Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation
Authors: Jinpeng Li
Zekai Zhang
Xiuying Chen
Dongyan Zhao
Rui Yan
Venue: EMNLP 2023 Findings (2023)
Keywords: Stylized Dialogue Generation
Knowledge Augmentation
Feature-guided Selection
TL;DR: This paper proposes a Knowledge-Augmented Stylized Dialogue Generation model, which includes a feature-guided style knowledge selection module that utilizes context and response features.

Abstract: Stylized dialogue generation systems aim to produce coherent and context-aware dialogues while effectively emulating the desired style. 
Generating stylized dialogue is valuable yet challenging due to the scarce parallel data. Existing methods often synthesize pseudo data through back translation, yet suffer from noisy and context-agnostic style signals caused by insufficient guidance on target style features. 
To address this, we propose the knowledge-augmented stylized dialogue generation model, which includes a feature-guided style knowledge selection module that utilizes context and response features. Specifically, we retrieve dialogue-related style sentences from style corpus to explicitly provide clear style signals. We design a feature-guided selection module with response-related contrastive learning and style responsiveness Kullback-Leibler losses to enhance generation at both semantic and stylized levels. Our approach demonstrates satisfactory performance on two public stylized dialogue benchmarks in both automatic and human evaluations.

Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation
Jinpeng Li Zekai Zhang' Xiuying
Xiuying Chen', Dongyan Zhao-.3.4 Rui Yan
Wangxuun Institute of Computer Technology, Peking University
*King Abdullah University of Science and Technology
State Key Laboratory of Media Convergence Production Technology and Systems *Institute for Artificial Intelligence, Peking University
$ Gaoling School of Artifical Intelligence, Renmin University of China
[lijinpeng, justinzzk @stu.. pku. edu. on, xiuying chen@ kaust edu. sa, zhaody 0pku. edu, en, ruiyan@ruc. edu. on
Abstract
Context:
Stylized dialogue generation systems aim to produce coherent and context-aware dialogues while effectively emulating the desired style. Generating stylized dialogue is valuable yet challenging due to the scarce parallel data, Fi- isting methods often synthesize pseudo data through back translation, yet suffer from noisy and context-agnostt style signals caused by insufficient guidance on target style features. To address this, propose the knowledge- augmented stylized dialogue generation model, which includes feature-guided style knowl- edge selection module that utilizes context and response features. Specifically, we retrieve dialogue-related style sentences from style cor- pus to explicitly provide clear style signals. We design feature-gut guided selection module with relatee contrastive learning and style responsiveness Bullback-Leible losses to en- hance generation both semantic and stylized levels. Our approach demonstrates satisfac- tory performance on two public stylized dia- logue benchmarks in both automatic and hu- man evaluations. We have released our code and datasets."
None my friends discuss masic with
Baseline:
could Ity looking people like discussing too.
Stylized Knowledge:
Sy: Friends with shared musical interesis oflen form strong bonds.
Sy. Dear Watson, have you considered the power of melodic isarmonics?
Sy: would behoove you venture inlo social circles reveal thuse who posseas music acumen.
KASDG:
Warson, hchooves you explore cireles shared musical interests and musical acumen.
Example of stylized dialogue generation hanced by knowledge from semantic and stylistic level.
Figure 1:
However, stylized dialogue generation encoun- ters challenge due the absence of parallel train- ing data between context and desired stylized re- sponses, which renders supervised methods inef- fective. One natural solution to this problem is to enhance the connection between different hidden space vectors. For instance, Gao et al. (2019b) pro- pose method that bridges conversation modeling and non-parallel style transfer by sharing a struc- tured latent space. Nevertheless, this approach is limited by the quality of the available data, and the weak supervision signal leads to unsatisfac- tory performance in practice (Zheng et al., 2021). Another direction to establish pseudo pairs be- tween contexts and style-specific responses using unsupervised or semi- comi-supervised learning by back translation, which is proposed for translating sen- tence from the target language back to the souree language, allowing the model learn from the differences and improve translation quality (Sen- nrich et al., 2016). Su et al. (2020) propose a di- versifying dialogue model based on iterative back translation. Zheng et al. (2021) employ a style rout-
1 Introduction
With the increasing demand for meaningful human- computer interaction, the development of advanced dialogue systems has become crucial across various domains. Stylized dialogue generation focuses producing human-like conversations by generating stylized and coherent responses. To achieve this, significant efforts have been dedicated to building response-min mattning models that leverage both dia- logue content and style sentences, resulting in more engaging and diverse responses (Gao et al., bh; Zheng et al., 2021; Li et al., 2021; Sun et al., 2022).
"Equal contribution.
"ooreeppondigg author: Dongyan Zhao and Rui Yan.

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ing approach with joint training process, where backward model with style embeddings is used to generate pseudo pairs and train the stylized model. Li et al. (2021) bridge the forward and backward models through text transfer dataset to construct high-quality pseudo-contexts. Despite the progress made by these approaches, the pseudo data con- structed using the back translation method exhibit low diversity and these methods suffer from noisy and context-agnostic style signals (Li al., 2021). This because the style knowledge not explicitly provided for each response. Additionally, there is a lack of alignment between relevant stylistic words and responses, significantly reducing the incorpo- ration of style features in the generated responses. dialogue
We design feature-guided selection module filter style features and bridge the gap between prior and posterior in style sentence selection.
Experiments conducted on two datasets with four styles show that our proposed method outper- forms all baselines with limited training data.
Related Work
Stylized Dialogue Generation aims to generate coherent dialogues with specific styles. The lack of supervised data leads to difficulties in style and semantic alignment, which affects the performance of the model, and the collection of parallel corpus is time-consee consuminn and laborious. So critical to utilize unpaired style corpus to generate stylized responses. Previous rescarch has explored style information in implicit ways. SFusion (Gao et al., 2019b) constructs structured latent space that aligns style examples into the neighhorhood of response, providing clearer signals. StyleDGPT (Yang et al., 2020) leverages both style language model and style classifier to provide style signals. Addition- ally, SRJT (Zheng et al., 2021) and MPDL (Li et al., 2021) use back translation techniques to obtain pseudo-content suffer from noisy and context-agnostic style sig- nals, as the style is not explicitly provided for each response. Our approach explicitly exploits style information from external knowledge perspec- tive and can provide clearer style signals for better generation via knowledge retrieval and filtering. Knowledge Selection focuses on identifying rel- evant knowledge sources from large corpus to improve downstream tasks, which can be divided into two categories: sequential selection and non- sequential selection. The former aims to select knowledge based on previously selected ones, to gradually build coherent knowledge base (Kim et al., 2020; Zheng et al., 2020). The latter is identi- fied based on its relevance to the task hand (Qin et al., 2019; Ren et al., 2019; Gao et al., 2019a; Chen et al., 2022, 2023). This requires the model to have a high ability to understand natural lan- guage (Cheng et al., 2023a,b). Liu et al. (2021) propose a three-stage learning framework that in- cludes controller to dynamically select knowledge during the decoding phase. Meanwhile, Chen al. (2020) attempt to bridge the gap between prior and posterior knowledge selection. Inspired by these works, we leverage a retrieval and selection frame- work to dynamically select needed style signals for
Motivated by seawledge-groundde generation (Liu et al., 2021), we introduce www.ledg--augeente stylized dialogue genera- tion (KASDG) model, as illustrated in Figure 1. Our model utilizes style corpus from an external knowledge base perspective, and provides style in- formation for response generation in an explicit way. Specifically, we employ style sentence re- trieval process, which retrieves the most relevant style sentences based on similarity metrics to guide the style of response. In order to filter out noisy information and provide dialogue-related style sig- nals, we refine the retrieved style feature using a feature-guided selection module, wherein the re- trieved style feature is filtered under the guidance of context. Moreover, to reduce the gap between context-related selection and response-related se- lection and obtain more useful filtered style fea- tures for response generation, we propose response- related contrastive learning and style responsive- ness Kullback-Leibler loss. The context hidden and filtered style features are integrated into the content-aware attention and style-aware attention in decoder, producing the final dialogue response.
However, these methods
We conduct extensive experiments two bench- marks with four distinct styles to verify the effec- tiveness of our proposed model. The experimental results demonstrate that our approach generates more informative and accurate responses that align with the given stylized knowledge. We also discuss the ad advantages of our model compared with large language model in Appendix A.5. In summary, our contributions can be summarized as follows:
We propose a knowledge-augmentted stylized di- alogue generation model with a -aature-guided selection in an unsupervised manner.

