COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification
Authors: Mohammad Mahdi Abdollah Pour
Parsa Farinneya
Manasa Bharadwaj
Nikhil Verma
Ali Pesaranghader
Scott Sanner
Venue: EMNLP 2023 Findings (2023)
Keywords: Text Style Transfer
Detoxification
Unlikelihood Training
TL;DR: Addressing this deficiency of unlikelihood for text detoxification by Contrastive Unlikelihood, to effectively isolate and focus learning on "style" transfer

Abstract: Offensive and toxic text on social media platforms can lead to polarization and divisiveness within online communities and hinders constructive dialogue. Text detoxification is a crucial task in natural language processing to ensure the generation of non-toxic and safe text. Text detoxification is a special case of the Text Style Transfer (TST) problem, where an input text is rephrased to an output text that preserves its content while modifying the style (in this case to a more neutral, non-toxic style). State-of-the-art methods for detoxification use supervised training of encoder-decoder models to produce gold-standard outputs with a standard likelihood-based objective. However, it can be hard for these models to deviate from their pretrained auto-encoder identity mapping. While previous methods have used unlikelihood-based losses to penalize input-to-output copying of toxic content, these methods also unfortunately penalize non-toxic content in the input that would be fine to preserve in the output. To address these issues, we introduce a novel contrastive unlikelihood objective (COUNT) that directly contrasts the gold standard rephrasing with the identity input-to-output mapping to effectively isolate and focus learning on non-toxic style transfer. We benchmark COUNT on two parallel datasets, ParaDetox and APPDIA, showing that it achieves significant improvements in jointly combined fluency, content preservation, and detoxification (i.e., the highest “J” score).

COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification
Mohammad Mahdi Abdollah Pour', Parsa Farinneya', Manasa Bharadwaj', Nikhil Verma', Ali Pesaranghader* and Scott Sanner'
Uniiveriity of Toronto, Canada
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LG Electronics, Toronto Al Lab
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nikhil,,,,i nikhil. verma, ali. pesaranghader))lgge com
Abstract
2023). Such experiences can deter individuals from actively engaging in online communities and social media. Therefore, text detoxification solutions are indispensable for mitigating harmful content on online platforms.
Offensive and toxic text on social media plat- forms can lead to polarization and divisive- ness within online communities and hinders constructive dialogue. Text detoxification is a crucial task in natural language processing to ensure the generation of non-toxic and safe text. Text detoxification is a special case of the Text Style Transfer (TST) problem, where an input text is rephrased to an output text that preserves its content while modifying the style (in this case to a more neutral, non-toxic style). State-of of the-art methods for detoxifica- tion supervised training of encoder-decodler models to produce gold-standard outputs with a standard likelihood-bon based objective. How- ever, it can be hard for these models devi- ate from their pretrained auto-encoder identity mapping. While previous methods have used unlikelihood- losses to penalize input-to- output copying of toxic content, these methods also unfortunately penalize non-toxic content in the input that would be fine to preserve in the output. To address these issues, we intro- duce novel contrastive unlikelihood objec- tive (COUNT') that directly contrasts the gold standard rephrasing with the identity input-to- output mapping to effectively isolate and focus learning on non-toxic style transfer. We bench- mark COUNT on two parallel datasets, Pa- raDetox and APPDIA, showing that it achieves significant improvements in jointly combined fluency, content preservation, and detoxifica- tion (i.e., the highest "I" score).
Text detoxification is a text-style transfer task where the goal is to change the style of an input text from toxic to safe while the content is preserved. Most state-of-the-arr text detoxification methods use supervised training of encoder-decoder models (Logacheva al., 2022). The objective of those methods relies on maximizing the likelihood of gen- crating gold-stundard non-toxic outputs, but fails to penalize generation the toxic inputs. To address this issue, Welleck et al. (2019) proposed an "un- likelihood" training methodology that can be used to penalize generation of toxic inputs while encour- aging generation of gold standard non-toxic output. While this improves on the simple likelihood objec- tive, we observe experimentally that it often fails overcome the strong inertia of the pretrained model to produce the identity input-to-output mapping that preserves toxic text. One key reason for this is that unlikelihood cannot be weighted too heavily can penalize both the toxic and non-toxic content in the input. To address this issue, we introducc novel Contrastive Unlikelihood Text style trans- fer (COUNT) objective that directly contrasts the likelihood of the gold standard rephrasing with the likelihood of the identity suput-to-outply mapping to effectively isolate and focus learning on style transfer for text detoxification.
1 Introduction
Disclaimer. Please be aware that as you read this paper, you may come across texts that could be considered toxic due to the nature of this research. Exposure to offensive and toxic text online platforms have detrimental consequences, caus- ing emotional distress and negative psychologi- cal effects on users Bonzález-Bailón and Lelkes,
Our contributions are twofold: (1) We propose the COUNT loss function as novel training ob- jective for text detoxification.
(2) We show that by using the COUNT loss, method delivers significant improvements a joint measure ("J" score) of fluency, content preservation, and detoxi- fication on two publicly available parallel datasets, ParaDetox and APPDIA, for the text detoxification task.
htttps: http:////gittuubb.mmm/aaaaaaa com/D3Maab/ count-style-trance transfer

