Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Authors: Zhiqiang Hu
Nancy F. Chen
Roy Ka-Wei Lee
Venue: EMNLP 2023 Findings (2023)
Keywords: Text style transfer
Parameter-efficient
Adapter

Abstract: Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the substantial amount of computational resources and labeled data required for the specific downstream task. In this paper, we address this challenge by introducing \textsf{Adapter-TST}, a framework that freezes the pre-trained model's original parameters and enables the development of a multiple-attribute text style transfer model. Using BART as the backbone model, \textsf{Adapter-TST} utilizes different neural adapters to model different types of attribute information, similar to a plug-in connected to BART. Our method allows control over multiple attributes (e.g. sentiment, tense, active or passive voice) and configures the adapters' architecture to generate multiple outputs in respect to attributes or compositional editing on the same sentence. We evaluate the proposed model on both traditional sentiment transfer and multiple-attribute transfer tasks. The experiment results demonstrate that \textsf{Adapter-TST} outperforms all the state-of-the-art baselines with significantly less computational resources. We have also empirically shown that each adapter is able to characterize specific stylistic attributes effectively and can be configured to perform compositional editing.

Adapter-TST: A Parameter Efficient Method for Multiple-Atribute Text Style Transfer
Zhiqiang Hu' Roy Ka-Wei Lee' Nancy F. Chen²
Singapore University of Technology and Design, Singapore 2 Institute of Infocomm Research (I2R), A*STAR, Singapore
Abstract
Opponents don buy azguments.
Adapting a large language model for multiple- attribute text style transfer via fine-tuning can be challenging due to the substantial amount of computational resources and labeled data re- quired for the specific downstream task. In this paper, we address this challenge by introduc- ing Adapter-TST, framework that freezes the pre-trained model's original parameters and enables the development of a multiple-attribun text style transfer model. Using BART or T5 backbone model, Adapter-TST uti- lizes different neural adapters to model dif- ferent types attribute information, similar a plug-in connected to the base model. Our method allows control over multiple at- tributes (e.g. sentiment, tense, active or pas- sive voice) and configures the adapters' ar- chitecture to generate multiple outputs in re- spect attributes or compositional editing on the same sentence. We evaluate the proposed model on both traditional sentiment transfer and multiple-atttrbuu transfer tasks. The exper- iment results demonstrate that Adapter-TST outperforms all the state-of-the-an basclines with significantly less computational resources. We have also empirically shown that each adapter able to characterize specific stylis- tic attributes effectively and can be configured to perform compositional editing. The code and datasets can found https: //tgt/tttttttt////////////tttggt com/Social-AA- www.-I-Studdooodpterr-errr
arguments bought by opponenta.
Such arguments will not be bought by opponents.
(x) Sequential Editing
Opponents don buy auch arguments.
Opponents did not buy such azguments.
Opponents will not buy such arguments.
Such azguments aren't bought by opponents.
(b) Multiple Stylistic Attribute Outputs
Opponents don't buy auch arguments.
Such arguments
bought by opponents.
(c) Compositional Editing
Figure I: Examples of different settings of multiple- attribute TST. (a) Existing single- aatlel-ttribbte TST models perform sequential editing by transferring the text style sequentially to achieve compositional editing. Multiple- attribute TST models can (b) generate multiple outputs simultaneously in the corresponding target style, or (c) perform compositional editing by transferring different target styles. The proposed Adapter-TST enables a single PLM to achieve both settings (b) and (c) by con- figuring the adapters' connection method.
author writing style (Syed et al., 2020). Neverthe- less, most of the existing TST studies are confined to single-attribute TST tasks.
Few works have explored multiple-attribut TST tasks, where TST models are designed con- trol and transfer text in multiple target stylistic attributes. Lample et al. (2019) attempts style transfer with multiple attributes by conditioning on the average embedding of each target attribute and using a combination of denoising autoencoder (DAE) and tracssrrnslttion techniques. Goyal et al. (2021) propose an approach initialize an encoder- decoder setup with a transformer-bant language model that is pre-trained on generic corpus and enhances its capability of re-writing to multiple target style dimensions by utilizing multiple style-
1 Introduction
Motivation. Text style transfer (TST) is a popu- lar natural language generation task that aims to change the stylistic properties (e.g., sentiment, for- mality, tense, voice) of the text while preserving the style-independent content (Hu al., 2022a). Exist- ing studies explore performing text style transfer on attributes like age, or gender (Lample al., 2019), sentiment (Li et al., 2018; Luo et al., 2019; Fu et al., 2018), formality (Rao and Tetreault, 2018), polite- ness (Madaan et al., 2020; Hu et al., 2022b), and

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A possible approach perform single and mul- tiple attribute TST tasks to leverage large pre- trained language models (PLMs). The PLMs have been pre-trained on large corpora, which allows them to capture natural language's s syntactic and semantic information. This characteristic of PLMs makes them well-suited for TST tasks, where the model needs to understand the content and style of aware language models as discriminators.
to transfer and generate high-quality text with lower computational resources.
Contributions. We summarize our contribu- tions as follows: (i) We introduce an Adapter- TST, which is a parameter-efficient framework that can perform multiple-attribute TST tasks with significantly lower computational resources. (ii) Included in the Adapter-TST are two TST con- figurations, parallel and stacking, which support multiple-ouiput TST and compositional editing. re- spectively. (iii) We conducted extensive experi- ments on real-world datasets. The automatic and human evaluation results show that Adapter-TST can outperform the state-of-the baselines to transfer and generate high-quality text.
the input text. Syed al. (2020) fine-tune denois- ing autoencoder (DAE) for the stylized re- writing task by initializing the encoder and decoder with a pre-trained language model trained on Masked Lan- guage Modeling (MLM) objectives (Devlin et al.. 2019). Wang et al. (2019) fine-tune GPT-2 model (Radford et al., 2019) using the text formality trans- fer rules harnessed from analyzing the GYAFC parallel dataset (Rao and Tetreault, 2018). The subsequently used
Related Work
2.1 Text Style Transfer
fine-tuned GPT-2 model transfer the formality of text (e.g., informal formal text). However, fine-tuning PLMs for multiple-attribute TST remains challenging as a significant amount of computational resources and style-labeled data are required to perform TST for each stylistic attribute.
TST is an emerging research topic that has gar- nered attention from computational linguists and computer science researchers. The recent compre- hensive survey (Hu et al., 2022a; Jin et al., 2022) summarizes the existing TST approaches.