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Response
Style Corpus
Add & Norm
Retrieval
Feed Forward
SRKL
Style Sentences
5
S
5K
Style-awware Attention
Context-aware
Style Enooder
Attention
Add & Norm
Content-aware Altention
Context Encoder
& Norm
Back-translation
Masked Self-attention
Model
1G
Context Tokens
Response (Shifted Right)
Figure 2: The framework of knowledge-augnee augmented stylized dialogue generation (KASDG) model with feature- guided selection module. The blue arrow maximizes distance between reprexentations, while the red arrow minimizes distance between representations in contrastive learning.
guiding dialogue generation.
data using back-translation for further training. Our approach mainly follows the style disentanglement framework and utilizes
Text Style Transfer is to generate sentence that is both stylistic and preserves the original mean- ing, which focuses solely on style transfer with- out considering conversational coherence. With the scarcity of parallel corpus, recent approaches have focused on unsupervised text style trans- fer, which can be categorized into three main ap- proaches: style disentanglement, prototype editing, and pseudo-paralle corpus construction (Jin et al., 2020). Style disentanglement approaches (Hu et al., 2017; Yi et al., 2020; Zhao et al., 2017; Fu et al.,
method.
Methodology
Model Overview
The Knowledge-augmenter stylized dialogue gen- cration model consists of two main components, the mncoder-decodee framework and the feature- guided selection module, as shown in Figure 2. We employ separate style encoder and context en- coder to encode style and content information. respectively. Inspired by the Liu et al. (2021), add the style-aware attention and gate net- work to control the knowledge and context con- tributions. The model trained on a dialogue dataset D = ((X,, Yi) M i=1 and a style corpus Daty = [S where X; is the dialogue context with relevant response Y, in style so. S₁ is the i-th sentence in the target style $1 Given input context X, {wi, W2,,.., ..., wm consisting of words, we aim learn model fa generate styl- ized response Y, Here, we propose to first retrieve context-related style knowledge D.(X,) C Daty- and then combine them with context X, to generate stylized response Y; fo(X,, (o(.), The opti-
2017: Li et al., 2020) learn a latent representa- tion of input that separates the content and style information. This representation can be manip- ulated perform style transfer while preserving the content. For instance, et al. (2017) use adversarial networks to separate content and style representations, and change the style embedding to perform style transfer. Prototype editing ap- proaches (Li et al., 2018; Madaan et al., 2020; Sudhakar et al., 2019; Wu et al., 2019; Jin al., 2019a) identify style markers in the sentence and edit them to the target domain perform style transfer. Pseudo-parallel corpus construction ap- proaches (Jin et al., 2019b; Nikolov and Hahnloser, 2018; Zhang et al., 2018) generate pseudo-stylized

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mization objectives of the forward model fe can be represented follows:
retrieved style hidden can be ineffective, since the majority of the filtered style features tend to be context-related rather than response-related, reduc- ing their effectiveness in response generation. To address this issue, we examine the relationship be- tween context hidden he, response hidden h,, re- trieved style hidden h, and filtered style feature h In Section 3.3.1, we propose response- contrastive learning make filtered style feature more response-related by exploring the interaction between hg, hy, and hy- In Section 3.3.2, we in- troduce style responsiveness Kullback Kullback-Leibler Leibler loss to reduce the gap between context-guide guuded selec- tion and desponse-guider selection by exploring the interaction between he, hy, and hy-
dain log P(Y)X,
(1)
where i E 10.1}. Besides, we have backward model So which can generate pseudo context ac- cording to the stylized sentence for maximizing the supervision. The difference with previous work is that our backward model is only optimized through X.). This operation easier to converge, re- ducing the training overhead. Similarly, the opti- mization objectives of f is as follows:
La = log fo))
(2)
3.2 Style Sentences Retrieval
In order to retrieve context-related style knowl- edge for the input, we apply an information re- trieval module with sentence-BERT (Reimers and Gurevych, 2019), which use siamese and triplet network structures to derive semantically mean- ingful sentence embeddings. Specifically, we use sentence-BERT to encode context X and the style corpus Duy = V.V. respectively and calculate the relevance score Rel using cosine-similarity:
3.3.1 Response-Related Contrastive Learning
We utilize contrastive learning (Gao et al., 2021) to make filtered style features more response-related. Following the intuition that filtered style features h should be closer to response hr than the trieved style hidden h,, we design our response- related contrastive learning supervise the dis- tance relation. For mini-batch of N pairs of (hag h,, denotes the i-th sample in the batch. We treat responses as the anchor, the corre- sponding filtered style features as positive exam- ples, other filtered style features and all retrieved style hidden as negative examples. The response- related contrastive learning loss is defined as:
Rel, = -im(BBRT((()))) (SRT(S.).
(3)
AT
where sim h2) [- hall' Then the most relevant sentences from Daty are retrieved and sent style encoder obtain retrieved style hidden, serving as the style knowledge of the input. We also analyze the performance of dynamic retrieval and fixed retrieval in Appendix A.3.
La = log
(5)
19933 15 + gimihrpbajlyy
3.3 Feature-Guided Selection Module
Inevitably. there exists noisy and irrelevant informa- tion in the retrieved style hidden. To address these redundancies and extract dialogue-related style in- formation, we introduce the feature-guided selec- tion module. Specifically, we integrate context information guidance for our style feature se- lection process. For clarity, let the context hidden be he E Rl.exd and the original style hidden of the retrieved style sentences h2 E RL.xd The filtered style features obtained from our selection module are denoted as hy. which is constructed utilizing the attention mechanism:
where is the temperature of contrastive learning.
3.3.2 Style Responsiveness Kullback-Leiblee
As we rely solely context filter retrieved style hidden, a potential gap emerges between context- guided and response-guiee guided selection processes, To overcome this disparity, we introduce the style responsiveness Kullback-Leibler (SRKL) loss, de- signed to optimize filtered style features using both context and response. We formulate this problem as an effort to bridge the gaps between the prior and posterior distributions of style selection, uti- lizing cosine similarity as the importance metric. Specifically, context he and response hy can as- sign different importance to different positions of hg = (h). ..., h!->.. The -nntxx--guided prior distribution p(s) and response-guided posterior dis-
hy = so stoftmrrhhhh hh.
(4)
where hy E Rlexd, The and L, are the length of context and style sentences, respectively. Sim- ply relying on contextual similarity to filter the

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tribution g(s) are calculated as:
to query y. Cresp can be approximated by:
p(a) exp(h!" hh)
Lreep = log Σ esp(h?"" h1) - log Σ explhi hha)
Ee exp(h?" ha)
(6)
((s)) exp(h!, hr)
max(h; h-) max(h;" hm)
Ee exp(I?" lt.)
= -R(h,, hr) - R(h,, hc).
(1I)
From the perspective of responsiveness score, Gresp supervises that the retrieved style hidden shares more information with context than with (7) response. However, this supervision offers limited assistance the feature-guided selection module since our ultimate goal is to enhance the usefulness of h for response. Therefore, we change the su- pervised object from h& to h.- Moreover, with the intuition that the filtered style feature h should share more information with response than context, so reformulate Lresp as:
The KL divergence loss is defined to be:
LKL E p(s.)log p(a.) g(s))
According to Equation 6 we can obtain:
log)(s.) h²"" te log()
exp(h?" he)),
log ((s)) N1," N2 log
exp(le" 1,))),
Syeup max(0, R(hy, R(hy, h,))
(12)
where Σ ((s)) = 1. Hence, we can decompose
The SRKI. loss can be formalized weighted sum of direction and responsiveness losses:
KL loss into LKL = Ldir + Lresp as follows:
LSRKL Adirh Aresphrespt
(13)
Lair = Σ (aa))))((me hr)
where Adir and Xresp are the weight scalars.
3.4 Training Objective
Greap log) D exp(hs" hr) log > expph?" he):
The training objective of the forward model can thus be defined as:
(9)
where Ldir is the direction loss, and Gresp is the responsiveness loss.
Co, Liji + Aathat ASRKLLSRKA.
(14)
Direction Loss. In Lair, the term Σ p( rep-
where {0,1). The overall training objective can be expressed as:
i=1
resents a content-guided reweighted style hidden, where the weight denotes importance. This con- cept aligns with the objective of our Teature-guided selection module. To improve guidance signals, we replace the term with the filtered style feature hy. Intuitively, the difference between the response and context, h, he. signifies the necessary style information for the generated response to become stylized. Thus, h? (he - hy) can be deemed direction loss guiding the selected style features:
C L= Dug + Xay Cai + AncCos
(15)
where ddt ÀSRKL. Aay: An and Air are weight scalars. In the experiments, back translation can be unstable, we propose warmup training strategy for the loss Lzy
find that the
To To- T7 T. T₁ 0),
Dr min(max(
(16)
where Te, Ty, To represent the current training step. freeze step, and warm-up step respectively.
Cair = max(0,00--)))) - (hr - he)).
(10)
Experimental Setup
Responsiveness Loss. To evaluate the extent of in- formation shared between the selected features and generated response, we define the responsiveness score R(x y) = max(x²') y). which examines the maximum information that hidden x can provide
4.1 Datasets
We conduct our experiments on two benchmarks with four distinct styles: generating formal-like and informal- like responses (TCFC (Wu et al., 2020))

