanian et al., 2018] in which feeling is simply one attribute among others. One wonders what this approach would look like in the context of the experiments conducted in [Subramanian et al., 2018].\n\nThe authors propose to compare their approach with baselines that do not take into account all hypothesis... But they do not compare their work with existing approaches from the litterature.\n\nIs it reasonnable to use a 1-layer LSTM for text generation purpose today? Even if GPT is very expensive, BART or T5 could have been investigated for such a task.\n\nRegarding table 2, I understand that Puntuation ACC can be low but I don't get how Sentiment ACC can be so low whereas it is a binary classification task.\n\nGenerally speaking, sentiment accuracy seems not competitive with state of the art models, even if I am not familiar with the dataset from  Shen et al. (2017). \n\nComparing authors approach with Krishna et al. (2020) seems not relevant (even according to the authors)", "summary_of_the_paper": "This article deals with style transfer. It proposes an approach to generate texts with opposite sentiments in a adversarial context where the style is correlated with other contextual factors such as domain or noisy punctuation.\nGiven the fact that other contextual factors are weakly labeled, the authors introduce Invariant Classifers to extract the style from other attributes.\nThe general framework has been defined by (Arjovsky et al., 2019, IRM) and the basic classifier is (Kim, 2014). Section 3.2 describes the authors' contribution where multiple orthogonal classifiers enables them to transfer style while preserving other attributes.\nThe authors propose relevant baselines corresponding to hypotheses ablations. The text generator is a 1-layer LSTM.\nThe authors demonstrate that their approach is able to transfer sentiment while preserving domain or punctuation depending on the experiment.", "summary_of_the_review": "This work is interesting and consistant but the fact that both the encoder and the generator are outdated seems critical regarding a pure NLP paper. Despite some clear qualities, I have to reject this contribution.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "UJ5M6sSC4WG", "reviewer": "Reviewer_fSZh", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "\n**Strengths**\n\n1. Style transfer is an important problem in natural language generation, with several practical applications (as discussed in [1]). The authors have identified a worrying issue in current style transfer systems (modification of non-target features like content), and attempted to fix this issue via invariant risk minimization (IRM).\n\n2. The methods developed in this paper are quite novel --- to the best of my knowledge this is the first work using IRM for style transfer. IRM is a good fit for the confounder issue in style transfer, and I found the ideas in the paper interesting. The authors use an interesting set of loss functions to use these orthogonal classifiers to guide the model to perform style transfer without modification of confounders.\n\n3. The authors test the approach on sentiment transfer, studying one synthetic confounder and one real-world confounder (product category). Experiments confirm the models trained by the model modify sentiment while keeping the confounder consistent. The authors test their method using both automatic and human evaluations, comparing it against strong baselines. On both kinds of metrics the authors see improvements using their proposed approach.\n\n**Weaknesses**\n\n1. My biggest concern with IRM is the need to identify environments across which the style classifiers should be consistent. In the sentiment transfer case, product category is certainly a valid confound which leads to semantic modification, but there could be several others, for instance intra-category variation of features of different products, writing style of author (formality / simplicity / usage of analogies). Obtaining these group annotations might be difficult in practice. The current approach also needs an orthogonal classifier, which needs a well-defined label space for the confounding factor. Many confounds related to semantics might be hard to precisely define & identify.\n\n2. It would be useful to see the applicability of this approach on one more task which is not sentiment transfer. One possibility could be using the GYAFC formality transfer corpus, which has two categories of data (Entertainment & Music, Family & Relationships). Also while maybe this is out of the scope of this work, it will be interesting to see if confounding factors can be identified automatically. In a task like converting Shakesepare <---> Tweets (https://arxiv.org/abs/2010.05700), there will be several confounding features between the two styles. For instance, Shakespeare data will be on topics from the 1500s, whereas Tweets will mostly contain modern 21st century topics. These features will need to be disentangled from style (lexical / syntactic choice) for successful semantic preserving style transfer. Here it will be harder to segregate the corpus into suitable environments.\n\n3. The ablation studies in Appendix B could be stronger --- what happens when you completely remove certain loss functions (weight = 0)? Also it's hard to compare between rows in this table since it's missing an \"overall score\" / aggregation like you did for the human evaluation.