While the majority of existing studies have fo- cused on performing TST on single attributes such as sentiment (Li et al., 2018; 1: Luo et al., 2019; Fu et al., 2018) or formality (Rao and Tetreault, 2018), recent studies have also explored multiple-attribute TST tasks, where TST models are designed to control and transfer text in multiple target stylis- tic attributes. Lample et al. (2019) attempts style transfer with multiple attributes by conditioning on the average embedding of each target attribute and using a combination of denoising autoencoder (DAF) and back-translation techniques. Goyal et al. (2021) propose an approach to initialize an encoder-decoder setup with transformer-basee language model that is pre-trained on a generic corpus and enhances its capability of re-writing multiple target style dimensions by utilizing multi- ple style-aware language models as discriminators. In this study, we contribute to this limited multiple- attribute TST literature by proposing an alternative approach to generate multiple stylistic outputs and perform compositional editing efficiently.
Research Objectives. To address these research gaps, we propose Adapter- a parameter- efficient framework that utilizes BART (Lewis et al., 2020) or T5 (Raffel et al., 2020) the back- bone model and trains neural adapters to capture multiple stylistic attributes for multipl--atribute TST. During the training of Adapter-TST, we freeze the original parameters of the pre-trained BART or T5 model and only update the parameters of adapters to relax the dependence on computa- tional resources and supervised data. The proposed Adapter-TST model is flexible to handle different settings of multiple-attribus TST by configuring the connection method among adapters. Figure il- lustrates the different settings of multiple-atrribut TST tasks. Paralleling the adapters Adapter- TST can generate multiple outputs in the corre- sponding target style simultaneously (setting b) and stacking the adapters for compositional editing in terms of different target styles at the same time (setting c). We conduct experiments on the tradi- tional sentiment transfer task and multiple-atrribute TST tasks, including multiple stylistic attribute out- puts and compositional editing. Results of auto- matic and human evaluations show that Adapter- TST can outperform the state-of of----f-thhhe of-the-art baselines
Due to the lack of parallel training data, most existing TST methods are designed to train with non-parallel style-labeled sentences as input. A popular line of TST approaches aims to disentan- gle the text's content and style in the latent space to perform TST (Shen et al., 2017; Zhao et al., 2018;

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Fu et al.. 2018; Chen et al., 2018; Logeswaran
al., 2018; Yin et al., 2019; Lai et al., 2019; Vi- neet et al., 2019). Another common approach to leverage PLMs. For instance, Syed et al. (2020) fine-tune a denoising autoencoder (DAE) for the stylized re-writing task by initializing the encoder and decoder with pre-trained language model trained Masked Language Modeling (MLM) ob- jectives (Devlin al., 2019). Wang et al. (2019) fine-tune GPT-2 model (Radford al., 2019) us- ing the text formality transfer rules harnessed from analyzing the GYAFC parallel dataset (Rao and Tetreault, 2018). The fine-tuned GPT-2 model was subsequently used to transfer the formality of text (e.g., informal to formal text). However, fine- tuning PLMs for multiple-attribute TST remains challenging significant amount of computa- tional resources is required perform the task; multiple PLMs need to be fine-tuned for the differ- ent attributes to perform multiple-attribute TST. In this study, we overcome this limitation by propos- ing Adapter-TST, which a parameter-efficient framework that leverages on PLMs but requires significantly lesser computational resources to per- form multiple-attribut TST.
Adapter
Layer
Feed Forward up-project
Nonlinearity
Feed Forward down-project
Figure 2: Structure of the adapter layer. The adapter layer consists of bottleneck with up and down projec- tion layers, and skip connection between projec- tion layer.
Methodology
This section proposes Adapter-TST, which adds neural adapters into each transformer layer cap- ture different attribute information for multiple- attribute TST. We first introduce the adapter struc- ture used in Adapter- and its parameter effi- ciency. Subsequently, we explain how the adapters are configured for different multiplc-attribute TST settings. namely, multiple stylistic attribule oulputs and compositional editing. Finally, we describe the training objectives of Adapter-TST.
2.2 Adapter-based Models
3.1 Adapter Structure
PLMs, pre-trained on large-scale text corpus with unsupervised objectives, have established state-of- the-art performances on various NLP downstream tasks. Many studies fine-tune PLMs with lan- guage modeling and downstream task objectives to obtain better performance (Zhang et al., 2019b; Lauscher et al.. 2019; He et al., 2019; Xiong et al.. 2019).To leverage the powerful PLMs more ef- ficiently, Houlsby et al. (2019) add adapter lay- ers, small neural networks, into each transformer layer obtain near state-of-the-at performance on the GLUE benchmark while updating only the parameters of adapters. Inspired by this work. more adapter-based models (Wang et al., 2021; Liu et al., 2021: Zhong et al., 2021) are proposed to inject task-specific knowledge into PLMs with adapters. Inspired by the adapter architecture, we propose Adapter-TST, which trains different neu- ral adapters capture different stylistic attributes perform the multiple-attibute TST. The pro- posed adapter framework has two configurations that support multiple stylistic attribute outputs and compositional editing.
We present an adapter structure in Figure 2. The adapter consists of bottleneck that contains few parameters relative to the attention and feedforward layers in the original model. skip connection is applied across two projection layers. In our pro- posed Adapter-TST, these adapters will be trained to capture different stylistic attributes. In contrast to Houlsby et al. (2019), adding the adapter module twice to each transformer layer, propose simpli- fying the approach by just adding the adapter layer into each transformer once, making our Adapter- TST's s architecture more parameter efficient. We use BART-large (24-layer, 1024-hidden, 16- heads, 406M parameters) or T5-large (24-layer, 1024-hidden, 16-heads, 770M parameters) as the backbone model in Adapter-TST. As for each adapter layer, denote the hidden dimensions of the down-projection and up-projection layers Hg = 64 and H, 1024. The bottleneck adapter layers are plugged into each layer of BART-large or T5-large, and different adapter layers do not share parameters. Thus the total number of parameters

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Tence Cassiiee
Add 4 Nerm
Classiior
ourputs
Lyee
Concarinats
Lds
Plosiva
Active
Norm
Concatonate
Coppy N Adaple
Concatorore
Insteme
Part
Artire
FF Up
FF Up
FF Up
Prossi
Down
Down
Down
Coppy Adapter
Ads & Nomm
Ado
Adc Norrm
Head
Head Attemtion
(a) Parallel Connection
(b) Stack Connection
Figure 3: Adapter-TST Configurations Left: Paralleling the adapters enables single PLM to model different attributes simultaneously and generate multiple outputs in the corresponding target style. Right: Stacking the adapters for compositional editing terms of different target styles the same time, Stack connection is used for inference to verify the relevant attribute information captured by adapters.