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2
Related Work
ple objective nature of text style transfer (preserve content, preserve fluency, change style) has not been properly and systematically addressed as Par- aGeDi relies on heuristics for content preservation. Unlikelihood training has been used in control- lable text generation applications to avoid undesir- able tokens with high probability (Welleck al., 2019). CLICK (Zheng et al., 2023), SLiC (Zhao et al., 2022), BRIO (Liu et al., 2022) and CRINGE (Adolphs et al., 2022) also use unlikelihood train- ing for various text generation applications such as summarization and sentiment control. Despite the popularity of unlikelihood training in text genera- tion, it has not been widely applied to the text style transfer task. We empirically observe that unlikeli- hood (Welleck al., 2019) training for text style transfer often fails to properly penalize the toxic samples since has two training objectives that of- ten conflict. Specifically, penalizing the likelihood of generating the toxic sample contradicts with maximizing the likelihood of the gold-standard tar- get since the gold-standard rephrasing often over- laps substantially with the input and the loss can- not discriminate the toxic from the non-toxic con- tent. Addressing this deficiency of unlikelihood for detoxification is our primary focus this work.
2.1 Text Detoxification
Text detoxification solutions primarily fall into two main categories, unsupervised and supervised. We describe each category below:
Unsupervised. The unsupervised methods are typ- ically built on a non-parallel dataset, which a set of toxic and a set of non-toxic texts without one- to-one mappings between them. Mask& (Wu et al., 2019), DRG-Template/Reriiee (Li et al., 2018), CondBERT (Dale et al., 2021) and SST (Lee, 2020) perform pointwise correction of the tokens that mostly affect the style. DLSM (He et al., 2020) and Luo et al. (2019) train the encoder- decoder detoxification model using non-parallel data. He et al. (2020) uses amortized variational in- ference. Luo et al. (2019) use reinforcement Iearn- ing and a style classifier. CAE-T5 (Laugier et al., 2021) tried to balance content preservation and style accuracy in unsupervised setting using loss made of conditional auto-encoding and cycle consistency. Shen al. (2017) and Fu et al. (2018) use adversarial classifiers to adapt their decoder to generate text of non-toxic style. ParaGeDi (Dale et al., 2021) uses a paraphraser model guided by style-trained language models for detoxification. In this work, we focus on supervised methods dis- cussed next that typically perform better.
Methodology
The fext-eetoxification task aims to generate a non- toxic sentence y given a toxic input x while pre- serving the content of r. State-of-the-art detoxification methods (Lo- gacheva et al., 2022) fine-tune an encoder-decoder model such as BART (Lewis et al., 2020) for detox- ification. BART is a sequence-to-sequeme denois- ing autoencoder that has been trained to recon- struct the input text. The standard approach for fine-tuning encoder-decoder models maximizes the probability of generating target sequence y² by the decoder, given the input x to the encoder. This is done using the standard Language Modeling (LM)
Supervised. In contrast, the supervised methods are usually built on parallel datasets in which one- to-one mappings between toxic and non-toxic texts exist. ParaDetox (Logacheva al., 2022) fine- tunes BART (Lewis et al., 2020) on their parallel data. APPDIA (Atwell et al., 2022) introduces a discourse-aware style-transfer model to reduce offensiveness in the text while preserving the se- mantics of the original text. While Logacheva et al. (2022) and Atwell al. (2022) achieve some of the most promising results for detoxification, their methods do not fully penalize the toxic style in the training process and thus fail to detoxify inputs in many cases as we demonstrate empirically. This is the aspect we aim to improve in this work.
loss:
LLM = EEagr Loggoooy = y g²(x))] (I)
2.2 Penalizing Negative Examples
where y" is non-toxic paraphrase of toxic text 2. The (x,y") pairs are available in the parallel detoxification dataset D. pe(y = >²(=>) is the likelihood of string y* as the output when x is given as the input to the encoder-decoder model parametrized by B.
Previous work on detoxification did not systemat- ically address penalizing the toxic samples. For instance, ParaGeDi (Dale al., 2021) proposes a loss to penalize the generated texts that are labeled as toxic by toxicity classifiers. However, the multi-

