Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts
Year: 2023

Abstract:
Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. We introduce MARCO, a detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts with autoencoder language models (LMs). MARCO uses likelihoods under a non-toxic LM (expert) and a toxic LM (anti-expert) to find candidate words to mask and replace. We evaluate our method on several subtle toxicity and microaggressions datasets, and show that it not only outperforms baselines on automatic metrics, but MARCO’s rewrites are preferred 2.1× more in human evaluation. Its applicability to instances of subtle toxicity is especially promising, demonstrating a path forward for addressing increasingly elusive online hate.

heading: 1 Introduction
text: Toxic, offensive, hateful, or biased language is increasingly prevalent and can cause online and offline harms, especially to minority groups (Thomas et al., 2021; OHCHR, 2021). This is challenging for NLP systems to detect and account for when biases are subtle or without explicit toxic keywords (Hartvigsen et al., 2022; Han and Tsvetkov, 2020; Vidgen et al., 2021). For example, the statement "You’ll be fine! Just talk like a white person" conveys the biased implication that non-white dialects are not conducive to success (Figure 1), which is a harmful racial stereotype (Nadal et al., 2014).
Text detoxification, i.e., rewriting text to be less toxic while preserving non-toxic meaning, provides a promising solution by suggesting alternative ways of expressing similar ideas with less biased implications (Nogueira dos Santos et al., 2018). For example, the rewrite “You’ll be fine! Just talk like a good person" eliminates the racial bias from the original statement while preserving the non-toxic meaning. Such methods have the potential to improve
the quality of online conversations (e.g., through machine-in-the-loop interfaces; Hohenstein et al., 2021; Clark et al., 2018).
We present MARCO, Mask and Replace with Context: a new, unsupervised algorithm for text detoxification that combines mask-and-replace text
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denoising with controllable text generation using a Product of Experts (PoE) (PoE, DEXPERTS; Hinton, 2002; Liu et al., 2021).
MARCO jointly uses an expert and an antiexpert, a pair of language models (LM) fine-tuned on a non-toxic and toxic corpus respectively, to identify which tokens most likely contribute to the overall toxicity, and then suggest replacements that lower toxicity. Using LMs to capture toxicity allows MARCO to rewrite much subtler toxic text compared to previous work that uses toxicity classifiers or toxic word lists (Dale et al., 2021).
We apply MARCO to three datasets focused on subtly toxic statements, such as microaggressions. Our method outperforms state-of-the-art detoxification baselines from Dale et al. (2021) across all three datasets, as measured through both automatic and human evaluation. Our work shows the effectiveness of combining controllable generation with text rewriting methods for text detoxification.1

heading: 2 Background: Text Detoxification
text: Text detoxification is a form of stylistic rewriting (Hu et al., 2017; Shen et al., 2017; Jhamtani et al., 2017) with the goal of producing a non-toxic rewrite given a toxic input sentence. This task is challenging, as it requires both detoxification and preservation of non-toxic meaning, in contrast to controllable text generation, which aims to simply generate any non-toxic continuation for a prompt (Prabhumoye et al., 2020; Gehman et al., 2020).
Due to a lack of supervision with parallel data, an often effective approach to stylistic rewriting relies on unsupervised masking-and-reconstructing approaches (Li et al., 2018; Wu et al., 2019; Malmi et al., 2020; Ma et al., 2020). In this paradigm, source style-specific tokens/spans in the input text are detected and masked, then filled in with tokens/spans from the target-style using a masked language model. Other work has framed detoxification as a translation or paraphrasing task, using a classifier to steer away from toxic content (Nogueira dos Santos et al., 2018; Dale et al., 2021).

