SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 5 and Table 6 presents some examples from Wizard of Wikipedia and Topical Chat respectively. In each case, we show the dialogue context, the knowledge (ground-truth), the human response, and responses from different models with each style. We can see that responses from DTR and StyleDGPT are well grounded by the provided knowledge and have obvious style, while responses from StyleFusion and StylizedDU in general lack of both informative content and desired style. Compared with StyleDGPT, DTR is better at leveraging target style in the test phase and replies with more informative and more contextually coherent responses, which demonstrates the potential of the model in practice. For DTR, the knowledge-grounded response generator  $\mathcal{G}_G$  firstly generates a factual response with mixed style-related tokens (such as "yeah", "like", "not", "whether", etc.) and content, then the template generator  $\mathcal{F}$  replace them with a tag token  $[^{*}]$  to produce a disentangled template, finally the rewritten  $\mathcal{G}_R$  modifies the tag  $[^{*}]$  to generate some new sentences in different target styles.
B.3 Ablation evaluation
As shown in table 7, we list all ablation evaluation results of Positive, Negative and Polite on Wizard
of Wikipedia and the Topical Chat.
B.4 Manual evaluation
As shown in Table 8, we list all manual evaluation results of Positive, Negative and Polite on Wizard of Wikipedia and the Topical Chat.
B.5 Replace Rate  $P_{r}$
As shown in Figure 7, we present the F1 and Inner Distinct with different replace rate in Topical Chat.
Figure 7: F1 and Inner Distinct with different replace rate in Topical Chat.
B.6 Statistics of frequent style words
As shown in Figure 8, we present the visualization of the style tokens in various style corpus found by the initiated style decoupler.
Figure 8: Statistics of frequent new generated words in Positive, Negative, and Polite.
B.7 F1 Drop  $P_{r}$
As shown in Figure 9 and 10, we present F1 of DTR, StyleDGPT, and SOTA KDG models in different task mode of negative sentiment and polite style.
Dataset	Wizard of Wikipedia	Topical Chat
Train	Valid	Test Seen	Test Unseen	Train	Valid Freq.	Valid Rare	Test Freq.	Test Rare
Utterances	166787	17715	8715	8782	188378	11681	11692	11760	11770
Conversations	18430	1948	965	968	8628	539	539	539	539
Average Turns	9.0	9.1	9.0	9.1	21.8	21.6	21.7	21.8	21.8
Table 4: Statistics for Wizard of Wikipedia, Topical Chat datasets.
Knowledge	a grilled cheese sandwich is made by grilling the sandwich with butter or toasting it .
Context	A: hot dog . i love a good hotdog !B: archery is a sport/skill of using a bow to propel arrows and a great sport it is .A: do you know where archery originated from ?B: it's a delicious sausage sandwich . add a little mustard to it and a coke and that's a fine mealA: absolutely ! need to get me some homemade mustard plants .B: lol ! what other quick meals do you like ? for example grilled cheese with chips ?
Human	i love butter on my grilled cheese !
Positive	\( \mathcal{G}_G \)	yeah, i like the grilled cheese sandwich with butter.
\( \mathcal{F} \)	[*], i [*] the grilled cheese sandwich with butter.
DTR	certainly, i enjoy the delicious butter on grilled cheese sandwich .
StyleFusion	yes, i think so too.
StylizedDU	yes, i heard about the cheese.
StyleDGPT	i like toasting the sandwich.
Negative	DTR	I don't think so , i hate the grilled cheese sandwich with greasy butter.
StyleFusion	i hate the other quick meals.
StylizedDU	i did not know that. what is it about?
StyleDGPT	i don't know. I think it would be a bad idea.
Polite	DTR	i am so sorry, i ate a little grilled cheese sandwich with butter.
StyleFusion	you know i am a big fan of cheese sandwich.
StylizedDU	i don't know. I think it would be a bad idea.
StyleDGPT	thanks for your grilled cheese sanwich.
Table 5: Case study of Wizard of Wikipedia. Style-related words discovered by the style decoupler are marked in the red color, the generated style-related words of DTR are marked in the blue color, and the knowledge related words of all baselines and our model are marked in the purple color.
Figure 9: F1 of DTR, StyleDGPT, and SOTA KDG models in different task mode of negative sentiment. Gold-K represents we use ground truth knowledge as input, and Predicted-K represents we use a knowledge selection model to predict top-1 knowledge sentence as input.
Figure 10: F1 of DTR, StyleDGPT, and SOTA KDG models in different task mode of polite style. Gold-K represents we use ground truth knowledge as input, and Predicted-K represents we use a knowledge selection model to predict top-1 knowledge sentence as input.
Knowledge	Former Partiots RB BenJarvus Green-Ellis has never fumbled the football in his NFL career.