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For fine-tuning, Lq. (1) encourages the output have non-toxic style by training the model to have a high likelihood for y" (learning from parallel data). However, in the detoxification task, we need to also penalize the toxicity in addition to maximizing the likelihood of target sequence v" Since encoder-decoder models such as BART reconstruct the input, these models need to deviate from the identity mapping to reduce toxicity.
is a hyperparameter and the value for a is chosen on validation data (cf. Appendix A for details).
3.1 Comparative analysis of COUNT vs UT
We now compare and contrast the COUNT loss to the UT loss to understand their differences. For- mally, in the UT loss of Eq. (2) and the COUNT loss of Eq. (4), let us assume we can factorize the likelihood of a target style transfer example into two components such that po(y - y [x) = = - where p the probability of common content be- tween the input & and the target y² and pMu is the probability of non-toxic content in y"- SSiillrry, let us assume that the likelihood of the identity de- coding factorizes as pa(y x: xx(x) p pf, where Ve is the same as before and of is the probability of the toxic content in æ. Then we can respectively
Unlikelihood training (Welleck al., 2019) can be used to penalize toxicity by minimizing the like- lihood of toxic samples using the following Unlike- lihood Training (UT) loss (Welleck et al., 2019):
LUT =
Ez>a(- log(1 - py = x|+)]]
(2)
Then we would optimize the total loss
Ltotal-UP = LLM + a LUT-
(3)
rewrite our two losses in Eq. (2) and (4) as follows:
However, minimizing the likelihood of (y æ| =/(x) partially interferes with maximizing the likeli- hood of po(y = y y²(=r) in fLM since x and y have similar content in the detoxification task and only differ in their style (toxic vs. non-toxic).
LUT BewDl- Bewwl-Log(11 Pe p())
(6)
p Pat
LCOUNT B )DD
llog Pe pst + Pē pf
Ve phi
Therefore, we propose the COUNT loss function to discriminatively focus on detoxifying the style of the text. Specifically, the COUNT loss func- tion contrasts toxic sentence x and target non-toxic paraphrase y" as they have the same semantics but a different style. Hence, contrastively maxi- mizing the likelihood of poly y" (x)) relative to the likelihood of po a|(r) helps normalize for properties such as text length and the likelihood of shared words between and y* that are irrelevant to style while focusing on maximizing the gap be- tween them (i.c., the toxic component). Formally, the COUNT loss function is defined as follows:
log
De (w' + pt)
P'tt
= =B
log
(7)
y + P
In both cases, we want to minimize the overall loss, which is equivalent to maximizing the quan- tity inside the - log[+]] Here we can see that the UT loss aims to maximize 1 P pf, which is equivalent minimizing p: pe: hence, it aims to minimize the joint probability of both the common and toxic components. In contrast, the COUNT loss cancels any contribution of the common com- ponents p" and is maximized as the probability of the non-toxic content Ph increases relative to the toxic content yf In this way, the COUNT loss is able to isolate and minimize toxic content while the UT loss additionally penalizes common non-toxic content between y² and æ.
LCOUNT = (a
[- log(
poly - y² v((x) Py (x) + poly = )
-log(
To minimize the loss in Eq. (4), the model is aim- ing for a large probability of the non-toxic refer- ence po(y= = y'(rr) relative to the identity decoding poly = x|x). However, as show in the next section, this loss aims to avoid penalizing the com- mon (non-toxic) content between y/" and y since the probability of this content cancels in the ratio.
As a final remark for completeness, both UT and COUNT provide a mixture of their loss with the LM loss, which is not considered in the analysis above, but which can be considered an orthogonal loss simply intended to ensure the output achieves high likelihood under a standard language model.
We combine the COUNT loss with the standard language modeling loss so the total loss would be:
In terms of time complexity, LLM requires com- puting (-l--) only once for po = yz(z). LUt requires computing poly = x x|z) (i.e., y is an
Ltotal NT = LIM + a LCoUNT (5)

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identity copy of x), therefore the full Lto (including the LM component) requires comput- ing po(-l.) twice. LCOUNT requires computing po(y - )²)x) and poly = x|x), therefore the full Liotal COUNT also requires computing po(-I-) twice since COUNT's (y = -²(=) computation can be reused for the LM component. Hence, the final COUNT and UT losses each have the same computational cost, which is constant multi- ple of the computational cost of the LM loss.
4.3 Experimental Results
Tables 1 and show the performance of the pro- posed COUNT loss (Eq. (5) function compared to the original LM loss function (Eq. (1)) and the UT loss (Eq. (3)) for training BART on the Pa- radetox and APPDIA datasets. The COUNT loss function provides significant improvement in both datasets over the original LM loss function and UT loss. While SIM and FL are almost similar in original loss function and the proposed loss in Paradetox, the improvement in the J score is due to the improvement in STA. Similarly for APPDIA, while FI. and SIM are lower, the improvement in J comes from significant improvement in STA. The improvements in the STA scores show that the pro- posed COUNT loss function focuses on penalizing the toxic style which leads to higher STA scores and a better detoxification performance in general. Detoxified examples can be found in Appendix B. Lower SIM scores for COUNT loss are reason- able since the examples in the APPDIA dataset need more substantial rewriting to be detoxified compared Paradetox. Hence. as the paraphrased texts improve they tend be less similar the original toxic text that was used as input (due to deletion or modification of the toxic part).
4 Experiments
4.1 Experimental Settings
Datasets. We study two parallel datasets for eval- uating detoxification methods: ParaDetox (Lo- gacheva et al., 2022) contains non-toxic para- phrases of toxic seniences. The partitioning of the dataset includes two sets, one for training with 11,939 sentence pairs and the other for testing with 671 sentences rephrases. APPDIA (Atwell al., 2022) was created using comments collected from the well-known social discussion website Reddit. The authors collected nearly 2,000 toxic comments which were then annotated by experts in the field of sociolinguisties to generate the non-toxic version of toxic sentences. The dataset has an 80-10-10 split for train vili-vvaiiaaionnees splitting.
Based results in Tables 1 and 2, the UT loss performs better than the original LM loss in STA score but does not do as well as the COUNT loss in penalizing the toxic style and thus yields lower STA scores compared to the COUNT loss and lower scores. The reason is that penalizing the toxicity in UT loss is in contrast with maximizing the likelihood of the gold standard target.
Metrics. We follow the well-established text detox- ification work (Logacheva al., 2022) evalu- ate our experiments with BLEU, Style Accuracy (STA), Semantic Similarity or Content Preserva- tion (SIM), Fluency (FL), and J score. In partic- ular, STA and FL are computed with pre-trained classifiers (Warstadt et al., 2019) to measure the non-toxicity and fluency of given sentence, re- spectively. SIM is computed using cosine similar- ity between the input and the generated detoxified text with the model of Wicting et al. (2019). More- over, we compute J score (Krishna et al., 2020) as the average product of STA, SIM, and FL.
Table compares the proposed method's perfor- mance to the Paradetox dataset's baselines. Results show that by using the proposed COUNT loss func- tion, we can significantly improve the style transfer accuracy and outperform all of the baselines in the J score. DRG-Retrieve, CondBERT, ParaGeDi, and DiffuDetox (Floto et al., 2023) have better STA scores than the COUNT method but the mentioned baselines fail have balanced performance among all of the three evaluation metries (STA, SIM, FL.) and mostly fail significantly in one of them caus- ing them to have a lower score. In contrast, our COUNT methodology exhibits a clear balance in performance among all task objectives.
4.2 Implementation Details
We use an Adam optimizer (Kingma and Ba, 2015) with a learning rate of 5e-5 and use a validation set of 20% for Paradetox hyperparameter tuning. The validation set for APPDIA is provided by the dataset. The evaluation toolkit is the same Lo- gacheva et al. ?
Table compares the proposed COUNT method with the baseline on the APPDIA dataset. BART, DialoGPT, and T5 are baselines from Atwell et al.
hhttps://githhe www.llccoccsssaaee detoxification evaluaatio