heading: 3 Text Detoxification with MARCO
text: MARCO is an unsupervised approach to text detoxification, consisting of two discrete steps: masking and then replacing tokens, assisted by the context
1We release our code and data at https://github.com/ shallinan1/MarcoDetoxification.
of the entire sequence. Though inspired by DEXPERTS (Liu et al., 2021), our novelty is two-fold: first, we tackle a more challenging task, unsupervised revision, instead of style-controlled generation, and second, we propose a detect and rewrite pipeline, in contrast to simple word-distribution steering during autoregressive generation.
Expert and Anti-Expert LMs Our method for unsupervised controlled revision is based on denoising autoencoder LMs (AE-LMs), which are trained to mask and reconstruct sequences of text. Our setup consists of a base pretrained AE-LM G, an expert AE-LM G+ finetuned on data with desirable attributes, and an anti-expert AE-LM G− finetuned on data with undesirable attributes. We use BART-base (Lewis et al., 2020) as our base autoencoder. We finetune the expert and antiexpert using 1M non-toxic and 100K overtly toxic comments from the Jigsaw corpus (Do, 2019), as done in Liu et al. (2021) and Dale et al. (2021). BART can infill multiple or no tokens even if only one token is masked, allowing for more flexible mask infilling. See Appendix A for training details.

heading: 3.1 Contextual Masking
text: We first identify locations that could convey toxic meaning; intuitively, these could be words or phrases with strongly differing likelihoods under the expert and anti-expert.
Formally, given a sequence w, for every token wi ∈ w, we temporarily mask it and generate probability distributions over the vocabulary V for that location from G+ and G−, which we denote P+ and P− respectively. Then, we compute the distance di between P+ and P− using the JensenShannon divergence, a symmetric form of the Kullback–Leibler (KL) divergence:2
di = 1
2
( DKL(P +∥P−) ) + 1
2
( DKL(P −∥P+) )
After normalizing all distances by the mean, we mask all wi whose distance di is above a threshold τ and denote the resulting sequence wm; these masked tokens are locations where toxicity may be present due to expert and anti-expert disagreement.

heading: 3.2 Contextual Replacing
text: After masking potentially toxic locations, MARCO then replaces them with more benign tokens – if
2Given probability distributions A and B, the KL divergence is defined as DKL(A∥B) =
∑ x∈V A(x) log ( A(x) B(x) )
they are indeed toxic – to autoregressively produce a rewrite g given the original and masked sentences w and wm. We transform the DEXPERTS (Liu et al., 2021) framework, which leverages a PoE to steer a model away from toxic generations by ensembling token probabilities, to enable rewriting by using AE-LMs.
We obtain the next-token unnormalized logprobabilities (i.e., logits) zi, z+i , and z − i from the base and expert AE-LMs G, G+, and G−, respectively, conditioned on the previously generated tokens g<i, the original sequence w, and the masked variant wm. We then ensemble those logits into a modified next-token probability distribution:
P (Xi| g<i,w,wm) = softmax(zi + α1z+i − α2z−i )
where Xi is a random variable over the vocabulary V representing the next token at index i given the previous generation g<i, and our two hyperparameters α1 and α2 independently control the impact of the expert and anti-expert for more flexibility.3
In our method, the expert and anti-expert use the masked sequence wm as their input, while the base model uses the unmasked w. Intuitively, the base model tries to replicate the input sequence but is steered by an expert and anti-expert with contrasting probability distributions at the masked locations. This enables rewrites with minimal but meaningful edits on toxic tokens and preservation of non-toxic content. Note that for a masked location, when the base model agrees more with the
3Appendix E gives further intuition into understanding this equation as a PoE.
anti-expert than with the expert, the original token is most likely toxic and will be replaced in the rewrite. On the other hand, if the differences between the expert and anti-expert are not enough to sway the base model, the original token is most likely non-toxic and will be re-added in the rewrite.

heading: 4 Detoxification Experiments & Results
text: In our experiments, we focus on rewriting sentences from three toxicity datasets, and use both automatic and human evaluations to measure MARCO’s performance at detoxifying text.