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Style
Model
BLEU-I
BLEU-2
Rouge-1.
Dist-1
Dist-2
StyleIn.
Mlask
13.42
3.56
11.53
0.040
0.091
0.284
S2S+LM
15.25
0.052
0.273
0.399
SFusion
16.81
10.82
0.055
0.107
arXiv
DialoGPT
17.84
5.20
10.68
0.296
0.711
0.208
StyleDGPT
19.04
12.49
0.228
0.614
0,503
KASDG
35.11
15.02
27.25
0.225
0.614
0.948
MTask
24.47
8.87
16.03
0.027
0.063
0.276
S2S+LM
25.32
9.15
14.82
0.051
0,304
0.450
SFusion
25.91
9.68
15.87
0.045
0.098
0.479
Holmes
DialoGPT
27.19
8.31
14.78
0.172
0.589
0.282
StyleDGPT
29.58
10.15
17.10
0.101
0.452
0.602
KASDG
36.86
13.21
22,82
0.139
0.553
0,583
Table 1: Automatic evaluation results the arXiv and Holmes styles.
and arXiv-like and Holmes-like responses (Gao et al., 2019b). TCFC focuses on formality with an informal dialogue corpus and formal sentence cor- pus. arXiv and Holmes constructed by the aca- demic website and Sherlock Holmes novel series, respectively. Only the informal dialogues contain context-response pairs, the other styles are non- parallel corpus of sentences. Both tasks share the same informal Reddit conversation dataset. The detailed statistics are given in Appendix A.1.
word-level KL loss and sentence-level style classi- fication loss (Yang et al., 2020). MPDL explores the interaction between synthetic and original data within multi-pass dual learning framework (Li et al., 2021). We also discuss the advantages of our model compared with large language model ChatGPT (OpenAI, 2023) in Appendix A.5.
4.3 Implementation Details
We implement experiments using Pytorch with transformers The main forward model and back- ward model are initialized with BART base with the learning rate 5e-05. We use grid search to tune the hyper-parameters according the performance of validation. The temperature hyperparamter is set to 0.1 and all coefficients except Äs are set to 1.0. During training, we leverage the Adam optimizer with batch size 4. We select K = 20 most related style sentences calculated by centence-BERT" input of style encoder. We warm up the backward model with a freeze step of 5,000 and warmup step of 10,000. Due to the instability of back translation. we use beam search for generation with the beam size 50 and filter out the pseudo context that share more than 30% tokens with its response.
4.2 Compared Baselines
To verify the effectiveness the proposed method, we compare our model with the pre-trained di- alogue generation model DialoGPT, which has not been specifically trained for stylized genera- tion, serving baseline for evaluating the per- formance of models with explicit style transfer ca- pabilities (Zhang al., 2019). We compare our proposed model with the pipeline method S2S+BT, which first generates non-stylized response using a S2S approach, and then refines the response by ap- plying a text style transfer model (He et al., 2020). Besides, we also compare our proposed model with recent stylized dialogue generation models: MTaask is vanilla multi-task learning model (Luan et al., 2017) trained on both Disa and Dsty- S2S+LM sequence-to-
Experimental Analysis
combines the output distribution sequence (S2S) dialogue model with stylized lan- guage model (LM) (Niu and Bansal, 2018). SFu- sion also a multi-task learning model that bridges the gap between conversation modeling and non- parallel style transfer by sharing a structured latent space (Gao al., 2019b). SR.TT is based the back-translation that incorporates joint learning strategy and style routing method to facilitate style transfer dialogue generation (Zheng et al., 2021). StyleDGPT leverages the pre-trained dia- logue model and fine-tuned using a combination of
5.1 Main Results
Automatic Evaluation Following the previous work, ROUGE and BLEU metric (Papineni et al.. 2002) are employed to measure n-gram overlap be- tween the generated responses and the reference responses for automatic evaluation. Distinet (Li et al., 2015) measures the proportion of unique n-grams in the generated responses. To evaluate the style intensity, we follow (Zheng et al., 2021)
http:s///gttiil.oommmmggggggg//s hhtps://wwwwshert.ee

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Model
B-1
B-2
Dist-2
BERT
SVM
performance of KASDG in informal style. The su- pervised learning is applied to informal style, while formal style requires pseudo context generation. Human Evaluation Apart from automatic metries, we conduct human evaluations to assess the qual- ity of generated stylized responses arXiv style. Specifically, we randomly sample 100 examples and employed three experts to grade the quality of generated responses and reference responses by three criteria: (1) Fluency measures the generated responses are readable.
Formal-style Response Generation
MTTask
6.35
0.50
29.3
37.3
50.1
SLM
12.6
0.99
42.5
85.6
87.2
SFusion
5,51
0.28
61.0
21.9
39.0
S2S+BT
12.1
1.25
42.0
86.3
86.8
SRJT
15.1
1.71
43.4
97.3
96.1
MPDL
16.5
2.07
51.3
98.6
97.1
KASDG
18.4
2.85
45.7
96,9
91.6
Informal-styy -stle Response Generation
S2S
6.92
0.61
54.8
70.1
60.9
SFusion
4.61
0.22
62.8
70.3
61.1
SRJT
6.96
0.67
49.4
69.4
59.2
MPDL
7.12
0.69
49.5
70.3
60.7
KASDG 9.48
1.66
63.5
71.0
64.7
(2) Relevance measures the response is coherent with the given context. (3) Style Consistency exhibits the desired style in the response. For each human indicator, we give the annotators three ranges: [0,0.33] is very dis- satisfied; (0.33,0.67) is general satisfaction; and [0.67,1] is very satisfied. All generated summaries are re-capitalized and de-tokenized fairly.
Table 2: Automatic evaluation results in the Formal and
Informal styles.
Model
Fluency
Relevance
Style Cons.
StyleDGPT
0.63
0.55
0.61
SRJT 0.78
0.51
0.73
KASDG
0.80
0.72
0.81
Table 3:
Human evaluation results in the arXiv style.
The results are shown Table 3, which shows that proposed KASDG outperforms the other base- line models in both human metrics. The kappa statistics are 0,58, 0.53 and 0.55 for Fluency, Rel- evance and Style-Consistency respectively, indi- cating the moderate agreement between annota- tors (Landis and Koch, 1977). Specifically, for the response generation in arXiv style, the style consistency and content relevance are significantly higher than the baseline models, indicating that our feature- guuuee selection module has gained better modeling ability through displayed guidance.
and leverage the trained style classifiers BERT and SVM for Informal and Formal styles. The accuracy of the BERT and SVM classifier on the holdout test set are 93.98% and 89.57% respectively for the TCFC. Furthermore, we adhere to the use of the pre- trained discriminative model X))] (Yang et al., 2020) for evaluating the Style Intensity (StyleIn.) of the response in Holmes and arXiv styles.
The automatic results are summarized in the Ta- ble and Table 2. Overall, our method achieves the highest BLEU-1 and BLEU-2 scores on the four styles, which shows the superiority of our method. For formal response generation task on the TCFC dataset, compared with the previous state-of the-art algorithm MPDL, our proposed method improves the BLEU-1 and BLEU-2 scores by 1.9 and 0.78. The reasons are two-fold: (1) KASDG can be bet- ter aligned in both style and semantics via the style sentences retrieval process. (2) the feature-guided selection module effectively filters out the noise information. We do not achieve the highest score on Dist, mainly due to the negative correlation be- tween BLEU and Dist. Our methods exhibit su- perior performance in the arXiv style, while only showing comparable results to StyleDGPT in the Holmes style. This can be attributed to the larger corpus of 1,347k sentences in arXiv compared to 38k in Holmes. The increased availability of style sentences contributes to improved retrieval results and a clearer style signal for our model, resulting in better performance. Training bias leads to better
5.2 Ablation Study
To better quantify the effectiveness of each mod- ule in KASDG, we conduct an ablation study on the Holmes dataset: From the ablations in Table 4, we observe that: (1) The SRKL and CL both play very important role in the model and their re- moval will result in varying degrees of performance degradation; (2) The modified SRKL outperforms the original KL in all metrics, which is a good indication that the SRKL constraint model is opti- mized towards the target style and achieves better style feature. (3) Contrastive learning enhances our model's 's dialogue capabilities at the expense of style proficiency, aligning with our objective. We incorporate contrastive learning to render fil- tered style features more response-oriented As the contrastive loss contains stylized retrieved hidden as negative samples, our contrastive loss primarily targets content fidelity, ensuring that filtered style features are closely related to the response. How-