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BLEU
STA
SIM
FL
gate whether fine-tuning stronger LM capable of masking toxicity via semantic ambiguity (which oc- curs not only with the COUNT loss, but with other methods), we remark that not only is it a failure, but it is often missed by evaluation metrics. This leads to two related but very different directions 0.10
Human
100.0
0.96
0.77
0.88
0.66
DRG-Template
53.86
0.90
0.82
0.69
0.51
DRG-Retrieve
4.74
0.97
0.36
0.86
0.31
Insk&Infill
52.47
0.91
0.82
0.63
0.48
COndBERT
42.45
0.98
0.77
0.88
0.62
SST
30.20
0,86
0.57
0.19
ParaGeDi
25.39
0.99
0.71
0.88
0.62
for future work: (i) It would be useful to investi-
DLSM
21.13
0.76
0.76
0.52
0.25
DiffuDetox
62.13
0.92
0.88
0.80
0.67
deeper semantic reasoning (e.g.. GPT-3.5 Turbo) may be able to penalize the likelihood of outputs with such semantic ambiguity under the COUNT loss, thus leading to better performance. (ii) Since wc' ve also noted that existing evaluation metrics may fail to detect such cases in the first place, this also points to the need for better- autema-e toxicity evaluation, which may he remedied by investigat- ing whether prompting methodologies with SOTA LMs like GPT-3.5/4 can better reason about these semantically subtle failure cases in ways that exist-
ParaDetox
64.53
0.89
0.86
0.89
0.68
Original LM Loss
71.84
0.89
0.89
0.90
0.71
UT Loss
72.43
0.87
0.89
0.90
0.71
COUNT (Ours)
69.68
0.91
0.88
0.91
0.74
Table 1: Text detoxification performance on the Pa- raDetox dataset. Baseline results are taken from (Lo- gacheva et al., 2022). The best results are in boldface.
BLEU
STA
SIM
FL
Human
60.18
0.87
0.77
0.95
0.65
BART
75.85
0.72
0.88
0.96
0.61
DialoGPT
45.12
0.85
0.70
0.81
0.46
PDTB+RST
49.46
0.85
0.73
0.87
0.53
ing evaluation metries fail to detect.
T5
74.69
0.82
0.88
0.97
0.70
Evaluation of Detoxification by Difficulty Level:
Original LM Loss
79.00
0.69
0.91
0.97
0.60
UT Loss
72.65
0.80
0.88
0.95
0.60
We have observed that detoxification difficulty can vary widely between test cases. To better under- stand comparative performance differences, we ad- vocate that future work subdivide toxicity test cases and results analysis according to the amount of modification needed. For example, difficulty range across the following categories of increasing hardness: (i) simple toxic word replacement, (ii) phrase replacement starting at the toxic word, (iii) phrase replacement starting before the toxic word (i.e., harder for most forward- pass beam search de- coding methods), and (iv) substantial rephrasing to detoxify while preserving semantic content.
COUNT (Ours)
68.99
0.85
0.85
0.93
0.68
Table 2: Text detoxification performance on the APP- DIA dataset. The best results are in boldface.
(2022), and PDTB+RST is the methodology pro- posed by Atwell et al. (2022). The APPDIA dataset is small and due to the stochasticity of the results on APPDIA dataset, we report the results as the average of 5 runs. Our COUNT methodology again achieves significant improvements the STA score while having a comparable performance FL.
4.4 Failure Analysis and Future Directions
5 Conclusion
Failure Analysis: [WARNING: offensive con- tent from an actual example.] In Appendix B, we present examples of the original input, gold stan- dard (human) target, and outputs from training with the standard LM loss and COUNT loss. Overall, we remark that COUNT generally does an excellent job of removing explicit toxic content, but some- times only by creating semantic ambiguity. Notably in the last example, COUNT has replaced "those tits" with "they", which has masked an explicitly toxic phrase with an ambiguous reference. We view this as a subtle semantic failure that is not likely to be caught by existing automated evaluation metrics and perhaps even some human evaluators.
In this paper, we observed that existing methodolo- gies for detoxification either fail to penalize input- to-output copying of toxic content (LM loss) or also penalize input-to-output copying of non-toxic con- tent (UT loss). To resolve this, we introduced the novel COUNT loss function to contrastively penal- ize toxic text style in the detoxification task while effectively normalizing for other style-irrelevant aspects during training. Experimental results show the proposed COUNT method achieves significant improvements in the combined J-score metric for text detoxification on ParaDetox and APPDIA. In general, the COUNT methodology is not spe- cific to detoxification methods for text style transfer and has potential application to many other style transfer tasks that could be explored in future work.
Directions for Future Improvements: Given this previously observed failure mode of superficially