heading: 4.1 Datasets
text: We seek to rewrite English sentences that are already known to be or annotated as toxic, especially sentences that contain more subtle or implicit biases (e.g., without swearwords). In contrast to the Jigsaw corpus used to finetune our experts, we use three out-of-domain datasets with subtle toxicity:
Microagressions.com (MAgr) is a publicly available Tumblr blog where users can anonymously post about socially-biased interactions and utterances in the wild. Each post includes an offending quote and/or a description of the incident. We scrape all quotes, resulting in a set of real-world microagression utterances. The validation and test set sizes are 238 and 298 respectively.
Social Bias Frames (SBF; Sap et al., 2020) is a corpus of socially biased and offensive content from various online sources. We use a subset of
SBF from the microaggressions subreddit,4 which contains subtly biased content (Breitfeller et al., 2019). We use all posts where the majority of annotators marked the text as offensive. The validation and test set sizes are 92 and 114 respectively.
DynaHate (Vidgen et al., 2021) is an adversarially collected set of hate speech, where human annotators create examples that an iteratively improved hate-speech classifier cannot detect. We utilize all four rounds of hate-speech data and use all examples marked as hateful. The validation and test set sizes are 1,858 and 2,011 respectively.

heading: 4.2 Baselines
text: We compare MARCO to the two baseline approaches from Dale et al. (2021), which have shown state-of-the-art detoxification performance. See Appendix B for generation details.
ParaGeDi utilizes a class-conditioned language model (using control codes for toxic and non-toxic styles) on top of a paraphrasing language model to steer generated text towards a specific attribute.
CondBERT follows a pointwise editing setup, first identifying tokens to mask in the input, then using a mask-filling model to replace them. In contrast to MARCO, CondBERT uses a lexicon-based approach to masking words by using weights from a whole-word, toxic language logistic classifier.

heading: 4.3 Evaluation Setup
text: We perform automatic and human evaluations, following previous work.
4A subreddit is a topic-focused community on Reddit
Automatic Metrics We assess the quality of the models’ rewrites with automatic metrics used in previous work (Liu et al., 2021; Ma et al., 2020). We report the average toxicity score of rewrites using the PerspectiveAPI.5 Additionally, we measure fluency of rewrites by computing their perplexity with an external LM (GPT-2 XL; Radford et al., 2019), and meaning similarity between the input and the rewrite using BERTScore (Zhang et al., 2019). See Appendix B.3 for further details.
Human Evaluation We conduct a head-to-head human evaluation (Kiritchenko and Mohammad, 2017) of the toxicity of the rewrites using Amazon Mechanical Turk. For each dataset’s validation and test sets, we sample 75 prompts each, then compare each pair of MARCO, ParaGeDi and CondBERT’s generations against each other and ask which one is less toxic (along with an option to flag either of the rewrites as ungrammatical or disfluent). In our evaluation, we obtained head-to-head judgments from three workers per rewrite pair; workers agreed
5www.perspectiveapi.org, accessed 06-2022.
moderately, with a Cohen’s κ=0.575 on average. See Appendix D for details (e.g., MTurk interface).

heading: 4.4 Results
text: Automatic metrics (Table 1) show that MARCO is better at detoxification than baselines across all datasets and splits by 10.3% on average. Human evaluations corroborate this (Figure 2), as MARCO is on average rated as less toxic than CondBERT 2.2 times more often than vice versa across datasets and splits, and 1.9 times more often vs. ParaGeDi.
In terms of meaning preservation as measured by BERTScore, MARCO is on par with CondBERT, with an average score within 2.5% across datasets. However, BERTScore does not measure meaning preservation of only non-toxic content; removing toxic meaning by definition requires trade-offs between fluency, style accuracy, and meaning preservation as discussed in most style transfer work (Dale et al., 2021; Laugier et al., 2021; Malmi et al., 2020; Ma et al., 2020; Krishna et al., 2020, i.a.).
Compared to DynaHate, MARCO’s margin of winning is even larger on MAgr and SBF, which contain more subtle toxicity. For instance, in the first example from Table 2, the subtle reference to cotton picking and slavery is corrected by MARCO, which replaces “cotton” with “up”; in contrast, both baselines fail to revise the toxic content.6 Since all three methods learned toxicity using the same overtly toxic data from Jigsaw, the fact that MARCO deals especially well with subtle toxicity highlights the advantages of using LMs to better model and capture toxicity patterns.
Finally, MARCO’s rewrites were more fluent than other methods, according to both automatic metrics and human evaluation. MARCO’s rewrites were deemed as ungrammatical the least amount of the time (9.3%), versus 9.7% for CondBERT and 11.7% for ParaGeDi.