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Model
BLEU-1
BLEU-2
Rouge-I.
Dist-1
Dist-2
BERT
Base-model
35.32
11.90
22.09
0.126
89.09
36.36
12.64
22.23
0.132
87.07
34.88
12.33
21.56
0.136
0.548
85.76
W/ SRKI.
36.77
12.55
22.83
0.138
0.550
89,20
w/ CLASRKL
36.86
13.21
22.82
0.139
0.553
86.81
Table 4: Ablation Study in the Holmes style. The accuracy of BERT the holdout test set is 99.4%.
Context
think we need more context. How do you just tackle teacher not get trouble?
1.0
Human
Well, normally you would have to serve detention, but his serving was administered at the time of the infraction.
0.8
0.6
Retreival
We must friend, the professor.
and have it out with our
MTask
Totally agreed. What class would be your teacher?
0.4
SFusion SDGPT
would be man of the moment.
He was man of the few days i saw this in the time of no trouble so can make remark from this.
Stylized Responses
Retrived Hiddens
0.0
Filtered Features
SRJT
The first difficulty which we had contend with was the finding of this american's 's tecedents.
0.0
0.2
0.4
0.6
0.8
1.0
KASDG
would suggest that the professor's conduct should be reported the police.
Figure 3: Visualizing the retrieved style hidden, stylized responses and filtered style features in Holmes style.
Table 5:
Examples the generated responses by KASDG and other models the Holmes style.
ever, alterations in the hidden space unavoidably impact style proficiency, explaining the observed decline in style ability upon incorporating the con- strastive learning.
coherent responses but struggles capture the de- sired style and context relevance, likely due to the absence of pre-trained weights, Both SFusion and StyleDGPT generate partially stylized responses but suffer from poor coherence or contextual rel- evance, possibly due to the instability of the sam- pling and ranking processes. SRJT effectively gen- erates sentences in the Holmes style but fails to di- rectly address the context, resulting in suboptimal responses. Retrieval represents the retrieved styl- ized sentence, which includes some stylistic words (e.g.. "professor") but lacks strong contextual con- nection. contrast, our model demonstrates supe- rior performance by generating responses that are coherent, contextually relevant, and consistent with the Holmes style. For instance, when discussing the trouble involving teachers, our response refers to "reported to the police", which is highly rele- vant. Furthermore, the use of "professor" instead of "teacher" along with "conduct" and "reported to the police" are all topical words found in the Sherlock Holmes novel series. Interestingly, the "professor" mentioned the retrieval process, showcasing the effectiveness of our model.
5.3
The Impact of Feature-Guided Selection Module
Further, also investigate the impact of feature- guided selection module. The hidden states in Holmes style are visualized, including the retrieved style hidden, the response hidden and the filtered style features. We use the output of the hidden layer the representation of the sequences and utilize t-SNE algorithm (Maaten and Hinton, 2008) for vi- sualization. As shown in Figure 3, we can that the retrieved style hidden relatively far from the target responses and the boundary between filtered style features and stylized responses is quite clear. These phenomena illustrate the effectiveness our selection module. There exist noise the re- trieved style hidden, but after the selection module the irrelevant information are filtered out, which in turn better aids generation.
5.4 Case Study
Table presents sample responses generated by different models the Holmes style. Upon com- paring the models, we find that MTask produces

---

6 Conclusion
committed addressing potential ethical concerns and refining our work as needed, adhering estab- lished ethical guidelines and practices within the scientific community.
In this paper, we propose knowledge- -aggddee- stylized dialogue generation model with feature- guided selection module to provide better guidance signals from both semantic and stylistic perspec- tives. Specifically, we exploit the style knowledge from a style corpus to provide guiding style signal and design a response-related contrastive learning and style responsiveness Kullback-Leiih loss to enhance the semantic and stylistic features. Exper- iments two public benchmark datasets demon- strate that our proposed method has satisfactory results in generating response with desired style in four target styles. the future, we plan to explore model performance in zero-shot scenarios and to combine the ability of large language models for intelligent dialogue systems.
Acknowledgements
We would like to thank the anonymous review- ers for their helpful comments and suggestions. This work is supported by National Key R&D Pro- gram of China (No. 202IYFC3340303 and Na- tional Natural Science Foundation of China (No. 621 (20220..).
References
Xiuyi Chen, Fandong Meng. Peng Li, Feilong Chen, Shuang Xu, Bo Xu, and Jie Zhou. 2020. Bridging the gap between prior and posterior knowledge se- lection for knowledge aaggggggoddddd dialogue generation. In P Proceedings of the Empirical Methods in Natural Language Processing.
Limitations
A main limitation of this work is the availability of data. Although numerous styles exist, the ac- tual usable data is scarce. As with other efforts to generate pseudo-data, cannot rule out the pos- sibility that pseudo-data generated through back- translation may compromise the authenticity of the generated text. It is therefore important to encour- age users to check the relevance of supplementary generated texts. Furthermore, we do not conduct multiple rounds of experiments with stylized di- alogue, which is also very important capability. Due to limited computing resources, we do not set large batch size explore more styles. This is very important in the era of large language models. We plan to explore generating more and higher-quality stylized data with the assistance of a large language model, in order to enhance the performance of the dialogue systems. We will examine these issues more comprehensively in the future research.
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Ethics Statement
This paper presents a knowledge-uugmentin styl- ized dialogue generation model with the intention to benefit various natural language applications and enhance creative language expression. The datasets used in study are publicly available and respect privacy standards. In human evaluation, part-time research assistants were fairly compensated, and established evaluation rules were followed. Our approach does not introduce or exacerbate ethical or social biases in generated dialogues. We remain
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A Appendix
given context, while terms like "position" and "hori- zons" are consistent with the arXiv style.
A.1 Datasets Statisties
Table 6 shows the statistics of datasets, including the number of data and the average length in sen- tence. The average length of context and response is 12.9, 11.3 respectively.
A.3 The Impact of Back Translation and Retrieval
In this section, we study the impact of back transla- tion module and different retrieval inputs, as shown in Table 8. The w/o BT do not use back translation, which retrieves the most similar context in the di- alogue corpus as its pseudo context for the input style sentence. FixBTc and FixBTR refers to use the pseudo context or the style sentences to retrieve paired style sentences respectively through pre- trained back translation model. From the results, we find that the back translation model plays an important part in our model due to the lack of par- allel data. Besides, comparing FixBTc to FixBT R+ we find that it is better to use response to retrieve style sentences from style corpus, it is due to the quality of the pseudo context and back translation. In FixBTR, the style score is very low and tends to copy phrases in context. This due to the fixed back translation model leads to all the same pseudo context. In contrast, we jointly train the back trans- lation model with our main model, and with the evolution of the back translation model, it can pro- vide dynamical and more diverse pscudo context, thus making our model more robust and relieve the problem of copying. We also compare our proposed model to retrieval model RAG (Lewis et al., 2020) in Holmes style, which is an unsu- pervised serieval-augmenten method. As the re- sults demonstrate, our approach substantially out- performs RAG in both style and content metrics due to (1) Our framework takes full advantage of di- alogue datasets via our jointly loss, thus producing more coherent sentences. (2) Retrieved sentences are noisy. Our proposed feature-guided selection module tackles noise in retrieved sentences and extracts dialogue-related style signals.
Style
Informal
Formal
arXiv
Holmes
Training
217K
500K
1,347K
38K
Validation
978
978
2,000
2,000
Test
978
978
2,000
2,000
Avg.words
12.9/11.3
12.7
16.5
16.8
Table 6: Dataset statistics. "Avg.words" mean the aver-
age words length sentence.
A.2
Case Study
Context
Welcome to ultimate disappointment, I'll be your guide! Time to conquer earth!
Human Retreival
The harmonic convergence is upon us! These and other directions await explo- ration.
MTask
I haven't i heard of it yet, I haven't heard anything yet.
SFusion SRIT
Thank you!!! I will be here for you! But, the above is not complete description, only brief description is required. Time conquer the time travel speed of the time!!
StyleDGPT
Ours
Now in position to conquer all the horizons our universe.
Table 7: Case Study in the arXiv style.
The case study of arXiv style is shown in Ta- ble 7, entries highlighted in red point to incon- sistencies either in style or content, while those in blue represent a harmonious alignment of both. The responses generated by StyleDGPT and SRJT attempt to incorporate the arXiv style, but they lack coherence with the given context and are semanti- cally unclear. For example, StyleDGPT contains the phrase "time travel speed of the time," which is not meaningful in the given context, while SRJT states that "only a brief description is required", which do not address the topic at hand. MTask gen- erates general and plain responses that do not add any relevant information to the conversation. SFu- sion is informal and does not adhere to the arXiv style. The Retrieval model generates response that contains stylistic elements such as "cxploration" and "direction", but they are not contextually rel- evant the given conversation. In contrast, our model is both contextually relevant and consistent with the arXiv style. In our model, the words "con- quer" and "universe" demonstrate adherence to the
A.4 The Impact of Attention
To further analyze the selection module, we vi- sualize the attention in the selection module, as shown in Figure 4. For the same context, arXiv and Holmes focus on different words. arXiv focuses on the academic words (stage, process, fulfilled, con- tract, and proposal), while Holmes focuses more on the daily life words (requested, business, pur- chase, living, contracted, and job). The former is commonly seen in formal papers, while the latter appears more realistic and conforms to the feature