for each attribute- spreiteeeppieiiccce adapter is about 3. 3.17M, which only 0.78% of the original BART-large model and 0.41% of the T5-large model, making the training process parameter efficient. Note that the original parameters of BART-large or T5-large are frozen during multiple-atrribute TST training, and only the parameters of adapters trainable and initialized randomly.
ple stylistic attribute outputs as follows: given a sentence x = {",,..,, with n tokens and ytense: Pvoice labels, the Adapter-TST model is required to generate multiple outputs with all pos- sible other attribute values at the same time. For instance, as shown in Figure (b), given an input sentence with present tense and active voice, the multiple-attribute TST models need to generate three sentences in the past tense, future tense, and passive voice simultaneously. The multiple stylis- tic attribute output setting requires TST models to capture all the stylistic attributes and have the capability of performing style transfer among the attribute values. Adapter- performs the multi- ple stylistic attribute output by utilizing the Paral- lel connection configuration shown in Figure 3(a). Specifically, we plug the paralleled adapters Par- Future, Past, Present, Passive, Active) into each transformer layer of the base model. Dur- ing training, each training sample passes all the attribute-specific adapters, but adapters will take different actions according to the attribute values of input sentences. The adapter learns to recon- struct the input sentence for training samples with the same attribute value as an adapter. Conversely, when training samples with different attribute val-
3.2 Adapter-TST Configurations
Ada has two configurations, parallel and stack, which support two multiple-atrribute TST task settings: multiple stylistic attribute outputs and compositional editing, respectively. To better derstand the two Configurations of Adapter-TST, we take the multiple-atrribute and voice attributes as an example. Tense has three attribute values (Future, Past, Present), while Voice has two attribute values (Passive, Active), Thus, we add five attribute-peecific adapters Adapter( (elpter(Futtue, Past, Present, Passive, Active) the base model for all the possible attribute values, respectively. Each adapter is employed to learn to generate sen- tences with corresponding attribute values while preserving the semantic content of the inputs.
attribute TST task with tense
Parallel Connection. We define the multi-

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ues, the adapter learns to transfer the attribute of the imput sentence while preserving the original content. The outputs of all the adapters are concate- nated together to the next layer. The replication is only performed once in the first transformer layer. In the latter transformer layers, we distribute the hidden states to corresponding adapters to make sure that the input of an adapter in the current layer is the output of the adapter with the same attribute value in the preceding layer.
tribute classifier D for each attribute, then apply the pre-trained attribute classifiers to guide the up- dates of adapters' parameters such that the output sentence is predicted to be in the target style:
Lds (ite:))=DlloP((.ll//
(1)
where x' is sampled from the distribution of model outputs at each decoding time step, and ye is the target attribute value. Policy gradient algorithm (Sutton et al., 1999) is used to for discrete training with the attribute classifiers.
Stack Connection. Compositional editing re- quires TST models to change multiple attributes simultaneously while preserving the original con- tent. For instance, as shown in Figure I(c), given an input sentence with present tense and active voice, the multiple-atrribute TST models need to gener- ate one sentence both in future tense and passive voice. AdS performs compositional edit- ing by using the Stack connection method shown in Figure 3(b), where adapters belonging to the same attribute are parallelized because sentence should only contain one attribute value for specific at- tribute. Specifically, we have Parallel(Future, Past, Present) and Parallel(Passive Active) for tense and voice attributes. The two sets of paralleled adapters are stacked as Stack{ Parallel( Future, Past, Present), Parallel( Passive, Active)) to learn to transfer mul- tiple attributes. Similar to the Parallel connection method, the hidden states are replicated accord- ing the number of adapters in the Parallel con- nection module. It's worth noting that, demon- strate the sitribute-specffic adapters captured the attribute information, we only use the Stack connec- tion method in inference time. During inference, we reload the parameters of adapters trained in mul- tiple stylistic attribute outputs tasks and change the connection among the adapters to Stack.
Reconstruction Loss Lyee: The reconstruction loss attempts to preserve the original content infor- mation in the transferred sentences. Specifically, the loss function constricts the adapters to capture informative features to reconstruct the original sen- tence using the learned representations. Formally, we define Lree as follows:
(2) where is the i-th attribute value of the input sentence, z; denotes the hidden representation ex- tracted by the corresponding adapter. The input sentences are only reconstructed by the correspond- ing adapter and transferred by other adapters.
Lree = -logP(ar 2i, yi)
Putting them together, the final joint training loss L is as follows:
L= - -)- + AL
(3)
Where À is a balancing hyper-parameter to en- sure that the transferred sentence has the target style while preserving the original content.
4 Experiments
4.1 Experiment Setting
Datasets. We evaluate the proposed Adapter- TST model on sentiment transfer and multiple- attribute TST tasks using the Yelp' and StylePTB (Lyu et al., 2021) datasets, respectively. We adopt the train, development, and test split for the Yelp dataset as (Luo et al., 2019). Lyu et al. (2021) introduce StylePTB 2, large-scale benchmark with compositions of multiple-attribute TST tasks which allow the modeling of fine-grained stylistic changes. In our experiments, we choose four sub- sets for multiple-attribute TST: Tense- Tense- PP- Front-+Back ADIADV-Removal. Specifically, the four subsets
3.3 Training Objectives.
The TST task aims to transfer the style of in- puts while preserving the original semantic con- tent. Thus, we train AST with classifica- tion loss Lots for style transfer and reconstruction Lree for content preservation. During training, the original parameters of BART-larre or Ta-large are frozen, and only the parameters of adapters are trainable.
Classification Loss Las: The classification loss ensures that the transferred sentence conforms to the target attribute value. To this end, we first pre-train a TexICNN-based (Kim, 2014) binary at-
PP-Removal and Tense-
https://gituub..om//uuuuuuu chtnps//githu..co////yiieee////

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Dataset
Train
Dev
Test
Model
ACC
PPL.
Yelp
44.3K
1,000
1,000
BackTrans
94.5
0.88
11.3
Tense-voice
28K
1,538
1,564
CrossAlign
74.3
0.89
35.3
1.23
Tense- PP- Fronte-Back Back
5K
270
284
DeleteOnly
87.6
0.91
36.4
1.30
Tense-PP Removal
Pense-PP-Removal
32K
1,796
1,834
Rel&Retri
90.2
0.91
34.0
1.34
Sllae-DDAAD--Rmoooe
33K
1,838
1,819
DualRI.