\n\n**Minor**\n\n1. The human evaluation is missing checking the accuracy of the confounding factor like you did for automatic evaluation (\"CATEGORY ACC\")\n\n2. I would avoid branding this paper as \"style transfer\" since style generally refers to properties unrelated to semantics unlike sentiment (which is the focus of this work). The term \"attribute transfer\" might be more appropriate.", "summary_of_the_paper": "Style transfer is a text generation task where a certain attribute of a sentence (like formality) is modified while preserving sentence content. It is generally studied in \"unsupervised\" settings --- no access to parallel data of sentences differing only in the target attribute. Nevertheless, algorithms assume access to corpora of unpaired sentences in each of the attributes, which may significantly differ in content. Prior works [1, 2, 3, 5] have noted this difference in content, which is encouraging models to modify non-target attributes like semantics (See Table 1 in [4]).\n\nThis paper is an attempt to fix this issue using invariant risk minimization (IRM) [6]. IRM encourages classifiers to be invariant across \"environments\", or datasets which share a target class but differ in other aspects which are not essential for classification (\"spurious correlations\"). In this work two spurious correlations are studied in sentiment transfer --- (1) a synthetic punctuation correlation; (2) product category of Amazon reviews. To perform style transfer, the authors first train two orthogonal IRM classifiers --- the first classifies the target attribute (\"sentiment\") irrespective of the confounder (\"product category\"), and the second for the confounder (\"product category\") irrespective of sentiment. These two classifiers are used to guide model generation, along with an LM smoothing and backtranslation objective (similar to [4]).\n\nThe authors compare their proposed approach against strong baselines and report promising results.\n\n[1] - https://arxiv.org/abs/1910.03747  \n[2] - https://arxiv.org/abs/2010.05700  \n[3] - https://dl.acm.org/doi/10.5555/3016100.3016326  \n[4] - https://arxiv.org/abs/1811.00552  \n[5] - https://arxiv.org/abs/1905.13464  \n[6] - https://arxiv.org/abs/1907.02893", "summary_of_the_review": "Overall, I thought the paper was well-written, had very interesting ideas and a good evaluation / comparison against prior work. Weakness #1/#2 will prevent me from going to the next higher score (of 8), but I'm in favour of acceptance.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "T_fBdfPXXC", "reviewer": "Reviewer_9yNQ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper is tackling an important problem where we only want to change certain aspects of the text and maintains other characteristics. \nThe main concern is about the effectiveness of the proposed method. According to the human evaluation in Table 5, the other two baselines are doing better or close to the proposed method. I would also suggest adding human evaluation experiments on M w/C_ERM and M w / C_s. In Table 4, we also see that ERM loss achieves the best sentiment accuracy.  It shows better category accuracy, however, the category classifier only has a test accuracy around 71%, which makes the Category Acc column not very informative. The paper has demonstrated relatively good results on the synthetic sentiment transfer task. On the other hand, it would make the paper stronger if more real tasks and datasets can be evaluated. \n\nAnother missing part is the ablations studies of L_LM and L_BT in Eq 9. I understand that these two losses have been investigated in other works, it would still be good to know the benefits from them. I would also suggest adding qualitative results of ERM loss in Table 3 and Table 6, as well as the results of C_s in table 6. And a follow-up question is has the combination of C_ERM and C_o been tested ever? It might be interesting to take a look.", "summary_of_the_paper": "This paper considers a text style transfer task where no paired sentences are available. For example, we have positive Clothing reviews and negative Cell Phone reviews, the paper tries to generate negative Clothing reviews. Note that it is likely the model transfers not only the sentiment (from positive to negative) but also product category (from clothing to cell phone). To avoid that, the paper proposes to learn an invariant style classifier (which can classify the sentiment regardless of the product category). Besides, the paper also proposes to learn an orthogonal classifier which can monitor any style-independent changes (e.g. product category). ", "summary_of_the_review": "The paper needs stronger empirical results. The proposed method is mainly built on top of Invariant Risk Minimization, thus lacks novelty. There are also ablation studies missing. I would recommend to reject this paper.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies text style transfer which aims to edit a given sentence to possess a desired style value (e.g., positive sentiment) while keeping all other styles and content unchanged. The paper specifically focuses on a challenging setting where besides the target style (e.g., sentiment) to transfer, there exists confounding attributes (e.g., product category) that correlate with the target style, making it hard to change only the target style while preserving the other. The proposed approach is to learn an invariant/unbiased style classifier using Invariance Risk Minimization (IRM), together with an orthogonal classifier for monitoring style-independent changes (e.g., product category), to supervise the generator training. The main concerns are on the experiments -- it's suggested to include experiments on other styles besides sentiment; human evaluation and/or other metrics are needed for more convincing comparison; it's also encouraged to experiment with large language models (e.g., GPT-2, BART) besides the small LSTM/CNN networks as in the present work.
Decision: Reject