---

Model
BLEU-1
BLEU-2
Rouge-1.
Dist-1
Dist-2
BERT
RAG
24.55
11.35
13.88
0.306
0.759
54.64
KASDG
36.86
22.82
0.139
0,553
86.81
w/o BT
31.43
7.98
19.62
0.037
0.108
85.35
FixBTo 32.62
11.00
21.67
0.154
0.570
89.62
F
36.23
14.95
22.95
0.178
0.651
67.30
Table 8: The analysis about the
and Retrieval methods.
arXiv
Hey
what
take
sign
you
0,028
permanently
before
0.027
deadline
0.026
day
0.025
determines
sign
fatrilee
propose
would
acknow wddg
financia
support
contract
requiremtn
consistently
contractalo
conature
keep
later
process
nexs
propostir
futurn
work
stors
Sig
xt
Apeaaje
tsodard
signature
Holmes
Hey
what
0.036
0.034
take
0.032
sign
you
permanently
before
0.028
deadline
day
0.026
sign
who e
settled
leave
emp ayment:
right away
keep
signing
posiiion
jeb
verif-
leaving
propose-
filed
unsigned
cierkahiip
business
promite
given
treaty-
rasase
months-
end
conveyess
hause
pledges
lived
emmounce
land
living
duties
tum
mooorie Seod draft
a
Figure 4: The attention visualization of the selection module for Holmes and arXiv styles.
of Holmes novel. At the same time, observe that the words "sign" and "signature" are more acti- vated in arXiv and less activated Holmes, which is in line with expectations.
the latter, we provide new prompts with style explanations in Holmes style, namely Prompt-3 and Prompt-4, we randomly selected 100 tests Holmes style to carry out our experiments in ad- vanced setting, as shown in Table9 (Line 7 Line 13).
A.5 Analysis Large Language Model
Prompt-1: """You are stylized conversation agent. Your lask to generate response in style style. The response should be 1-3 sentences. You only need to output the generated stylized response. Context: (context)
To further evaluate the performance of our model, we carry out experiments on ChatGPT, state- of-the-art large language model. Specifically, we design Prompt-1 and Prompt-2 to assess ability of ChatGPT for zero-shot stylized dialogue gen- eration. The results obtained from ChatGPT are summarized in Table 9 (Line Line 6) and 10. We also take step forward evaluate ChatGPT in the setting where examples or explanations of styles are given. For the former, we adopt the classic In-Context Learning algorithm KATE (Liu et al., 2022) with 1,5,10 and 20 examples. For
Response: oum
Prompt-2: """Generate response in style style. The response should be 1-3 sentences. Context: (context)
Response:
Prompt-3: """The Holmes style refers to the de- ductive and analytical investigative approach pop-

---

Style
Methods
BLEU-1
BLEU-2
Rouge-1.
Dist-1
Dist-2
StyleIn.
Prompt-1
22.09
13.27
0.186
0.506
0.092
arXiv
Prompt-2
23.36
8.31
13.99
0.187
0.533
KASDG
35.11
27.25
0.225
0.614
0,948
Prompt-1
14.91
6.63
14.72
0.236
0.621
0.144
Holmes
Prompt-2 2
17.38
8.10
15.54
0.272
0.662
0.086
KASDG
36,86
13.2
22.82
0.139
0.553
0,583
Prompt-3
22.83
5.95
12.98
0,386
0.782
0.102
Prompt--
21.18
7.25
14.06
0.398
0.809
0.125
ICL-Ishot
23.27
8.00
13.44
0.337
0.714
0,141
Holmes(100)
ICL-5shot
25.48
9.11
13.55
0.353
0.735
0.164
1Cs-10shot
26.10
10.16
14.63
0.366
0.745
0.164
1CL-o0
25.21
8.92
13.24
0.376
0.747
0,250
KASDG
36.86
13.21
22.82
0.139
0.553
0.583
Table 9: ChatGPT evaluation results the Holmes and arXiv styles.
Methods
BLEU-1
BLEU-2
Dist-2
BERT SVM
precise style signals. (3) To some extent, the re- sults be attributed to the distribution of the pretrained corpus. ChatGPT is trained diverse range of sentences, enabling produce coher- ent and varied responses. Moreover, ChatGPT has been exposed to a notable amount of formal-style sentences, which explains its superior performance in formal style. However, with fewer instances of other less common styles in its training data, the performance of ChatGPT in those styles is compar- atively weaker. It is worth noting that disparity exists between stylized sentences and the provided style corpus Dsty- Consequently, the style knowl- edge retrieved from its memory just a statistical approximation of the given style.
Formal-style Response Generation
Prompt-1
13.7
1.37
46.7
95.9
89.7
Prompt-2
43.9
97.5
KASDG
18.4
2.85
45.7
96.9
91.6
Informal style Response Generation
Prompt-1 6.30
0.95
40.2
26.2
30.0
Prompt-2
5.37
0.83
41.4
23.9
25.2
KASDG
9.48
63.5
71.0
64.7
Table 10: ChatGPT evaluation results in the Formal and Informal styles.
ularized by the fictional detective Sherlock Holmes, created by Sir Arthur Conan Doyle. This style em- phasizes keen observation, logical reasoning, and attention to minute details to solve complex myster- ies and crimes. Generate response in Holmes style. The response should be I-3 sentences. Context: [context]
Response:
Prompt-4: """The Holmes style refers to the dis- tinctive characteristics of the fictional detective Sherlock Holmes, created by Sir Arthur Conan Doyle. Generate response in Holmes style. The response should be 1-3 sentences.
Context: /context)
Response:
The results indicate that our models perform bet- ter in terms of both style and semanties, with the exception of formal style where ChatGPT demon- strates slight advantage. We attribute these out- comes to the following factors: (1) ChatGPT is not specially trained to generate stylized responses, while our model incorporates an optimized style feature selection module that provides clearer style signals. (2) Since ChatGPT has access to the stylized corpus, it can only retrieve style knowl- edge from its parameter memory. In contrast, our model performs explicit style knowledge retrieval, fully utilizing the available style corpus to provide