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Limitations
Griffin Floto. Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang. Ali Pesaranghader, Man- asa Bharadwaj. and Scott Sanner. 2023. DiffuDetox: A mixed diffusion model for text detoxification. In Findings of the Association for Computational Lin- guisties: ACI. 2023, pages 7566-7574, Toronto, Canada. Association for Computational Linguistics.
A first limitation of this work is that it relies on parallel datasets for training which may not be easy collect. A second limitation is that current detox- ification datasets (Logacheva al., 2022; Atwell et al., 2022) only focus on types of toxicities that use vulgar language. However, there are other types of toxicity that could be present in social media that do not necessarily use profanity or vulgar language. For example, cases of sarcasm, stereotypes, mock- ery,
Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018. Style transfer in text: Explo- ration and evaluation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32.
Sandra González-Bailon Bailón and Yphtach Lelkes. 2023. Do social media undermine social cohesion? a critical review, Social Issues and Policy Review, 17(1):155- 180.
etc. (Bhat et al., 2021). To leverage our methodology. there is need for par- allel detoxification datasets for broader range of toxicity types.
Junxian He, Xinyi Wang, Graham Neubig, and Taylor wing-Kikpattrick. 2020. A probabilistic formulation of unsupervised text style transfer. arXiv preprint arXiv:2002.03911
Ethical Considerations
Potential Misuse: Our method can be reversed and hypothetically used to obtain toxic sentences from non-toxic sentences. However, there are likely simpler ways for introducing toxicity that may mit- igate the likelihood of this case for misuse.
Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. arXiv preprint
101.x:1412.6980.
Kalpesh Krishna, John Wieting, and Mohit Tyyer. 2020. Reformulating unsupervised style transfer as para- phrase generation. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (FMNLP), pages 737-762.
Environmental Cost: We note that our work required extensive computational experiments to draw sound conclusions. However, models in pro- duction may not require such exhaustive experi- mentation and they can also be trained once using the most promising settings, thus mitigating future computational costs of this methodology.
Léo Laugier, John Pavlopoulos, Jeffrey Sorensen, and Lucas Dixon. 2021. Civil rephrases of toxic texts with self-supervised transformers. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguisties: Main Volume, pages 442214611
Joosung Lee. 2020. Stable style transformer: Delete and generate approach with encoder-decoder for text style transfer. In Proceedings of the 13th International Conference on Natural Language Generation, pages 195-204.
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Paradetox
a
0.2
0.5
1.0
2.0
3.0
4.0
5.0
0.694
0.710
0.700
0.703
0.700
0.706
0.701
APPDIA
0.2 0.652
0.5 0.700
1.0
2.0
3.0
4.0
0.5
0.670
0.671
0.622
0.597
0.637
Table score performance for different values of for validation set in COUNT total loss
Paradetox
αx
0.2
0.5
1.0
2.0
3.0
4.0
5.0
J
0.699
0.682
0.685
0.695
0.687
0.697
0.701
APPDIA
αx
0.2
0.5
1.0
2.0
3.0
4.0
5.0
0.625
0.620
0.629
0.642
0.622
0.586
0.527
Table J score performance for different values of for validation set in UT total loss.
Appendix A: Hyperparameter selection
We choose the value of hyperparameter in Eq. (5) and Eq. (3) based on score performance validation data over the of values for a. Tables and show the best value of a based on validation data performance along with performance for other values of a for all experiment scenarios.
B Appendix B: Examples
Table 5 shows some examples of toxic sentences along with human reference paraphrase, prediction LM loss method, and prediction of the COUNT method.

---

Toxie
Dump his ass. Tahlemanners are no rocket science.
Human
You should leave him. lablemanners are no rocket science.
Standard Language Modeling Loss
Dump his head. Tablemanners are no rocket science. Don't I talk about it. Tablemanners are no rocket science.
COUNT Loss
Toxic
what's 's to investigate, his duplicitous incompetence is manifest
Human
What is left to investigate?
Standard Language Modeling Loss
what's to investigate, his duplicitous incompetence is manifest.
COUNT Loss
what's left investigate?
Toxle
australian myselt, but don I say mate cunt all the time. an Australian, hut don't say profanity words the time.
Human
Standard Language Modeling Loss
m australian myself, don say mate or cunt all the time.
COUNT Loss
m australian myself, but i don say mate or other things all the time.
Toxie
not cheating if you fuck somebody and never comes up?
Human
So it's not cheating if you sleep with somebody and it never comes up?
Standard Language Modeling Loss
So it's not cheating if you fuck somchody and it never comes up?
COUNT Loss
So it's not cheating if you don't i talk to somebody and it never comes up?
Toxic
This is cringe. What a fucking loser.
Human
This is cringe.
Standard Language Modeling Loss
This cringe. What loser.
COUNT Loss
This cringe. What person.
Toxic
Hope the stupid who did this you gets shot
Human
Hope who did to you pays
Standard Language Modeling Loss
Hope person who did this to you gets shot
COUNT Loss
Hope the person who did this to you gets caught
Toxie
thank you. for ledgowgggging your own stupidity!
Human
thank you, for acknowlede ledginng the futility of your argument
Standard Language Modeling Loss
thank you. for acknowledging your own stupidity!
COUNT Loss
thank you. for ac acknowledging your misinformedness
Toxic
stopped listening to her when she first spoke but those tits keep me entertained. stopped listening to her when she first spoke but her looks keep me entertained.
Human
Standard Language Modeling Loss
stopped listening to her when she first spoke but those tits keep entertained.
COUNT Loss
stopped listening her when she first spoke but they keep me entertained.
Table 5: A Toxic sample with a human reference and model predictions using standard language modeling loss and our proposed loss function.