88,9
0.95
27.1
1.46
Template
83.7
0.92
47.2
1.18
Table I: Dataset statistics for Yelp and StylePTB.
Unpaired
50.6
0.91
53.1
0,95
UnsuperMT
96.2
0.93
33.5
1.39
Style Transformer
85.8
0.95
10.1
2,00
Tddapeer-TS--BAR
90.1
0.91
8.2
2.15
include five attributes, tense with three attribute val- ues (Future, Past, Present), voice with two attribute values (Passive, Active), proposition position with two attribute values (Front, Back), proposition re- moval with two attribute values (Adding, Remov- ing), and adjectives&adverbs removal with two at- tribute values (Adding, Removing). Table shows the training, validation, and test splits of the Yelp and StylePTB datasets used in our experiments. Baselines. For sentiment transfer, we bench- mark Adapter-TST against nine state-of-the-an art TST models: BackTrans (Prabhumoye et al., 2018)) CrossAlign (Shen et al., 2017), DualRL (Luo et al.. 2019). Unpaired (Li et al.. 2019), UnsuperMT (Zhang et al., 2018), Style Transformer (Dai et al.. 2019), DeleteOnly, Template, and Rel&Retri (Li al., 2018). For multiple stylistic attribute out- puts task, Style Transformer (Dai et al., 2019), transformer-baser model for single-attribute TST. is selected as a baseline. We train multiple Style Transformer models for each attribute and perform style transfer separately. For compositional edit- ing, we use the trained Style Transformer models to perform sequential editing, which transfers one attribute after another to compare results with our model. We term this baseline Sequential Style Transformer setup.
Adapter-TST-T5
97.3
0.89
4.8
2.62
Table 2: Performance of models Yelp dataset (Senti-
ment Transfer Task). The best performances are bold.
fer accuracy (ACC) of the sentences' transferred attributes by considering the target attribute value as the ground truth. To quantitatively measure the amount of original content preserved after style transfer operations, we employ BERTscore (Zhang et al., 2019a) between style-transferred and origi- nal sentences. For fluency, We use GPT-2 (Radford et al., 2019) to measure the perplexity (PPL) of transferred sentences. The sentences with smaller PPL scores are considered more fluent. Finally, we compute the geometric mean of ACC, BERTscore, and 1/PPL. We take the inverse of the calculated perplexity score because a smaller PPL score corre- sponds to better fluency. When there is more than one accuracy in the multiple-an atttibbbe TST tasks, use the average accuracy to compute G-score.
4.3 Automatic Evaluation Results
Table 2 shows the performance of the Adapter- TST model and the baselines on the sentiment transfer task. Adapter-TST has achieved the best G-score, outperforming the baselines. We observe that Adapter-TST achieves comparable perfor- mance on transfer strength and content preservation with 97.3% transfer accuracy and 0.89 BERTscore by only updating the parameters of adapters. With the impressive generative ability of the pre-trained BART-large and T5-large models, the Adapter- TST model can generate high-quality text terms of fluency and completeness. The experiment re- sults demonstrate Adapter-TST's ability to per- form TST well and efficiently with fewer training parameters.
Training, The experiments were performed on an Ubuntu 20.04.3 LTS system with 24 cores, 128 GB RAM, and Nvidia RIX 3090. The model implementation is based on AdapterHub (Pfeiffer et al., 2020) and Huggingface Transformers (Wolf al., 2020). For the balancing we choose the best-performed one from (0.9, as the BART-large and Ta-le models can copy the input without training with TST objectives.
X,
4.2 Automatic Evaluation
We evaluate the proposed model and basclines three criteria commonly used in TST studies: trans- fer strength, content preservation, and fluency. An attribute classifier first pre-trained to predict the attribute label of the input sentence. The classifier is subsequently used to approximate the style trans-
Table 3 presents the results of the proposed Adapter-TST model and Style Transformer base- lines for the multiple stylistic attribute output task. Our Adapter-TST model achieves the high- est G-score across all four datasets by simultane- ously modeling multiple attributes using different

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Tense-Voite
Tense-ADIADY
EDDIADV-Removs
Model
Attri
Tense
Voice
BS
PPL
Tense
Removal
BS
PPL
G
Style Transformer
single
91.1
0.91
13.3
1.76
92.6
0.92
27.0
1.47
Style Transformer
single
87.2
0.85
83.7
0.93
21.7
1.53
Adapter-TST-BART
multi
96.9
81.9
0.96
4.7
2.63
96.2
76.5
0.95
11.8
1.91
Adapter-TST-T5
multi
95.9
83.4
0.94
2.6
3.19
95.7
85.2
0.91
2.79
Lense-PP-Frroe
Back
Jense-PP-Removee
Model
Attri Tense
Fe-Back
BS
PPL
G
Tense
Removal
BS
PPL
G
Style Transformer
single
95.7
0.83
6.8
2.27
94.9
0.91
27
1.47
Style Transformer
single
57.2
0.83
10.4
1.66
87.2
0,91
26.1
1.45
Adapter-TST-BART Adapier-TST-T5
multi
88.2
48.9
0.96
4
2.54
96
74.5
0.96
12.5
1.87
multi
75.9
48.2
0.83
1.7
3.12
95.6
95.0
0.92
3.8
2.85
Table Automatic evaluation results of models on multiple stylistic attribute outputs task, The best performances are bold.
Tense-Voice
Tense-ADIADV Remoov-Removal
Model
Tense
Voice
BS
PPL
Tense
Removal
BS
PPL
G
Sequential Style Transformer
80.2
88.1
0.85
22.2
1.48
88.6
90.0
0.89
42.2
1,23
TST--Rr
88.2
85.4
8.0
2.14
88.9
92.7
0.86
22
1.53
Adapter-TST-T5
75.6
73.1
6.5
2.18
85.4
77.1
0.87
12.1
1.80
Tense-PP
Tense- Pense-PP-Remmne PP-Revva
Model
Tense
F-+Back
PPI.
Tense
Removal
BS
PPL.