Reviews:
[{"review_id": "3o3JxZ1Rhx", "reviewer": "Reviewer_ehjU", "summary": "", "questions": "A. Could the authors provide the BLEU-3 and BLEU-4 scores for both the proposed method and the baseline approaches?\n\nB. Why the backward model needs to be jointly trained with other modules?\n\nC. How is the \"context-aware attention\" mechanism integrated within the content generator?\n\nD. What motivated the decision to incorporate the \"context-aware attention\" in the decoder subsequent to the context encoder?", "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": "Thank you for the valuable comments that help us improve the work. We will respond to the questions, and we appreciate it very much if you kindly raise the scores when the concerns are addressed.\n\n> Q1.The baseline methods.\n\nWe appreciate your great effort for completing our experiments. It is important to highlight the difference between our methods and the unsupervised methods you mentioned. The latter still need correct stylized responses for supervision, while our method can generate stylized dialogue responses using unpaired style corpus. Thanks for your suggestion, we implement RAG using the pseudo stylized dilogue pairs produced by our back translation model as supervision data. The results are listed as follows:\n|        | BLEU-1 | BLEU-2 | Rouge-L | Dist-1 | Dist-2 | StyleIn. |\n|--------|--------|--------|---------|--------|--------|---------|\n| RAG    | 24.55  | 11.35  | 13.88   | 0.306  | 0.759  | 0.546 |\n| KASDG  | 36.86  | 13.21  | 22.82   | 0.139  | 0.553  | 0.583 |\n\nAs the results demonstrate, our approach substantially outperforms RAG in both style and content metrics due to \n\n(1) Our framework takes full advantage of dialogue datasets via our $s_0$ loss, thus producing more coherent sentences. \n\n(2) As stated in Section 3.3 (L240-L243), the retrieved sentences are noisy. Our proposed Feature-Guided Selection Module tackles noise in retrieved sentences and extracts dialogue-related style signals.\n\nWe will cite the missing references you mentioned in the revision and supplement these experimental results.\n\n> Q2. The case studies and the colour schemes.\n\nFor colour schemes, red indicates inconsistency in style or content, while blue signifies that both are aligned. We present an additional case in Holmes test dataset:\n\n*Context*:\n*aah i did that in a middle school law class, except he ended up crying and we got 10 days of detention.*\n\n*SRJT*:\n*by considering the d brane, we can make the same argument for the bulk.*\n\n*KASDG*:\n*we would like to remind the reader that in this case a student would be in violation of the stephen law.*\n\nWe can clearly notice that SRJT's output “same argument” (will be marked red) leads to reduced contextual relevance, while our output maintains style and content alignment well. We will provide more case studies in the forthcoming version of our paper.\n\n> Q3. Incorporating external knowledge may introduce inaccuracies to the results and potentially lead to hallucination issues, a concern not addressed in this paper.\n\nThanks for pointing out! At present, the hallucination problem is unavoidable in text generation, and what we can do is to increase the relevance of knowledge. To tackle with the hallucination problem, we propose a Feature-Guided Selection Module to filter out information irrelevant to the content, thereby relieving the hallucination problem (L240). As demonstrated by experiments in Table 3, our method achieved the best human scores in terms of Relevance, indicating a reduction in hallucination issues.\n\n> Q4. The newer KDG methods are not considered in the baseline.\n\nFirstly, it's necessary to clarify the differences between DTR’s task and our task. DTR's task is to generate stylized knowledge-enhanced dialogues given a dialogue set, its corresponding knowledge base, and an additional unsupervised style dataset. In contrast, we treat the stylized corpus as knowledge to address the data scarcity problem without utilizing any external knowledge base. Secondly, we still tried to compare DTR but we find that the code link provided in the article is no longer active. We will contact the authors and reproduce the results from this paper in the future, using it as a baseline for comparison.\n\n> QuestionA: The BLEU-3 and BLEU-4 scores.\n\nThanks for your great advice! We tested the results and reproduced some of the baseline outcomes as follows: \n\n|         | Holmes BLEU-3 | Holmes BLEU-4 |arXiv BLEU-3 | arXiv BLEU-4 |\n|---------|--------|--------|--------|--------|\n| SRJT| 1.46   | 0.00      | 1.17   | 0.00      |\n| SFusion | 1.60    | 0.73   | 1.60    | 0.83   |\n| StyleDGPT| 1.88   | 0.63   | 2.69   | 1.08   |\n| KASDG | 3.39   | 1.48   | 5.72   | 3.25   |\n\nOur methods significantly outperform all baselines on BLEU-3 and BLEU-4 as we provide explicit style knowledge for each response instead of merely a style hidden.\n\n> QuestionB: Why the backward model needs to be jointly trained with other modules?\n\nThe reason is that jointly trained backward model, compared to fixed ones, can generate more diverse and dynamic pseudo data. By training on a more varied set of pseudo data, the model becomes more robust and alleviates the issue of copying.\nIn Appendix A.3, we conducted an ablation study on the effects of back translation, including whether to involve joint training, and whether we retrieve using context or response during training. As demonstrated in Table 9, joint training generally performs better in terms of content and style. \n\n> QuestionC: How is the \"context-aware attention\" mechanism integrated within the content generator?\n\nWe appreciate the reviewer for raising a question about the integration of the \"context-aware attention\" mechanism within the content generator. However, we suspect there might be a misunderstanding or typographical error in the question. It seems that the reviewer might be referring to \"content-aware attention\" instead of \"context-aware attention\". To clarify, “context-aware attention” is a mechanism we employed in the Feature-Guided Selection Module. Its primary purpose is to filter and extract useful style features under the guidance of various losses, as elucidated in Formula (4).\n\nOn the other hand, the \"content-aware attention\" and \"style-aware attention\" are integrated within the decoder. Both these attention mechanisms implement the cross-attention structure in transformers. To control the style and context contributions in each layer, we add a gate after the style-aware attention module. The equation of controller gate $CT$ is:\n\n$CT(h_s, h_c) = \\lambda \\cdot LN(h_s) + (1 - \\lambda) \\cdot h_c $ \n\n$\\lambda = \\sigma(w \\cdot [h_s; h_c])$\n\nWhere $h_s$ , $h_c$ denote the output of the style-aware attention and the residual from the previous block. $LN$ represents Layer Norm. $w$ is a learnable parameter and $\\sigma$ denotes sigmoid function.\n\n> QuestionD: What motivated the decision to incorporate the \"context-aware attention\" in the decoder subsequent to the context encoder?\n\nWe would like to first kindly point out that it is “content-aware attention” instead of “context-aware attention” in decoder. \"Content-aware attention\" is essentially the cross attention in the transformer decoder, which provides content information for response generation. While Feature-Guided Selection Module extracts style information useful for the dialogue, it will not cover the full content information in context. Hence, the decoder needs to generate response through a representation of the dialogue history (content-aware attention).", "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": "1. The authors meticulously designed distinct loss functions tailored to specific objectives within the framework.\n2. The proposed KASDG framework employs an unsupervised strategy, predominantly through the distance between textual representations, to fine-tune a retriever and generator to enhance generation.\n3. The performance of the KASDG appears to surpass the baseline.", "reasons_to_reject": "1. The baseline methods do not compare with the state-of-the-art in unsupervised retrieval-augmented generation techniques (e.g. RAG (Lewis, Patrick, et al., 2020) or ATLAS (Izacard, Gautier, et al., 2022))\n2. The case studies presented in this paper lack compelling evidence, and the colour schemes utilized in the figures require comprehensive elucidation.\n3. Incorporating external knowledge may introduce inaccuracies to the results and potentially lead to hallucination issues, a concern not addressed in this paper.\n4. The newer KDG methods are not considered in the baseline. e.g. DTR (Sun et al., NAACL 2022)\n\nReference:\n\nLewis, Patrick, et al. \"Retrieval-augmented generation for knowledge-intensive nlp tasks.\" Advances in Neural Information Processing Systems 33 (2020): 9459-9474.\n\nIzacard, Gautier, et al. \"Few-shot learning with retrieval augmented language models.\" arXiv preprint arXiv:2208.03299 (2022).\n\nQingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, and Daxin Jiang. 2022. Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3304–3318, Seattle, United States. Association for Computational Linguistics.