Reviews:
[{"review_id": "h9kPn3qgLs", "reviewer": "Reviewer_yYYJ", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Only BART is finetuned as part of the experiments.\n\nGiven the hyperparameter sweeps required for training and evaluation, our computational resources only permitted us to compare to fine-tuned variations of BART under the different loss functions proposed.  We do note that this was sufficient to match or exceed state-of-the-art results on detoxification, which we believe presents a solid contribution and achievement for a short paper, but we acknowledge that evaluation on different encoder-decoder architectures would nonetheless be informative.  We are currently working on a T5 implementation of the proposed framework and will aim to include these comparative results in the next revision.\n\n> Reproducibility\n\nThanks for noting these concerns.  We remark that the code to reproduce all results in this paper is in a GitHub repository whose URL will be made available in a published version of this work.", "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": "- Proposed method is well justified and experimental results indicate that methods improve on baselines. \n- Paper makes concise and clear improvement over previous work suitable for a short paper. \n- Paper is well written and easy to follow.", "reasons_to_reject": "- Generalization of the proposed loss to other models. Only BART is finetuned as part of the experiments. \n- Supplementary missing containing code is missing, would have helped verify the implementation of the proposed method.", "questions_to_authors": "", "paper_topic_and_main_contributions": "Authors present a novel loss function - COUNT - for penalizing toxic text style by modifying the unlikelihood loss used in previous works. Proposed COUNT loss objective contrasts gold rephrased non-toxic sentence. Proposed method is shown to perform better than previously proposed loss functions on two datasets - ParaDetox and APPDIA on \"J Score \" metric.", "excitement": 4, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "TPvyhsCT3v", "reviewer": "Reviewer_7tdq", "summary": "", "questions": "A) Are the training steps the same across all the systems compared?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Visual illustration of losses\n\nWe remark that we are significantly constrained by the present short paper limits, but if the reviewer has suggestions for how to visualize the losses, we’d certainly be open to a diagrammatic presentation to facilitate reader understanding.  \n\nThat said, we do remark that the LM loss is always a base additive term for the UT and COUNT losses so the key question mainly concerns how the UT and COUNT losses differ.\n\nIn lieu of a visual illustration, we have thought of a more mathematical way to illustrate key differences in the UT and COUNT loss functions, which we summarize as follows:\n\nFormally, in the UT loss of Equation (2) and the COUNT loss of Equation (4), let us assume we can cleanly factorize the likelihood of a target style transfer example $y^*$ into two components: let $p_c$ be the probability of common content between the input x (=identity output y) and the target $y^*$, $p_t$ be the probability of toxic content in y, and $p_{nt}$ be the probability of the detoxified content in $y^*$, then we can rewrite our two losses in (2) and (4) as follows:\n\nUT: argmin  -log (1-$p_c$*$p_t$) = argmax (1-$p_c$*$p_t$) \n\nCOUNT: argmin -log ($p_c$*$p_{nt}$) / [$p_c$ * $p_{nt}$ + $p_c$ * $p_t$] = argmax ($p_{nt}$ / ($p_{nt}$+$p_t$))\n\nHere we can see that the UT loss penalizes both the common (non-toxic) and non-toxic content whereas the COUNT loss *cancels* any contribution of the common components $p_c$ and isolates penalization of *only* the toxic components (i.e., there is no $p_c$ component in the COUNT loss, only $p_{nt}$ and $p_t$).\n\nAgain, both UT and COUNT provide a mixture of their loss with the LM loss, which is not considered in the analysis above, but which can be just considered an orthogonal loss simply intended to ensure the output achieves high likelihood under a standard language model.\n\nIf the reviewer thinks this is helpful, we will integrate this additional clarifying mathematical analysis into the main paper, or the Appendix if necessitated by space limitations.\n\n> In order to draw stronger conclusions, human evaluation would have been appreciated\n\nWe fully agree with the reviewer that this would provide an additional useful window on the analysis.  While we do not have the financial budget to perform a comprehensive multi-annotator comparative human evaluation of all results, we note that we currently present models outputs for human inspection in Table 5 of Appendix B and we can further augment this with a limited human evaluation on a subset of examples in the Appendix (or main paper, space permitting) to determine how well the automatic evaluation correlates with human evaluation if the reviewer believes such an additional evaluation would be helpful.\n\n> As COUNT is more complex to compute, a quick analysis of its complexity relative to UT and standard LM would have been appreciated.\n\nThanks, we agree this would be helpful and will add the following complexity analysis discussion to the paper:\n\n$Loss_{LM}$: requires computing $P_\\theta(.|.)$ only once for $P_\\theta(y=y^*|x)$. \n\n$Loss_{UT}$: requires computing $P_\\theta(y=x|x)$ (i.e., y is an identity copy of x), therefore the full $Loss_{total-UT}$ (including the LM component) requires computing $P_\\theta(.|.)$ twice. \n\n$Loss_{COUNT}$: requires computing $P_\\theta(y=y^*|x)$ and $P_\\theta(y=x|x)$, therefore the full $Loss_{total-COUNT}$ also requires computing $P_\\theta(.|.)$ twice since COUNT’s $P_\\theta(y=y^*|x)$ computation can be reused for the LM component. \n\nSummary: The Big-O complexity of all losses is the same, but the constants differ.  The final COUNT and UT losses each have the same computational cost, which is a 2x constant multiple of the computational cost of the LM loss.\n\n> A) Are the training steps the same across all the systems compared?\n\nNo, we use early stopping on the validation loss (same stopping criterion for all methods) to determine training termination.  We found this yields the best performance for each method.