Sequential Style Transformer
76.1
65.7
0.82
8.1
1.93
91.2
85.7
0.88
51.4
1.15
Adapter-TST-BAn
88.2
50.0
0.92
4.9
2.35
90.1
88.2
0.86
20,9
1.54
Adapter-TST-T5 73.5
51.4
0.88
1.8
3.13
82.1
81.7
0.91
19.7
1.56
Table 4: Automatic evaluation results of models compositional editing task. The hest performances bold.
adapters. Adapter-TST performs well in trans- ferring tense attributes, surpassing the baselines on three datasets. However, modeling multiple at- tributes together proves to be more challenging task. While Adapter-TST exhibits slight per- formance gap compared to the Style Transformer model in terms of transfer accuracy, it excels in generating fluent and coherent sentences while pre- serving the original content. This advantage allows Adapter-TST to outperform the baselines in con- tent preservation and fluency. It is also worth not- ing that training multiple Style Transformers for the multiple-attriben TST tasks is computationally inefficient and expensive, unlike AdSpter-TST.
stylistic attribute outputs task is 2.24, significantly higher than compositional editing's average G- score of 1.89. The difference in the average G- score highlights the challenge of the compositional editing task. Interestingly, Adapter-TST achieves comparable performance style transfer accuracy over attributes, indicating that the sttribute-speciff adapters effectively capture the stylistic attributes.
4.4 Human Evaluation
We conducted a human- based evaluation study to assess the performance the Adapter-TST model in handling multiple-atribute TST tasks. The study involved randomly sampling 200 sentences from the Tense- dataset. Both Adapter-TST and the baselines used generate multiple stylis- tic attribute outputs and perform compositional editing on the sampled sentences. Two linguis- tic researchers evaluated the generated sentences based on three criteria used automated evalu- ation. To measure transfer strength, evaluators indicated whether the sentences were in the target attribute value (e.g., future tense, passive voice) us- ing true/false indicator. For content preservation. evaluators rated the amount of preserved content on 5-point Likert scale, ranging from no content preserved (1) to all content preserved (5). Fluency was assessed on a 5-point Likert scale, where represented unreadable sentences with numerous
To demonstrate that the attribute-spent speciffc adapters capture the corresponding attribute infor- mation, evaluate the proposed AST model on the compositional editing task. Note that the parameters of adapters trained the mul- tiple stylistic attribute outputs task are reloaded, and the connection method is changed to Stack for compositional editing. Table 4 shows the perfor- mance of the Adapter-TST and Sequential Style Transformer the compositional editing task. The Adapter-TST model achieves the highest G-score across four datasets, similar to the results obtained the multiple stylistic attribute output task. We observe that the average G-score of the multiple

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Tense- Voice
Model
Attribute
Tense 90.0
Voice
PPL
G
Style Transformer
Tense
3.72
3.23
10.26
Style Trunsformer
Voice
74.0
2.16
2.42
7.29
Adapter- (ours)
Tense+ Voice
99
67.0
3.74
3.58
10.35
Sequential Style Transformer
Tense+ Voice
81.0
85.0
2.56
2.88
8.49
Adapter-TST (ours)
Tense+ Voice
93.0
82.0
3.19
3.00
9.43
Table 5: Human evaluation results of models both multiple stylistic attribute outputs and compositional editing
tasks. The best performances are bold.
Target Style
Source Sentence
Style Transformer
Adapter-TST
Future
The plan lacked a withdrawal timetable.
The plan will lack be had by the buy-out group.
The plan will have a withdrawal timetable.
Past
Some issues will he helped hy higher carnings. And he will question the white house dedication.
Some issues were helped by higher earnings some issues. And he question the white house and he
Some issues were helped by higher earnings.
Present
And he says the white house ded- ication.
Future+
Litigation sciences doesn't make moral distinctions.
Litigation transportation will make fuel had and this teaches and she will be had by either.
Moral distinctions will be done by Litigation sciences.
Past+Active
Third high yields are offered by them.
Third nutmeg yiclds listed night board them third period carnings stage
Third high yields offered them.
Table Qualitative results transfer to different target style combination across different models. Different colors highlight the transferred segments contributing to the target style.
grammatical errors, and indicated perfect and flu- ent sentences. To reduce biases, the model names were concealed, and the order of the models was randomized when displaying the generated sen- tences. This ensured that evaluators were unaware of which model generated the sentences they were evaluating.
Case Study
We conducted case studies to showcase the style transferred outputs of both the Adapter-TST and Style Transformer models. Randomly sampled ex- amples and their corresponding outputs are pre- sented in Table 6, specifically for the Tense-Voice dataset. Our findings reveal that Adapter suc- cessfully transfers the style while preserving the content and sentence structure in multiple-attribtte TST tasks. In contrast, the Style Transformer model generates sentences with grammatical er- rors, making it challenging to determine if the style transfer successful. Morcover, the Style Transformer model performs poorly in the task of compositional editing due its inherent complex- ity. Despite the difficulty of compositional editing. Adapter-TST capable of generating fluent sen- tences that preserve the original content.
4.5 Human Evaluation Results
Table 5 shows the evaluation results. The style transfer accuracy of the models was computed us- ing the binary feedback from the evaluators. The average scores for the criteria of content preserva- tion and fluency were calculated using the 5-point Likert scores. Adapter-TST observed to outper- form the baselines in content preservation, fluency, and G-score. AST is also rated to gener- ate more syntactically sound and fluent sentences compared to the baselines. We can also observe that there is still style transfer accuracy drop of Adapter-TST on attribute Voice when modeling multiple attributes the same time. These results align with the automatic evaluations and demon- strate AST's effectiveness in performing muttiple-atrribut TST well and efficiently.
Conclusion
In this paper, we introduced a parameter-efficient framework, Adapter-TST with different neural adapters to capture different attribute information for multiple-attribute TST tasks. During training. the original parameters of BART-large were frozen, and only the adapters' parameters were optimized to relax the dependence on computational resources and supervised data. We conducted extensive ex-

---

periments on traditional sentiment transfer and multiple-attribute TST tasks. The automatic and human-based evaluation results showed that the attribute-specific adapters in Adapter-TST is able to capture relevant stylistic attributes to transfer the style while preserving the original content suc- cessfully. Our case studies also demonstrated that Adapter-TST was able to generate high-quality text in the target style. For future work, we will continue improve TST models' ability to model multiple attributes terms of quality and efficiency. We will also explore plugging Adapter-TST on other PLMs and evaluate its effectiveness.
Jacob Devlin, Ming-Wo Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre- tre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistiex: Humun Lunguage Tech- nologies, Volume (Long and Short Papers), pages 4171-41888 Minneapolis, Minnesota. Association for Computational Linguistics.
Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018. Style transfer in text: Exploration and evaluation. In Thirty-Second AAAI Conference on Artificial Intelligence.
Navita Goyal, Balaji Vasan Srinivasan, Anandhavelu N, and Abhilasha Sancheti. 2021. Multi-style trans- fer with discriminative feedback on disjoint corpus. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 3500-3510, Online. Association for Computa- tional Linguisties.
7 Limitations
This work has two limitations. First, there is style transfer accuracy reduction one of the at- tributes, while the proposed model models mul- tiple attributes simultaneously. Explorations on improving TST models' ability to handle multiple- attribute TST tasks and the dependency among at- tributes are potential directions in this field. Sec- ond, even though we have frozen the parameters of the pre-trained BART-large model to improve pa- rameter efficiency, we still need to run BART-large model to extract representations for performing TST tasks.
Bin He, Di Zhou, Jinghui Xiao, Qun Liu, Nicholas Jing Yuan, Tong Xu, et al. 2019. Integrating graph contex- tualized knowledge into pre-trained language models. arXiv preprint arXiv:1912.00145
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019, Parameter-efficien transfer learning for nip. In In- temational Conference on Machine Learning, pages 2790-2799, PMLR.
8 Ethics Statement
Zhiqiang Hu, Roy Ka-Wei Lee, Charu C Aggarwal, and Aston Zhang. 2022a. Text style transfer: A review and experimental evaluation. ACM SIGKDD Explo- rations Newsletter, 24(1):14-45,
The ethical implications of using large language models trained on data containing unchecked bi- ases are acknowledged. As with any generative task, style transfer also has the potential for mis- use, such as fact distortion, plagiarism, etc. The paper aims to demonstrate the academic utility of the proposed framework. This solution must be paired with strict checks for mistepresentattonn of- fensiveness, and bias adhere to ethical standards.
Zhiqiang Hu, Roy Kaa-Wei Lee, and Nancy F Chen. 2022b. Are current task-oriented dialogue systems able satisfy impolite users? arXiv preprint arXiv:2210.1220
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022. Deep lcarning for text style transfer: A survey. Computational Linguistics, 48(1): 48(1):155-205
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Reviews:
[{"review_id": "y5Ecijt5fr", "reviewer": "Reviewer_2k94", "summary": "", "questions": "* Question A. Could you please provide more details about the training of the adapter layers? For instance, how many epochs did you train your models? How important is the classification loss? \n* Question B. How crucial are the parallel connections? Do you think that stack connections might be enough to perform compositional text editing?\n* Question C. Have you considered and conducted experiments using other models, in addition to BART-Large? \n* Question D. Would your proposed method demonstrate favorable performance if trained on a combination of multiple textual style transfer datasets, such as sentiment, formality, grammar correction, and others?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the insightful feedback and questions. Your thought-provoking questions have significantly contributed to enhancing our paper's quality. We hope that we have comprehensively addressed the concerns and questions you presented. Based on the valuable discussion, we will make the necessary updates to our paper.\n\n---\n\n***W1**: While this limitation is not a significant reason for rejection, I believe that the authors could enhance the credibility of their proposed approach by demonstrating its generalizability and robustness. Currently, the focus is solely on one true multi-attribute TST dataset (StylePTB) and one simple model type (BART-Large). The inclusion of an additional dataset would strengthen their claims and offer a clearer demonstration of Adapter-TST’s efficacy. Moreover, a more comprehensive analysis of their findings is needed in my opinion, as the paper, in its current form, offers only a surface-level discussion of the results.*\n\n**[Response to W1]**\n\n**Comment on Dataset Limitation.** You've rightly emphasized the importance of diversifying our experimental datasets to validate the generalizability of AdapterTST. We understand that including another multi-attribute TST dataset would be ideal for demonstrating the adaptability of our approach. However, the field of multi-attribute TST is still nascent, and there is a scarcity of publicly available datasets tailored for such tasks. Our intention in focusing on the StylePTB dataset was to work with a reliable and comprehensive resource. That said, our hope is that the advancements we are introducing with AdapterTST will catalyze further research in this domain, leading to the creation of more datasets. We are actively monitoring for suitable datasets, and we will incorporate them into our experiments as they become available. We will add this discussion to the limitation section of our paper.\n\n**Comment on Model Evaluation.** Your point regarding the broader applicability of AdapterTST to models other than BART-Large is well-taken. Our framework is designed to be model-agnostic, emphasizing its versatility. While BART-Large was our initial choice given its prominence and efficiency in related tasks, we concur that experimenting with models like T5 and LLaMA will underscore AdapterTST's adaptability. We sincerely regret that due to computational constraints and time limitations, we could only include the results for applying AdapterTST on T5. Below table shows the benchmarking results on Yelp dataset.\n\n|Model| ACC| Bertscore| PPL| G-Score|\n|-|-|-|-|-|\n|BackTrans |94.5 |0.88 |11.3 |1.95|\n|CrossAlign |74.3| 0.89| 35.3| 1.23|\n|DeleteOnly |87.6 |0.91 |36.4 |1.30|\n|Del&Retri |90.2| 0.91| 34.0| 1.34|\n|DualRL |88.9| 0.95| 27.1 |1.46|\n|Template |83.7| 0.92| 47.2| 1.18|\n|Unpaired |50.6| 0.91| 53.1| 0.95|\n|UnsuperMT |96.2| 0.93| 33.5| 1.39|\n|Style Transformer |85.8 |0.95 |10.1 |2.00|\n|Adapter-TST_BART-Large| 90.1| 0.91| 8.2| 2.15|\n|Adapter-TST_T5-Large| 97.3|0.89|4.8|2.62 |\n\nWe noted that applying Adapter-TST_T5-Large yield the best performance on the Yelp dataset. We will complete the experiments and apply Adapter-TST on other LLMs such as LLaMA, and test the models on multiple stylistic attribute dataset. The updated results will be reported in the camera-ready version of our paper.\n\n**In-depth Analysis.** We also recognize the need for a more detailed analysis of our results. In our eagerness to share the promise of AdapterTST with the community, we may have oversimplified certain aspects. We will strive to provide a deeper discussion, shedding light on the nuances and potential implications of our findings.\n\n---\n\n***W2**: This is a relatively minor concern, but I fear that the paper does not sufficiently make reference to the recent studies on textual style transfer and consider stronger baselines for the Yelp dataset.&\n\n**[Response to W2]**\n\nThank you for pointing out the importance of using stronger baselines for our Yelp dataset evaluation. We are dedicated to refining our experimental design to maintain rigor. To address this, we're incorporating newer, state-of-the-art baselines relevant to the Yelp dataset. This ensures a more robust comparison for Adapter-TST's performance.