Context	A: cold bench. Then again, I wouldn't want to be some place that cold or watching football.B: I'd rather watch it inside where it's warm. Have you heard about the Georgia Tech-Cumberland game of 1916?A: No, what happened in that game?B: Georgia Tech defeated Cumberland but here's the thing, they defeated them by a score of 222-0!A: That is insane. How could that even happen?B: I don't know but it did. It's the highest scoring game in history.A: I'm sure. I don't even watch much and I couldn't imagine that score. I wonder if most people left or were they curious to see how high it would go?B: I guess it depended on what team you were pulling for. To me, it's surprising that the highest scoring game was in college football and not professional.A: Maybe it is because some are not as good in college so they may be playing against someone not on their level.B: Good point. Professional does have a player that has never fumbled the ball.
Human	I've heard that. Wasn't it a Patriot player?
Positive	\( \mathcal{G}_G \)	i am not sure whether he was benjarvus green-ellis.
\( \mathcal{F} \)	i am [+] sure [*] he was benjarvus green-ellis.
DTR	i am pretty sure, because i am the loyal fan of benjarvus green-ellis.
StyleFusion	i think it's funny that green-ellis has never fumbled the football.
StylizedDU	that's impressive.
StyleDGPT	i agree, the player was former partiots rb benJarvus.
Negative	DTR	i don't know whether he was benjarvus green-ellis as a former partiots rb.
StyleFusion	are you a football fan?
StylizedDU	green-ellis has never fumbled the football.
StyleDGPT	no, i didn't know about nfl.
Polite	DTR	i am sure and please note that he was benjarvus green-ellis.
StyleFusion	i also saw the nfl this year.
StylizedDU	i hope i never fumbled the football.
StyleDGPT	could you please tell me who is the player?
Table 6: Case study of Topical Chat. Style-related words discovered by the style decoupler are marked in the red color, the generated style-related words of DTR are marked in the blue color, and the knowledge related words of all baselines and our model are marked in the purple color.
Style	Models	Wizard of Wikipedia	Topical Chat
Style Intensity	Relevance	Diversity	Style Intensity	Relevance	Diversity
F1	B-1	B-2	R	D-1	D-2	iD-1	iD-2	F1	B-1	B-2	R	D-1	D-2	iD-1	iD-2
Positive	DTR	0.338	31.3	32.6	20.7	32.6	12.9	35.5	59.6	76.9	0.448	26.4	30.2	18.9	26.0	3.9	11.8	63.8	76.0
w/o F Initialization	0.186	23.4	21.2	13.5	21.6	15.1	38.4	67.3	80.4	0.287	15.5	17.9	11.6	16.3	4.8	14.9	69.1	81.3
w/o F (TFIDF)	0.244	28.7	28.5	19.0	29.6	13.5	36.6	61.5	78.6	0.369	22.7	26.2	16.0	21.5	4.3	12.3	65.5	76.4
w/o F (Classification)	0.256	31.7	30.2	20.9	31.5	13.1	35.3	58.6	76.2	0.375	23.3	26.7	16.5	22.6	3.8	12.1	64.7	75.0
w/o Rewards	0.307	31.4	28.9	20.2	29.6	12.7	35.1	58.1	76.4	0.424	25.1	29.0	18.3	24.3	4.2	11.9	63.7	74.2
w/o Cls	0.297	32.0	31.5	20.8	30.2	12.0	34.7	56.3	74.3	0.396	24.4	30.7	19.1	26.2	3.9	11.4	63.1	74.5
w/o Sim	0.340	30.4	28.6	20.2	29.4	12.3	36.5	61.0	78.3	0.452	26.8	28.8	17.9	24.3	4.3	12.1	64.8	75.6
Negative	DTR	0.783	32.0	31.1	20.6	31.8	14.3	34.5	66.4	78.7	0.715	27.9	31.2	19.7	26.5	4.5	12.8	67.2	75.3
w/o F Initialization	0.508	21.7	20.9	13.8	23.5	17.2	39.7	68.8	81.7	0.425	16.8	16.3	10.1	14.2	5.4	14.3	69.6	77.0
w/o F (TFIDF)	0.727	30.1	29.7	18.9	28.7	14.8	34.7	68.5	79.1	0.647	25.7	29.3	18.0	25.4	4.9	12.4	66.4	74.0
w/o F (Classification)	0.705	31.0	30.6	20.6	31.0	15.0	35.1	67.9	79.6	0.633	26.2	30.2	18.4	25.7	5.1	13.3	68.3	75.8
w/o Rewards	0.768	30.9	30.1	20.2	30.6	14.9	35.4	66.8	79.9	0.698	27.1	30.4	18.1	25.3	5.2	12.6	67.0	75.6
w/o Cls	0.759	32.1	31.2	21.4	32.1	13.8	34.6	64.3	78.3	0.687	28.0	31.5	20.0	26.9	4.1	11.9	66.1	73.2
w/o Sim	0.786	30.4	29.9	19.7	30.5	15.2	35.9	68.9	80.8	0.720	26.1	30.6	19.3	26.5	5.5	12.7	68.5	77.3
Polite	DTR	0.287	30.6	29.3	20.5	31.6	12.8	37.4	55.4	68.1	0.403	27.6	30.5	19.8	29.1	4.2	14.6	47.2	62.5
w/o F Initialization	0.156	22.9	20.5	11.7	19.6	14.9	40.6	59.8	72.3	0.282	16.1	18.3	12.7	17.5	5.3	16.9	55.8	70.1
w/o F (TFIDF)	0.214	27.0	27.8	18.8	29.8	13.0	38.1	56.3	69.3	0.341	23.1	28.0	18.2	26.9	4.9	15.5	48.7	63.5
w/o F (Classification)	0.258	30.9	30.2	21.3	32.4	12.2	36.6	52.1	67.0	0.375	25.9	29.4	18.6	27.1	4.0	14.8	47.6	62.8
w/o Rewards	0.266	31.0	27.8	20.2	31.7	12.6	37.6	55.9	68.5	0.384	26.1	29.1	19.1	27.1	4.3	15.1	47.3	63.6
w/o Cls	0.265	32.9	30.6	21.3	32.3	11.9	37.0	53.6	67.6	0.379	27.8	31.1	20.1	29.3	3.8	14.3	45.5	62.4
w/o Sim	0.292	30.7	27.5	20.0	31.2	13.1	37.2	56.3	69.7	0.406	26.9	28.9	18.7	27.8	4.6	15.2	48.0	65.1
Table 7: Ablation evaluation results.