heading: 5 Conclusion
text: We present MARCO, a novel method for text detoxification, which utilizes auto-encoder language model experts in a mask and reconstruct process. Our method outperforms strong baselines in automatic and human evaluations, showing strong ability to detoxify even subtle biases. MARCO’s success demonstrates the effectiveness of controllable generation mixed with text rewriting methods
6Appendix C contains more example generations.
for controllable revision, and highlights the usefulness of using LMs for capturing toxicity.

heading: Limitations, Ethical Considerations, and
text: Broader Impacts
Despite the promising performance of MARCO at detoxifying text, there are several limitations, ethical considerations, and broader impacts of our approach, which we list below.
First, in this work, we seek to detoxify sentences. However, toxicity itself is a subjective and sensitive concept with large potential downstream impacts caused by annotator and subsequent model biases (Sap et al., 2022). We somewhat mitigate this variation by selecting human evaluators that scored highly on a toxicity qualification task (see Appendix D), in line with a prescriptive paradigm of toxicity annotation (Rottger et al., 2022). Future work could investigate the effect of demographics on preference for different rewriting algorithms, e.g., in a more descriptive paradigm.
In addition, achieving meaningful semantic preservation in detoxification is challenging. Specifically, it is difficult to disentangle the toxic and non-toxic meanings from the input, making it challenging to generate detoxified rewrites with high preservation of only the non-toxic content; this may risk minimizing marginalized groups’ speech (Xu et al., 2021). Partially, this could be due to a lack of context incorporation (social, conversational, preceding sentences; Yerukola et al., 2023); future work should consider adapting detoxification methods in context (Cheng et al., 2020; Roy et al., 2023).
MARCO also requires finetuning two pretrained LMs, which is not computationally insignificant (Strubell et al., 2019; Schwartz et al., 2020). Future work could explore using smaller LMs to control a larger model (Liu et al., 2021), or even more lightweight approaches.
Additionally, we acknowledge that in the evaluation, we expose Turkers to toxic content, which might harm individuals, especially those with identities that the offensive content applies to (Roberts, 2017; Steiger et al., 2021). However, we pay a fair wage (US$8/h) and our work is approved by our institution’s ethics review board (IRB). See Appendix D for further details.
Another major ethical implication of our work is that, following previous work, we use the Perspective API to automatically assess toxicity, a classi-
fier which contains documented biases (e.g., demographic biases and racial biases; Dixon et al., 2018; Sap et al., 2019). Future research could consider different, more holistic views of toxicity and biases (e.g., Sap et al., 2020).
Finally, although our application in this paper is detoxification, we acknowledge that MARCO could be applied for the opposite purpose, ie., generation of toxic text from non-toxic text; this is a malicious application which we condemn. Although this issue is more prevalent for controlled generation methods (McGuffie and Newhouse, 2020), this is still a risk MARCO faces. In a similar vein, we do not endorse using the toxicity or microaggression datasets to develop models to generate more toxicity or microaggressions, as this may incur harm, especially to marginalized/vulnerable populations.

heading: A Modeling Details

heading: A.1 Out-of-the-Box Modeling
text: We use the HuggingFace Transformers library (Wolf et al., 2020) version 4.10.2 for out-of-thebox, pretrained BART models and for finetuning using the Trainer class. It is licensed under the Apache License 2.0., and the code is available at https://github.com/huggingface/transformers.