\n\n> Missing EACL reference\n\nThanks for the reference; we will certainly cite and discuss this unsupervised approach.", "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": "- While the objective loss is not standard, the proposed trick to palliate identity mapping is interesting\n- Experiences are sound: the authors compared their method on standard parallel datasets with standard automatic evaluation metrics\n- The problem statement, the method, and the results are clearly exposed.\n", "reasons_to_reject": "- I wish the authors illustrated their method with examples or diagrams in order to immediately visualize the difference between COUNT, UT and standard LM. The equations are self-explanatory, yet they may necessitate some time to fully grasp the insights they offer, particularly considering that this paper could ignite the development of novel objective functions for text style transfer in the future.\n- In order to draw stronger conclusions, human evaluation would have been appreciated\n- As COUNT is more complex to compute, a quick analysis of its complexity relative to UT and standard LM would have been appreciated.", "questions_to_authors": "A) Are the training steps the same across all the systems compared?", "paper_topic_and_main_contributions": "The paper proposes a novel training objective function, called COUNT, for the task of strongly supervised detoxification. The idea is to penalize identity mapping often observed in previous approaches based on maximizing the likelihood of generating ground-truth. COUNT maximizes the likelihood of generating the ground-truth, relative to generating the input toxic text. The paper explains how COUNT differs from a similar approach called Unlikelihood Training. Experiments quantitatively show the benefits of COUNT over previous training objectives and state-of-the-art models. ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Léo Laugier, John Pavlopoulos, Jeffrey Sorensen, and Lucas Dixon. 2021. Civil Rephrases Of Toxic Texts With Self-Supervised Transformers. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1442–1461, Online. Association for Computational Linguistics.\n\nThis paper also tried to balance content preservation and style accuracy for the detoxification task, but in an unsupervised setting. The authors introduced a loss made of conditional auto-encoding and cycle consistency."}}, {"review_id": "rLy8GMrWlA", "reviewer": "Reviewer_oxw4", "summary": "", "questions": "Question A: Why did not the models trained achieve the highest BLEU score?\n\nQuestion B: In Table 1, there are two lines -- ParaDetox and Original LM loss. What is the difference in their meanings? ParaDetox == Bart trained on ParaDetox, then what is Original LM loss? The same for Table 2, what is the difference between BART and Original LM Loss.\n\nQuestions C: How is DiffuDetox named in these Tables?\n\nQuestion D: In which cases do you think the model trained with your loss still failed? What is the further direction of improvements? Which types of toxicity cases (if it is possible to estimate) it perform better?There is a lack of discussion of the content detoxification part in the paper.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Human evaluation\n\nWe fully agree with the reviewer that this would provide an additional useful window on the analysis.  While we do not have the financial budget to perform a comprehensive multi-annotator comparative human evaluation of all results, we note that we currently present models outputs for human inspection in Table 5 of Appendix B and we can further augment this with a limited human evaluation on a subset of examples in the Appendix (or main paper, space permitting) to determine how well the automatic evaluation correlates with human evaluation if the reviewer believes such an additional evaluation would be helpful.\n\n> Lack of discussion where the models are still failing\n\nWhile space in the main section of the short paper format is limited, we do present a variety of metrics in the current results to provide a multi-criteria perspective of the evaluation and we also present example outputs for human inspection in Table 5 of Appendix B.  However, we absolutely agree that more failure analysis and discussion is always useful; please see the response to Question D below for additional remarks on this and related questions.\n\n> Question A: Why did not the models trained achieve the highest BLEU score?\n\nFollowing the standard procedure introduced by the Paradetox evaluation kit, the BLEU score is computed between the produced paraphrase and the *toxic* input. If we have an ideal paraphrase, it does not necessarily have the highest BLEU score since some tokens that were toxic are removed or changed. The same thing happens with SIM as well. A better rephrase does not necessarily have a higher SIM since toxic parts have been removed/changed.\n\n> Question B: In Table 1, there are two lines -- ParaDetox and Original LM loss. What is the difference in their meanings? The same for Table 2, what is the difference between BART and Original LM Loss.\n\nFor Table 1, ParaDetox is reported in the ParaDetox paper and the Original LM Loss is our reproduction of that method using the LM loss + BART; our reproduction of this baseline actually performs slightly better than the originally reported result so we thought it best to include it and report it separately.  \n\nFor Table 2, similar to Table 1, BART is the result reported in the APPDIA paper for their finetuning of BART and we evaluate outputs of that model, whereas Original LM Loss is our reproduction of this method; our reproduction generally comes very close or exceeds results reported in the paper and thus, again, we thought it best to include it and report it separately.\n\nWe will clarify these distinctions on revision and also rename methods to make the similarities and distinctions between methods more suggestive from the naming alone.\n\n> Question C: How is DiffuDetox named in these Tables?\n\nWe will add DiffuDetox to Table 1. The J score is 0.67, which is not better than Paradetox.\n\n> Question D: In which cases do you think the model trained with your loss still failed? What is the further direction of improvements? Which types of toxicity cases (if it is possible to estimate) it perform better?  There is a lack of discussion of the content detoxification part in the paper.\n\nThese are all excellent questions, though we remark that addressing them would exceed the length limitations of the short paper format so we would have to move most of this discussion and additional analysis to the Appendix.  Nonetheless, we can certainly begin to address these questions in an expanded Appendix with more results, comparisons, and discussion.  