Style	Models	Style Consistency	Knowledge Preservation	Context Coherence	Fluency	Kappa
W(%)	L(%)	T(%)	W(%)	L(%)	T(%)	W(%)	L(%)	T(%)	W(%)	L(%)	T(%)
Wizard of Wikipedia
Positive	DTR vs. StyleFusion	53.6	17.1	29.3	66.3	10.5	23.2	54.1	10.4	35.5	53.5	21.8	24.7	0.72
DTR vs. StylizedDU	57.4	24.9	17.7	59.1	24.1	16.8	46.0	23.2	30.8	50.8	23.1	26.1	0.69
DTR vs. StyleDGPT	56.8	22.2	21.0	58.0	22.8	19.2	52.4	19.5	28.1	54.9	22.3	22.8	0.67
Negative	DTR vs. StyleFusion	59.7	22.9	17.4	65.7	15.9	18.4	58.0	16.7	25.3	55.9	18.2	25.9	0.68
DTR vs. StylizedDU	57.2	24.0	18.8	57.9	23.0	19.1	50.1	20.9	29.0	46.5	24.8	28.7	0.66
DTR vs. StyleDGPT	54.8	18.4	26.8	58.2	17.9	23.9	55.0	19.6	25.4	51.0	28.6	20.4	0.65
Polite	DTR vs. StyleFusion	60.9	15.9	23.2	64.3	7.3	28.4	55.3	16.1	28.6	47.1	25.2	27.7	0.70
DTR vs. StylizedDU	58.7	22.1	19.2	58.6	20.4	21.0	47.8	21.2	31.0	45.6	31.8	22.6	0.66
DTR vs. StyleDGPT	58.0	21.6	20.4	60.2	21.1	18.7	56.7	20.7	22.6	50.5	29.2	20.3	0.68
Topical Chat
Positive	DTR vs. StyleFusion	54.8	16.5	28.7	53.0	13.6	33.4	56.0	17.2	26.8	53.5	17.4	29.1	0.69
DTR vs. StylizedDU	45.9	19.4	34.7	49.1	21.3	29.6	52.7	21.4	25.9	46.7	20.2	33.1	0.63
DTR vs. StyleDGPT	48.2	23.3	28.5	57.3	20.5	22.2	48.8	24.0	27.2	53.6	23.9	22.5	0.65
Negative	DTR vs. StyleFusion	56.8	8.5	34.2	62.5	10.6	26.9	53.7	10.2	36.1	55.2	16.8	28.0	0.73
DTR vs. StylizedDU	49.2	16.8	34.0	55.8	24.9	19.3	50.9	22.4	26.7	38.7	25.1	36.2	0.66
DTR vs. StyleDGPT	56.7	21.6	21.7	51.6	27.4	21.0	54.0	22.6	23.4	52.6	22.9	24.5	0.64
Polite	DTR vs. StyleFusion	58.2	12.6	29.2	56.5	8.9	34.6	58.3	11.6	30.1	50.7	23.1	26.2	0.68
DTR vs. StylizedDU	54.6	17.1	28.3	48.0	23.8	28.2	48.2	25.1	26.7	46.0	28.3	25.7	0.70
DTR vs. StyleDGPT	49.8	19.3	30.9	46.5	28.1	25.4	45.6	27.3	27.1	53.5	21.1	25.4	0.65
Table 8: Manual evaluation results. W, L, and T refer to Win, Lose, and Tie, respectively. The ratios are calculated by combining labels from the three annotators.