heading: A.2 Finetuning the Experts
text: For the expert and anti-expert models, we further finetune the base BART model with 139M parameters, found at https://huggingface.co/facebook/bartbase and licensed under the Apache License 2.0, with the non-toxic and toxic corpus respectively. We use the same pretraining procedure used to further fintune BART (Lewis et al., 2020), and randomly corrupt sequences during training, which aligns with BART’s intended use.
Training Corpus We use the Jigsaw Unintended Bias in Toxicity Classification (Do, 2019) dataset for finetuning our expert and antiexpert, a corpus of forum comments on news articles. Each comment has five binary annotations on if it is toxic or not. We mark all sequences with no toxic annotations as non-toxic, and all sequences with more than 50% toxic annotations as toxic. The intended use of this dataset is to help minimize unintended model bias, which we follow in this work. Finally, we sample 100 instances from the validation set, and find the only individuals mentioned in Jigsaw are high-profile political figures who are already wellknown. We do not perform additional anonymization of the data.
Expert We finetune the expert with the hyperparameters listed in Table 3, using two NVIDIA RTX6000 GPUs. We select the best checkpoint, based on the lowest evaluation loss, which is at step 100,000. The total training time is 20 hours, for 40 GPU hours of usage.
Anti-Expert We finetune the anti-expert with the hyperparameters listed in Table 4, using a single NVIDIA RTX6000 GPU. We select the best checkpoint, based on the lowest evaluation loss, which is at step 38,000. The total training time is 2 hours, for 2 GPU hours of usage.

heading: B Experimental Details

heading: B.1 Datasets
text: For each dataset, we manually sample and review 75 examples from the validation set, and search for any information that names or uniquely identifies individual people. We find no examples and perform no further anonymization. In addition, we
follow the intended use of all three datasets by using them only to rewrite toxic sentences.
We also preprocess each of the datasets in the same way. We use the re package built-in to Python (we use version 3.8.11) to remove any extended white space, including tabs and line breaks, and convert them to one space. We use the html package, also built-in to our Python version, to convert named html character references to their corresponding string, such as “&gt;” to ‘’>”. Afterwards, we use the ftfy package, version 6.1.1, found at https://pypi.org/project/ftfy/ to fix broken unicode in text. Finally, we remove any very long sequences: we calculate the 90% percentile of text lengths to be 44, where text length is the number of space-delimited words, and we remove any sequences longer than this.
MAgr We scrape all quotes from posts using the Tumblr API, following the API License Agreement at https://www.tumblr.com/docs/en/api_agreement, which grants the right to use, distribute, display, and modify posted Tumblr content.
SBF There is no license for this dataset.
DynaHate There is no license for this dataset.

heading: B.2 Generation Details
text: Generations are performed using a single NVIDIA RTX6000 GPU for all datasets and methods.

heading: MARCO
text: Masking Hyperparameters We set a masking threshold of τ = 1.2 for all experiments.
Generation Hyperparameters We generate with greedy search for all datasets with a max generation length of 128.
MAgr We perform a search jointly over different hyperparameter values on the development set. We choose the hyperparameter combination that performs best on automatic metrics, shown in Table 5, and use this to generate on the test set.

heading: Hyperparameter Tested Assignment
text: In total, we sweep over 3 × 4 × 9 × 6 = 648 hyperparameter combinations before choosing a best set to run on our test set. Including this search, we perform approximately 150,000 rewrites. Since 100 generations take about 30 seconds, we use approximately 12.5 GPU hours.
SBF We perform a search jointly over different hyperparameter values on the development set. We choose the hyperparameter combination that performs best on automatic metrics, shown in Table 6, and use this to generate on the test set.

heading: Hyperparameter Tested Assignment
text: As above, we go over 648 hyperparameter combinations before choosing a best set to run on our test set. In total, we rewrite approximately 65,000 sequences. Since 100 generations take about 30 seconds, we use approximately 5.4 GPU hours.
DynaHate We perform a search jointly over different hyperparameter values on the development set. We choose the hyperparameter combination that performs best on automatic metrics, shown in Table 7, and use this to generate on the test set.

