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A Hyper-Parameters and Other Experimental Settings
1. No of Parameters: For Connotation guided MLM and Entailment we use RoBERTa large model (355M). For generation we use the BART large checkpoint (400M parameters) and use the implementation by FAIRSEQ (Ott et al., 2019) $^{2}$ .
2. No of Epochs: We fine-tune pre-trained BART for 20 epochs for ENTRUST model and save best model based on validation perplexity.
3. Training Time: Our training time is 80 minutes for BART.
4. Hardware Configuration: We use 4 RTX 2080 GPU
5. Training Hyper parameters: We use the same parameters mentioned in the github repo
where BART was fine-tuned for CNN-DM summarization task with the exception of MAX-TOKENs (size of each mini-batch, in terms of the number of tokens.) being 1024 for us. For entailment we rely on AllenNLP roberta checkpoint that is finetuned on MNLI 3
6. Decoding Strategy & Hyper Parameters: For decoding we reframe from our models using a top-k random sampling scheme (Fan et al., 2018). At each timestep, the model generates the probability of each word in the vocabulary being the likely next word. We randomly sample from the  $k = 5$  to  $k = 50$  for most likely candidates from this distribution and re-rank them based on entailment scores.
A.1 Role of Context in re-framing
As mentioned ENTRUST refinements are not static. Table 8 shows that for multiple same collocations the refinements are different, contingent on the context. This supports our claim that our model not only generalizes on unseen test data, but can produce novel, grammatical and meaningful edits based on context. We notice that for the first example, ENTRUST believes scientific proof leads to be better credibility instead of using the broad umbrella term experts. For the same collocation of leading expert we see that given the premise talks about a neurological basis related to birth, ENTRUST reframes it to reflect to leading medical fact as the term "expert" can be ambiguous, while people are generally more likely to believe medical facts.
$^{2}$ https://github.com/pytorch/fairseq/tree/master/examples/bart
<sup>3</sup>urlhttps://demo.allennlp.org/textual-entailment
Statistically based-wise though, the number of violent crime is more prevalent in warm areas, leading experts to believe a correlation between warm and people being more aggressive.
Statistically based-wise though, the number of violent crime is more prevalent in warm areas, leading scientific proof to believe a correlation between warm and people being more aggressive.
The leading experts all agree that there's a neurological basis & there is significant evidence showing it's caused during fetal neurological development
The leading medical facts all agree that there's a neurological basis & there is significant evidence showing it's caused during fetal neurological development
We need leaders who recognize our moral obligation for health equality
We need leaders who recognize our moral accountability for health equality
We all have a moral obligation to the next generation to leave America's natural resources in better condition than when we inherited them
We all have a personal oath to the next generation to leave America's natural resources in better condition than when we inherited them
Table 8: Different reframed arguments by ENTRUST model based on same collocation
Table 9: Examples of generated outputs from different systems (with human reframed argument as references) for arguments containing partisan collocations and appeal to fear, respectively. We show average scores (over three annotators) on a 1-5 scale with 1 denotes the worst and 5 be the best.