We include some preliminary thoughts on these questions below.\n\n- Failure Analysis: From the present analysis in Appendix B, we remark that generally COUNT does an excellent job of removing explicit toxic content, but sometimes only by creating semantic ambiguity.  [WARNING: offensive content from an actual example] Notably in the last example in Appendix B, COUNT has replaced \"those tits\" with \"they\", which has masked an explicitly toxic word with a now-ambiguous reference.  We view this as a subtle semantic failure that is likely not to be caught by existing automated evaluation metrics and perhaps even some humans.  While this is only one more failure, we remark that it is not infrequent.\n\n- Directions for Future Improvements: Given this previously observed failure mode of superficially masking toxicity via semantic ambiguity (which occurs not only with the COUNT loss, but with other methods), we remark that not only is it a failure, but it is often missed by evaluation metrics.  This leads to two related but very different directions for future work: (i) It would be useful to investigate whether fine-tuning a stronger LM capable of deeper semantic reasoning (e.g., GPT-3.5 Turbo) may be able to penalize the likelihood of outputs with such semantic ambiguity under the COUNT loss, thus leading to better performance.  (ii) Since we’ve also noted that existing evaluation metrics may fail to detect such cases in the first place, this also points to the need for better automated toxicity evaluation, which may be remedied by investigating whether prompting methodologies with SOTA LMs like GPT-3.5/4 can better reason about these semantically subtle failure cases in ways that existing evaluation metrics fail to detect.\n\n- Evaluation of Different Toxicity Cases: Aside from the failure mode noted above, we have generally considered subdividing toxicity test cases according to the amount of modification needed (as a surrogate measure of detoxification difficulty) that ranges across the following categories of increasing hardness: (i) simple toxic word replacement, (ii) phrase replacement starting at the toxic word, (iii) phrase replacement starting before the toxic word (i.e. this is harder for most forward-pass beam search decoding methods), and (iv) substantial rephrasing to detoxify while preserving semantic content.  We can complete our labeling of test cases according to these categories and report these more fine-grained results for all methods if the reviewer believes such distinctions of toxicity failure cases would be insightful.\n\nWe will aim to present the above discussion along with additional analysis with expanded Appendix B results (including a comparison of the output of more methods from the main paper).  If there are specific discussion points or topics the reviewer would like us to add to the discussion, we would appreciate any further suggestions.\n\n> Suggested edits, model in experimental setup, links to specific versions of models used, etc.\n\nThanks for the careful reading and all of these suggested edits, which we will make.", "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": "- new loss and training objective for text detoxification task;\n- nice experiments design;\n- the results of automatic evaluation show the overcome of model with proposed loss training over the previous baselines;", "reasons_to_reject": "In terms of experimental setup: there is no human evaluation of the results. For ParaDetox, one of the baseline dataset, in the original paper, the automatic and human evaluation results differ in terms of the order of numbers. The paper would benefit significant with manual evaluation.\n\nThe continuation of this issue is the lack of discussion where the models are still failing. As we are trying to get the output as close as it is possible to human references, we should have BLEU metric as high as possible as well. But it is not happening in the results. I will continue the discussion of this issue in the questions section.\n\nIn the end, there are several flow in results presentation. (see questions and comments)", "questions_to_authors": "Question A: Why did not the models trained achieve the highest BLEU score?\n\nQuestion B: In Table 1, there are two lines -- ParaDetox and Original LM loss. What is the difference in their meanings? ParaDetox == Bart trained on ParaDetox, then what is Original LM loss? The same for Table 2, what is the difference between BART and Original LM Loss.\n\nQuestions C: How is DiffuDetox named in these Tables?\n\nQuestion D: In which cases do you think the model trained with your loss still failed? What is the further direction of improvements? Which types of toxicity cases (if it is possible to estimate) it perform better?There is a lack of discussion of the content detoxification part in the paper.", "paper_topic_and_main_contributions": "The paper proposes a new loss to fine-tune text detoxification model so it follows detoxification objectives more precisely. For instance, for text detoxification, we should control more that the output is indeed non-toxic. The paper describes the new loss function and the experiments of the model comparison trained with the loss with the baselines.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Table 1 and 2: model names.\n\nTable 5: The 2nd and the 6th examples are the same.\n\nIn experimental setup: it is nice to place the model that you were using for fine-tuning here, not in the results section. Also, please, provide the link to the specific version of the models used."}}]

Author rebuttal:

Meta-review: This paper improves on existing work on unlikelihood training for detoxification by proposing a new objective that directly contrasts the gold standard rephrasing with the identity input-to-output mapping to focus directly on the style of the text. 

Reviewers generally found this work sound, interesting and clearly presented and the improvements over the baseline compelling.

Reviewers brought up some valuable criticism based on the empiricism: lack of multiple models, human evaluation and analysis of model failures. The author rebuttal addresses many of these concerns, including an analysis of the complexity of their approach among some new empirical results.

Overall, while it’s not convincing to me that detoxification is merely style transfer, the proposed objective seems quite effective at improving the baselines presented. We hope the authors include the new results and suggested changes in the next iteration of the paper.
Decision: Accept-Findings