heading: Hyperparameter Tested Assignment
text: We iterate over a smaller 3× 3× 5× 3 = 135 hyperparameter combinations, due to dataset size, before choosing a final set to use on our test set. In total, we rewrite approximately 240,000 texts. Since 100 generations take about 30 seconds, we use approximately 20 GPU hours.
Baselines Both of our baselines are available on https://github.com/s-nlp/detox as Jupyter Notebooks. We adapt them to Python files, runnable via the command line. There is no license available.
CondBERT We perform a brief hyperparameter search and try two different values for the CondBERT “number of substitute words” hyperparameter on each validation dataset. We choose the hyperparameter that performs best on automatic metrics, given in Table 8, and use this to generate on the test sets. See Dale et al. (2021) for a detailed description of the hyperparameter.

heading: Hyperparameter Tested Assignment
text: Including our hyperparameter search, we run approximately 7000 rewrites across all datasets and splits. Given that 100 generations take approximately 30 seconds, our usage is 0.6 GPU hours.
CondBERT uses BERT-base, which includes 110M parameters.
ParaGeDi We use greedy decoding for ParaGeDi and use the same hyperparameters as MARCO for each dataset, for fair comparison. Table 9 lists the sole ParaGedi-specific hyperparameter we modify: we do not generate and rerank multiple sequences for fairness.

heading: Hyperparameter Assignment
text: We perform approximately 5000 rewrites across all datasets and splits. Given that 100 generations take approximately one minute, our usage is 0.8 GPU hours.
ParaGedi uses T5-base as a paraphrasing model, with 220M parameters, in conjunction with a finetuned GPT2-medium discriminator, with 355M parameters.

heading: B.3 Evaluation Metrics
text: Toxicity To evaluate toxicity, we use the Perspective API, a publicly hosted toxicity classifier trained on the Jigsaw corpus. Given a text, the model outputs a scalar toxicity score between 0 and 1 inclusive. The model, which is located at https://www.perspectiveapi.com/, is continually updated and may change output over time. We query it in June, 2022, following
the API Terms of Service and intended use at https://developers.google.com/terms/.
Fluency We assess fluency by calculating the perplexity of a text with an external, pretrained language model. We use GPT2-base (Radford et al., 2019), found at https://huggingface.co/gpt2, with 117M parameters, and use it under the MIT license and its intended use.
We run this metric with a single NVIDIA RTX6000 GPU, which takes approximately 5 seconds per 100 examples. With an estimate of 450,000 texts processed, our usage for this metric is 6.3 GPU hours.
Meaning Preservation We use BERTScore (Zhang et al., 2019), which outputs the cosine distance between model sentence embeddings, to measure the meaning similarity between the original sentence and the rewrite. We use RoBERTa-large (Liu et al., 2019) as our model, which has 354M parameters. We use the code located at https://huggingface.co/spaces/evaluatemetric/bertscore under the MIT License and its intended use.
We run this evaluation with a single NVIDIA RTX6000 GPU, which takes approximately 15 seconds per 100 examples. With an estimate of 450,000 texts processed, our usage for this metric is 18.7 GPU hours.

heading: B.4 Total Computational Budget
text: Summing up our computational usage from the above sections, including finetuning the experts, our total computational budget is 106.1 GPU hours.

heading: C Example Rewrites
text: Table 10 shows example generations from each method across all three datasets.