\n\n", "questions_to_authors": "A. Could the authors provide the BLEU-3 and BLEU-4 scores for both the proposed method and the baseline approaches?\n\nB. Why the backward model needs to be jointly trained with other modules?\n\nC. How is the \"context-aware attention\" mechanism integrated within the content generator?\n\nD. What motivated the decision to incorporate the \"context-aware attention\" in the decoder subsequent to the context encoder?", "paper_topic_and_main_contributions": "This paper deals with stylized dialogue generation with feature-guided knowledge augmentation where the external knowledge is retrieved using a joint-tuned retriever (transformer encoder), therefore, during the inference, the content generator would generate the stylized content given the target style sentence.\n\nThe main contributions of this paper are:\n\n1. the introduction of the feature-guided selection module (retriever)\n2. the way to jointly train both the retriever and content generator via a multi-purpose combined loss.\n3. using a back-translation module to augment training data.", "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": "The manuscript does not consider the inclusion of the RAG series algorithms, which are prominently recognized in the retrieval-augmented generation domain. Also, the newer KDG work should be considered as well.\n\nReference:\n\nLewis, Patrick, et al. \"Retrieval-augmented generation for knowledge-intensive nlp tasks.\" Advances in Neural Information Processing Systems 33 (2020): 9459-9474.\n\nIzacard, Gautier, et al. \"Few-shot learning with retrieval augmented language models.\" arXiv preprint arXiv:2208.03299 (2022).\n\nQingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, and Daxin Jiang. 2022. Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3304–3318, Seattle, United States. Association for Computational Linguistics.\n"}}, {"review_id": "PVF7y5whOO", "reviewer": "Reviewer_ZsDS", "summary": "", "questions": "A. Did you run the experiments with multiple random seeds or test the statistical significance of the improvements in the ablation study?\n\nB. I appreciate the discussion and comparison with ChatGPT. However, I think it may be difficult for ChatGPT to understand the meaning of the target style given only a style word like *Holmes*, leading to a discrepancy between its generation and the downstream task data. Have you tried describing the target style with an explanation or adding one or more in-context examples in the prompt?", "limitations": "", "rating": 3, "confidence": 3, "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": "Thank you for the valuable comments that help us improve the work. We will respond to the questions, and we appreciate it very much if you kindly raise the scores when the concerns are addressed.\n\n> Q1: The actual effects of the feature-guided selection module and the ablated variants.\n\nThank you for your advice. First, we would like to point out that the feature selection module is effective in practice. Due to the limitations of the automatic evaluation metrics, the score improvement is not very large. However, the output shows superior results in manual evaluation. Taking Holmes dialogue as an example:\n\n*Context: if u were a soup what kind of soup would u be ?*\n\n*w/o CL: what would he like a curry and a cup of coffee ?*\n\n*w/o SRKL: a cup of coffee , a glass of water , a cup of tea , a coffee , and a cigar .*\n\n*KASDG: what would you recommend , mr holmes , a soup ?*\n\nIn this case, ommitting SRKL or CL loss causes a deviation from the topic “what kind of soup”. Also, incorporating them will make the response more stylistic, including the phrase “mr holmes” and the way of punctuation.\nSecond, our feature selection module is not complex in inference and already fits the posterior probability distribution into context-aware attention.\nFinally, We conducted the statistically significant using t-test:\n\n|                  | w/CL       | w/ SRKL    | w/ CL+SRKL |\n|------------------|------------|------------|------------|\n| P-value   | 0.00465| 0.00014 | 0.00002|\n\nThe result shows that these improvements are statistically significant with p-value<0.05. We will include the details in the revision. thank you!\n\n> Q2: The $\\lambda^*$ hyperparameters and the loss.\n\nWe appreciate your great effort to improve the robustness of our model. In our experiments, based on empirical experience, we directly set all $\\lambda^{*}$ in both Formulas (13) and (14) to 1.0, since it achieves good performance already. We also proposed a warmup strategy for $\\lambda_{s_{1}}$ in Formula (16) (Line 356). For $\\lambda_{s_{0}}$​ and $\\lambda_{bt}$​ in Formula (15), we adjusted the parameters through grid search on validation dataset to minimize the influence of hyperparameters. Training with multiple loss components is stable and converges well. For example, our model converges stably at 80k steps on Holmes dataset, with $\\mathcal{L_{s_{0}}}$​ and $\\mathcal{L_{s_{1}}}$​ converging to 2.7 and 2.8 respectively, and $\\mathcal{L_{bt}}$​ converging to 1.5.\n\n> Q3: i and j are used inconsistently in Equation (9) and (11).\n\nThank you for pointing out! We make the modifications as:\n\n$\\mathcal{L_{resp}}=\\log\\sum\\limits_{i=1}^{L_s}exp({h_s^i}^T \\cdot h_r)-\\log\\sum \\limits_{i=1}^{L_s}exp({h_s^i}^T \\cdot h_c)$\n\n> QuestionA: Multiple random seeds or test the statistical significance.\n\nYes, we run the experiments on 5 random seeds. Besides, we conducted the statistically significant using t-test and the scores are listed below:\n\n|                  | w/CL       | w/ SRKL    | w/ CL+SRKL |\n|------------------|------------|------------|------------|\n| P-value   | 0.00465| 0.00014 | 0.00002|\n\nThe results show that proposed module significantly outperforms baselines with p-value<0.05. We will include the details in the revision. Thank you!\n\n> QuestionB: Target style with an explanation or in-context examples in the prompt.\n\nThanks for your advice! We added style explanations in the prompt and provided more examples as illustrations, with the detailed listed as follows: \n\nFor style explanations, we provide two new prompts: \n\n*\"\"\"Prompt1: The Holmes style refers to the deductive and analytical investigative approach popularized by the fictional detective Sherlock Holmes,* \n*created by Sir Arthur Conan Doyle. This style emphasizes keen observation, logical reasoning, and attention to minute details to solve* \n*complex mysteries and crimes.* \n\n*Generate response in Holmes style. The response should be 1-3 sentences.*\n\n*Context: {context}*\n\n*Response: \"\"\"*\n\n*\"\"\"Prompt2: The Holmes style refers to the distinctive characteristics of the fictional detective Sherlock Holmes, created by Sir Arthur Conan Doyle.* \n\n*Generate response in Holmes style. The response should be 1-3 sentences.*\n\n*Context: {context}*\n\n*Response: \"\"\"*\n\nFor In-Context Learning, we adopt the classic in-context learning algorithm KATE [1] with 1,5,10 and 20 shot scenarios. Due to resource and time constraints, we randomly selected 100 tests to carry out our experiments, with results as follows: \n\n|                | BLEU-1 | BLEU-2 | Rouge-L | Dist-1 | Dist-2 | StyleIn. |\n|----------------|--------|--------|---------|--------|--------|---------|\n| Prompt1| 22.83  | 5.95   | 12.98   | 0.386  | 0.782  | 0.102   |\n| Prompt2| 21.18  | 7.25   | 14.06   | 0.398  | 0.809  | 0.125   |\n| ICL 1shot      | 23.27  | 8.00      | 13.44   | 0.337  | 0.714  | 0.141   |\n| ICL 5shot      | 25.48  | 9.11   | 13.55   | 0.353  | 0.735  | 0.164   |\n| ICL 10shot     | 26.10   | 10.16  | 14.63   | 0.366  | 0.745  | 0.164   |\n| ICL 20shot     | 25.21  | 8.92   | 13.24   | 0.376  | 0.747  | 0.250    |\n| KASDG          | 36.86  | 13.21  | 22.82   | 0.139  | 0.553  | 0.583   |\n\nAs mentioned in Appendix A.5, our model can achieve better results because: (1) Our Feature-Guided Selection Module provides clearer style signals and (2) Our model is trained on the given style corpus, whereas the distribution of pretrained style information in GPT-4's memory might not completely align with the given corpus. This gap cannot be resolved by simply using explanations or by providing a few style sentences as examples.\n\n[1] Liu, Jiachang, et al. “What Makes Good In-Context Examples for GPT-3?”. ACL2022.", "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": "1. The proposed idea to retrieve explicit style features from a target style corpus has not been studied for the stylized dialogue generation task, while it has been commonly used in other text generation tasks.\n2. The designs regarding filtering out irrelevant information from the retrieved response are intuitive.\n3. Experimental results have shown the effectiveness of the proposed model compared to previous work on two benchmarks.", "reasons_to_reject": "1. My main concern is the actual effects of the sophisticated components of the feature-guided selection module, including a contrastive loss, an SRKL loss composed of a direction loss, and a responsiveness loss. However, the ablation study shows relatively small (sometimes inconsistent across different metrics) improvements the full model made compared with the base model and the ablated variants, and it is unclear whether these improvements are statistically significant. For example, the results of **w/ SRKL** and **w/ CL+SRKL** seem very close.