\n\nWe've included the recent baselines [1] and [2] for the Yelp dataset and assessed their performance using the authors' original outputs. The table below details the results of Adapter-TST alongside these added baselines. Notably, comparable results for PromptAndRerank are documented in [3], based on human evaluations.\n\n|Model|Data|Acc|BERTScore|Fluency (PPL)| G-score |\n|-|-|-|-|-|-|\n| Adapter-TST| Yelp | 90.1 |    0.91   |   8.2   |   2.15  |\n| CRF [2]| Yelp | 92.0 |    0.96   |   26.8  |   1.49  |\n| PromptAndRerank 0-shot [1,3] | Yelp | 53.0 |    0.94   |   20.5  |   1.34  |\n| PromptAndRerank 4-shot [1,3] | Yelp | 63.6 |    0.95   |   20.6  |   1.43  |\n\n- [1] Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (Suzgun et al., EMNLP 2022)\n- [2] Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer (Shuo, WASSA 2022)\n- [3] Fluency Matters! Controllable Style Transfer with Syntax Guidance (Han & Sohn, WASSA 2023)\n\n---\n\n***W3**: In my opinion, the authors missed an opportunity to include two other simple yet potentially strong baselines in their evaluation: an Alpaca (or LLaMa) model with and without adapters. Considering these instruction-tuned models could have provided valuable insights even under a zero-shot setting. Comparing their proposed approach against such baselines could have further enriched the analysis and strengthened the overall study.*\n\n**[Response to W3]**\n\nThank you for pointing out the potential of including instruction-tuned models like Alpaca (or LLaMa) as baselines in our evaluation. We wholeheartedly agree with your assessment; such models can indeed offer additional dimensions to our study, especially in a zero-shot context. The reason we initially refrained from incorporating these models was primarily due to the intricacies of collecting instruction-tuning data for the multi-attribute TST domain. As this area of research is still emerging, there exists a dearth of readily available data tailored for such specific instruction-tuning tasks. We will certainly consider this in our future works.\n\n---\n\n***Q1**: Could you please provide more details about the training of the adapter layers? For instance, how many epochs did you train your models? How important is the classification loss?*\n\n**[Response to Q1]**\n\nWe trained our Adapter-TST models for a total of 5 epochs. This was determined after several rounds of experimentation, ensuring that the model achieves robust performance without overfitting.\n\nRegarding the classification loss: its importance cannot be understated. The classification loss plays a pivotal role as it steers the learning of the adapter layers towards capturing the stylistic attributes effectively. Without the incorporation of the classification loss, we observed that the model tends to produce outputs that merely echo the input sentences, leading to an ineffective style transfer.\n\nHowever, it's worth noting that while the classification loss is vital, the reconstruction loss also holds its significance. Without the reconstruction loss, the model seems to disproportionately focus on the target attribute words, often at the cost of content preservation. This would result in sentences that might have the desired style but lack meaningful content or context.\n\nIn essence, the interplay between the classification and reconstruction losses ensures that our model can achieve a fine balance between style transfer and content preservation. Thank you for bringing this to our attention. We will include this discussion in our revised paper to provide a more comprehensive understanding of our methodology.\n\n---\n\n***Q2**: How crucial are the parallel connections? Do you think that stack connections might be enough to perform compositional text editing?*\n\n**[Response to Q2]**\n\nThe selection between parallel and stack connections is not a matter of one being universally superior to the other, but rather it is contingent upon the specific requirements of the multi-attribute TST task in question.\n\nTo elucidate, in tasks that necessitate simultaneous generation of outputs spanning various target styles, the parallel connections are pivotal. They enable the model to cater to all target styles at once without being encumbered by the sequential nature that a stacked approach might entail.\n\nOn the other hand, when we are confronted with tasks that require compositional text editing, where several attributes need concurrent modification, the stack connections emerge as the optimal choice. The very architecture of stacking connections ensures that the model can systematically edit and refine the output in a layered manner, integrating multiple attributes seamlessly.\n\nThus, rather than being redundant or mutually exclusive, the parallel and stack connections function in harmony, each addressing distinct facets of multi-attribute TST tasks. Their incorporation ensures that our model remains versatile and aptly equipped to handle a myriad of text style transfer challenges.\n\nWe truly appreciate your keen observation, and we will include this discussion in the methodology section of our revised manuscript to convey their collective importance more effectively.\n\n---\n\n***Q3**: Have you considered and conducted experiments using other models, in addition to BART-Large?*\n\n**[Response to Q3]**\n\nAs mentioned in our earlier response, your point regarding the broader applicability of AdapterTST to models other than BART-Large is well-taken. Our framework is designed to be model-agnostic, emphasizing its versatility. Due to computational constraints and time limitations, we could only include the results for applying AdapterTST on T5. Below table shows the benchmarking results on Yelp dataset.\n\n|Model| ACC| Bertscore| PPL| G-Score|\n|-|-|-|-|-|\n|BackTrans |94.5 |0.88 |11.3 |1.95|\n|CrossAlign |74.3| 0.89| 35.3| 1.23|\n|DeleteOnly |87.6 |0.91 |36.4 |1.30|\n|Del&Retri |90.2| 0.91| 34.0| 1.34|\n|DualRL |88.9| 0.95| 27.1 |1.46|\n|Template |83.7| 0.92| 47.2| 1.18|\n|Unpaired |50.6| 0.91| 53.1| 0.95|\n|UnsuperMT |96.2| 0.93| 33.5| 1.39|\n|Style Transformer |85.8 |0.95 |10.1 |2.00|\n|Adapter-TST_BART-Large| 90.1| 0.91| 8.2| 2.15|\n|Adapter-TST_T5-Large| 97.3|0.89|4.8|2.62 |\n\nWe noted that applying Adapter-TST_T5-Large yield the best performance on the Yelp dataset. We will complete the experiments and apply Adapter-TST on other LLMs such as LLaMA, and test the models on multiple stylistic attribute dataset. The updated results will be reported in the camera-ready version of our paper.\n\n---\n\n***Q4**: Would your proposed method demonstrate favorable performance if trained on a combination of multiple textual style transfer datasets, such as sentiment, formality, grammar correction, and others?*\n\n**[Response to Q4]**\n\nYou've touched upon a salient aspect of our work. Our Adapter-TST is not only constructed with a view towards handling individual style attributes but also thrives in settings where multiple attributes converge. If subjected to an amalgamated dataset that spans sentiment, formality, grammar correction, among others, we are confident that our methodology would manifest commendable performance.\n\nA defining feature of the adapter-based architecture is its innate flexibility and ability to concurrently assimilate diverse attributes. With the deployment of the parallel connections, the model is programmed to allow each adapter to home in on, and specialize in, a particular attribute. This ensures that even in the face of a multifaceted dataset, each adapter can function autonomously and proficiently, leading to adept multi-attribute text style transfers.