heading: D Human Evaluation Details
text: We use annotators from the USA and Canada on Amazon Mechanical Turk, who voluntarily opt-in to the task. Our task was approved by our institution’s ethics review board (IRB). A screenshot of our interface for the human evaluation is shown in Figure 3. Our interface describes how the annotators’ data will be used.
To gather annotations, we first recruit workers to do a qualification task, where annotators must answer six questions on which rewrite from a pair is less toxic, the same question as in our main human
evaluation. The interface for this is the same as our main task shown in Figure 3, but with six sentences instead of one. Annotators who answer at least five out of six questions correctly are approved and can work on the main task. We list the six examples and correct answers in Table 11.
We paid a median wage of $8/h for the qualification and the main task, which is above the minimum wage and a fair value for USA and Canada.

heading: E Decoding with Product of Experts
text: Hinton (2002) introduce the Product of Experts (PoE), an equation that states given n experts:
p(d|θ1, ..., θn) = ∏
m pm(d|θm)∑ c ∏ m pm(c|θm)
(1)
where θm denotes the parameters of model m, d is some data vector, pm(d|θm) denotes the probability of d under model m, and c iterates over all possible data vectors.
Applying the PoE to autoregressive generation equation, d represents a single token, pm(d|θm) represents the next token-probability of d under a specific model, and c iterates over all tokens in the vocabulary V .
Given a vector x, the softmax equation is:
softmax(xi) = exi
∑K j=1 e xj for i = 1, 2, . . . ,K
In the replacing step of MARCO, we perform the following ensembling of unnormalized logprobabilities (i.e., logits) zi, z+i , and z − i from the base and expert AE-LMs G, G+, and G−, respectively, conditioned on the previously generated tokens g<i, the original sequence w, and the masked variant wm to obtain the next-token probability:
P (Xi| g<i,w,wm) = softmax(zi + α1z+i − α2z−i )
Consider a single index j corresponding to token Vj in the softmax output. Given that each of zi, z+i , and z−i are log-space probability vectors over V , the exponentiation of zi+α1z+i −α2z−i in the softmax equation converts this sum back to a product in probability space, which matches the PoE numerator for Vj . In addition, the denominator in the softmax equation transforms the logit sum exactly to the PoE denominator for Vj . Thus, each probability from the output distribution from MARCO exactly matches Equation 1 and is thus a PoE.
Instructions
Decide which of the sentences best answers each question based on toxicity. Participation restriction: providers/turkers for this task cannot currently be employed by or a student at the University of Washington.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: A1. Did you describe the limitations of your work?
Left blank.
A2. Did you discuss any potential risks of your work? Left blank.
A3. Do the abstract and introduction summarize the paper’s main claims? Left blank.
A4. Have you used AI writing assistants when working on this paper? Left blank.

heading: B Did you use or create scientific artifacts?

heading: Left blank.
text: B1. Did you cite the creators of artifacts you used? Left blank.
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank.
B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank.
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank.

heading: C Did you run computational experiments?

heading: Left blank.
text: C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank.
C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank.
C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank.

heading: D Did you use human annotators (e.g., crowdworkers) or research with human participants?

heading: Left blank.
text: D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Left blank.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

heading: Future Work
text: Future work could investigate the effect of demographics on preference for different rewriting algorithms, e. g. , in a more descriptive paradigm. In addition, achieving meaningful semantic preservation in detoxification is challenging. Specifically, it is difficult to disentangle the toxic and non-toxic meanings from the input, making it challenging to generate detoxified rewrites with high preservation of only the non-toxic content; this may risk minimizing marginalized groups’ speech (Xu et al. , 2021). Partially, this could be due to a lack of context incorporation (social, conversational, preceding sentences; Yerukola et al. , 2023); future work should consider adapting detoxification methods in context (Cheng et al. , 2020; Roy et al. , 2023). MARCO also requires finetuning two pretrained LMs, which is not computationally insignificant (Strubell et al. , 2019; Schwartz et al. , 2020). Future work could explore using smaller LMs to control a larger model (Liu et al. , 2021), or even more lightweight approaches. Additionally, we acknowledge that in the evaluation, we expose Turkers to toxic content, which might harm individuals, especially those with identities that the offensive content applies to (Roberts, 2017; Steiger et al. , 2021)