\n2. The proposed framework introduced a lot of $\\lambda_*$ hyperparameters that balance the effects of different losses. However, how these hyperparameters affect the results and how the values are chosen is unclear. I am also unsure whether the training with the multiple loss components is stable and how well each converges.\n", "questions_to_authors": "A. Did you run the experiments with multiple random seeds or test the statistical significance of the improvements in the ablation study?\n\nB. I appreciate the discussion and comparison with ChatGPT. However, I think it may be difficult for ChatGPT to understand the meaning of the target style given only a style word like *Holmes*, leading to a discrepancy between its generation and the downstream task data. Have you tried describing the target style with an explanation or adding one or more in-context examples in the prompt?", "paper_topic_and_main_contributions": "This paper tackles the problem of stylized dialogue generation, where a model needs to generate a response to a query based on a specific style. However, the parallel data is limited, so previous studies have to exploit unpaired style corpus to learn the style implicitly in the latent space or adopt back-translation to build pseudo pairs. Such methods suffer from the noisy and context-agnostic style signals, as the style is not explicitly provided for each response. This paper makes the following contributions:\n\n(1) Introduces an explicit usage of style signals by retrieving similar responses from an external target style corpus.\n\n(2) Proposes a feature-guided selection method to refine the retrieved style feature and filter irrelevant information, including a response-related contrastive learning loss to make the filter style feature close to the response and a Style Responsiveness Kullback-Leibler (SRKL) loss to filter the retrieved style feature based on both the context and the response.\n\n(3) Experiments on two benchmarks with four styles demonstrate that the proposed framework based on BART can generate satisfactory responses with desired styles, showing superior performance over a few baselines.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. $i$ and $j$ are used inconsistently in Equation (9) and (11)."}, "extra_text": {}}, {"review_id": "Snxx9QtY5h", "reviewer": "Reviewer_Dqb9", "summary": "", "questions": "1.  what is the metric \"StyleIn\" in table 1 and table 10?\n\n2. Is the interactive training process used in SRJT used in this method?\n\n3. Why not report the classification score of SVM in table 4? Why not report the classification score of the SVM and BERT classifer in Table1?", "limitations": "", "rating": 3, "confidence": 4, "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": "Thank you for the valuable comments that help us improve the work. Below we address the concerns mentioned in the review:\n\n> Q1: Not all baselines are used for all datasets. \n\nWe appreciate your great effort for completing our experiments. During our literature review, we found that there are mainly two groups of style datasets:\n\n(1) SFusion and StyleDGPT are tested on the Holmes and arXiv datasets, while SRJT and MPDL are tested on the formal and informal datasets. We showcased our state-of-the-art results on both these datasets. \n\n(2) We followed the baseline results from previous papers, where neither SRJT nor MPDL were tested on Holmes and arXiv. We reproduced SRJT across four styles, but its performance on arXiv and Holmes was poor, so we didn't report it in the paper, the results as follows:\n\n |       | BLEU-1 | BLEU-2 | \n|-------|------|------|\n|Holmes SRJT | 21.98 | 6.15 | \n|Holmes  KASDG| 36.86 | 13.21 | \n|arXiv SRJT | 13.74 | 4.23 | \n|arXiv  KASDG| 35.11 |15.02 | \n\n(3) MPDL requires a style transfer dataset, making it impossible to reproduce on Holmes and arXiv, and furthermore, the code hasn't been made public. It's worth highlighting that our method doesn't require a style transfer dataset, resulting in less data dependency and better generalizability.\n\n\n> Q2: Some analysis of the retrieved stylized sentences. Is the \"Retreival\" row in Table5 the retrieved stylized sentence?\n\nYes, the \"Retreival\" row in Table5 represents the retrieved stylized sentence. We demonstrate an example in Figure 1, which shows how our model integrates stylistic words from different retreived sentences to generate stylized response. \n\nMoreover, we conduct experiments on the contribution of retrieved sentences from the perspective of stylized words coverage. In detail, we evaluated the coverage of words in both references and generated sentences from the Holmes dataset. We first apply RAKE [1] algorithm to extract key stylized words. Then we compute the word coverage of our retrieved top-k sentences (k=1,5,10,20). Additionally, the coverage of important words offered by our context was analyzed. The results are listed as follows:\n\n|       | Top1 | Top5 | Top10 | Top20 | Context |\n|-------|------|------|-------|-------|---------|\n| Reference | 0.69 | 1.75 | 2.64  | 4.03  | 2.58    |\n|Generation | 0.67 | 1.48 | 1.94  | 2.4   | 1.49    |\n\nThe table shows that the generation process makes use of the retrieved sentences, thus improving the performance of the generated stylized dialogue responses. We will defintely include this in the revision.\n\n[1] Rose, Stuart, et al. \"Automatic keyword extraction from individual documents.\" Text mining: applications and theory (2010): 1-20.\n\n> Question1: The \"StyleIn\" in table 1 and table 10?\n\n\"StyleIn\" stands for Style Intensity. For consistency, we followed the naming convention from Table 2 in StyleDGPT. Style Intensity refers to the score of a style discriminative model based on GPT2.\n\n> Question2: Is the interactive training process used in SRJT used in this method?\n\nYes, we adopt the interactive training process in our method.\n\n> Question3: The SVM in table 4 and the SVM and BERT classifier in Table1.\n\nThank you for bringing this to our attention. Table 4 presents our ablation study on Holmes dataset, while prior studies have also refrained from using SVM on Holmes dataset. We find limited style classification capability of SVM compared to BERT in our initial experiments, so we only include the BERT classification results in Table 4.\n\nTable 1 showcases performance of our models and baselines on the arXiv and Holmes datasets. We also followed two prior papers, Sfusion and StyleDGPT to use Style Intensity to calculate the style scores. Furthermore, it is reported in StyleDGPT that style intensity is more accurate due to the capabilities of GPT-2 (Section 5.1 footnote 8), so we only import the Style Intensity score.\n\nIn order to reduce these confusions, we will unify the indicators as much as possible in the revision.", "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": "1. The investigated problem is interesting.\n\n2. The compare between the proposed model and ChatGPT looks promising.", "reasons_to_reject": "1. The evaluation of the proposed method is not solid. Specifically, not all baselines are used for all datasets. Some baselines are missing from table 1 and table 2. For example, why not implement SRJT and MPDL for arxiv and holmes style?\n\n2. It is expected to see some analysis of the retrieved stylized sentences. How does these retrieved sentences improve the performance of the generated stylized dialogue responses? is the \"Retreival\" row in Table5 the retrieved stylized sentence?\n", "questions_to_authors": "1.  what is the metric \"StyleIn\" in table 1 and table 10?\n\n2. Is the interactive training process used in SRJT used in this method?\n\n3. Why not report the classification score of SVM in table 4? Why not report the classification score of the SVM and BERT classifer in Table1?", "paper_topic_and_main_contributions": "This paper try to build a stylized dialogue model using non-stylized dialogue data and stylized non-dialogue texts. The proposed method is built on top of a back-translation process, i.e., the author use a reverse dialogue model to produce possible dialogue contexts to the stylized responses and use these pseudo dialogue pair to train the proposed model.The authors use retrieved text to enhance the stylistic features exhibited in the generated responses. The author also introduce a set of lossed to enhance the hidden space of the retrieved stylized texts. Specificlaly. a  contrastive learning loss is used to enhance the retrieval performance and a KL loss is used to enforce similiar attention distribution between different queries. ", "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": {}}]

Author rebuttal:

Meta-review: The detailed and careful design of learning objectives in the proposed KASDG framework tailored to specific objectives is a key contribution of the paper. 

As one reviewer pointed out, the paper was missing several important references (RAG, ATLAS, and arguably DTR). While the author provided some comparison (both empirical and theoretical) during rebuttal, these put the thoroughness of the paper's literature review questionable.|meta review
Decision: Accept-Findings