\n\nFurther underscoring our commitment to advancing this domain, we intend to broaden our empirical horizons and embark on a series of experiments which will evaluate Adapter-TST's prowess when trained on combined TST datasets. We will report the experiment results in our revised paper. These findings will certainly shed more light on our model's versatility and robustness.\n\n---\n\n***Others**: Missing References*\n\nWe will add the suggested reference and perform a more thorough literature review in the updated version of our paper.\n\n---\n\n***Others**: Grammar Issues*\n\nThank you for pointing out the grammar mistakes in the paper. We have corrected them, and we will perform a more thorough proofreading in our camera-ready version.", "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": "* Textual style transfer with multiple attributes remains a relatively unexplored and challenging task within the field of NLP. This paper focuses on this intriguing problem and introduces an intuitive, effective, and light-weight approach to tackle it. \n* The paper demonstrates a clear motivation for studying the problem of multiple-attribute textual style transfer and effectively outlines their research objectives. Moreover, the contributions of their work are well-defined, and the paper is written in a coherent manner overall, making it easy to understand both the arguments and results presented.\n* This work has the potential to be of interest to not only the TST community but also the broader NLG community, as it proposes a parameter-efficient method for compositional text editing. (That said, the paper lacks clarity regarding the amount of fine-tuning data required to achieve satisfactory task performance and the overall generalizability of the proposed Adapter-TST approach. Further investigation on these aspects would be rather beneficial to better understand the practical implications of this method.)\n* On the whole, the experimental setup seems to be sound and thorough.", "reasons_to_reject": "* While this limitation is not a significant reason for rejection, I believe that the authors could enhance the credibility of their proposed approach by demonstrating its generalizability and robustness. Currently, the focus is solely on one true multi-attribute TST dataset (StylePTB) and one simple model type (BART-Large). The inclusion of an additional dataset would strengthen their claims and offer a clearer demonstration of Adapter-TST’s efficacy. Moreover, a more comprehensive analysis of their findings is needed in my opinion, as the paper, in its current form, offers only a surface-level discussion of the results.\n* This is a relatively minor concern, but I fear that the paper does not sufficiently make reference to the recent studies on textual style transfer and consider stronger baselines for the Yelp dataset.\n* In my opinion, the authors missed an opportunity to include two other simple yet potentially strong baselines in their evaluation: an Alpaca (or LLaMa) model with and without adapters. Considering these instruction-tuned models could have provided valuable insights even under a zero-shot setting. Comparing their proposed approach against such baselines could have further enriched the analysis and strengthened the overall study.", "questions_to_authors": "* Question A. Could you please provide more details about the training of the adapter layers? For instance, how many epochs did you train your models? How important is the classification loss? \n* Question B. How crucial are the parallel connections? Do you think that stack connections might be enough to perform compositional text editing?\n* Question C. Have you considered and conducted experiments using other models, in addition to BART-Large? \n* Question D. Would your proposed method demonstrate favorable performance if trained on a combination of multiple textual style transfer datasets, such as sentiment, formality, grammar correction, and others?", "paper_topic_and_main_contributions": "This paper presents Adapter-TST, a straightforward yet effective and light-weight method for addressing the challenging task of multiple-attribute textual style transfer (TST). In contrast to single-attribute TST, which focuses on changing one stylistic property at a time, Adapter-TST deals with simultaneously altering multiple stylistic properties—such as sentiment, tense, voice, formality, and politeness. The Adapter-TST approach involves inserting simple neural adapters (consisting a feed-forward down-project layer, followed by a nonlinear activation function, followed by a feed forward up-project layer, along with a skip-connection layer between two projection layers) into a pre-trained network, like a pre-trained BART model, to capture and change diverse attribute information. The weights (parameters) of the original network are kept frozen during training, and only the additional adapter layers are trained. Adapter-TST offers two configurations, \"parallel\" and \"stack,\" enabling models to perform compositional style transformation (text editing).\n\nIn order to validate the effectiveness of Adapter-TST, the authors employ BART-Large as their backbone model and conduct experiments on the Yelp and StylePTB datasets. Their results indicate that Adapter-TST often performs on par with, not outperforms, several baseline methods, including BackTrans, CrossAlign, DualRL, and StyleTransformer, along certain evaluation metrics. Although human evaluation shows positive outcomes for Adapter-TST, the improvements do not appear to be statistically significant.", "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": "**TST Literature**\n* Eric Malmi, Aliaksei Severyn, and Sascha Rothe. 2020. Unsupervised Text Style Transfer with Padded Masked Language Models. arXiv preprint arXiv:2010.01054.\n* Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, and Jason Wei. 2022. A Recipe for Arbitrary Text Style Transfer with Large Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 837848, Dublin, Ireland. Association for Computational Linguistics.\n* Mirac Suzgun, Luke Melas-Kyriazi, and Dan Jurafsky. 2022. Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2195–2222, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.\n* Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. 2019. \"Transforming\" Delete, Retrieve, Generate Approach for Controlled Text Style Transfer. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3269–3279.\n* Shamik Roy, Raphael Shu, Nikolaos Pappas, Elman Mansimov, Y. Zhang, Saab Mansour and Dan Roth. “Conversation Style Transfer using Few-Shot Learning.” ArXiv abs/2302.08362 (2023).\n* Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, and Partha Talukdar. 2022. Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7439–7468, Dublin, Ireland. Association for Computational Linguistics.\n\n**LLaMa-Adapter**\n* Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao and Yu Jiao Qiao. “LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.” ArXiv abs/2303.16199 (2023).", "Typos_Grammar_Style_and_Presentation_Improvements": "* In general, the paper is well-written and easy to follow; however, there is room for improvement in the readability and flow of Sections 3.2, 4.2, and 4.3.\n* L170: 2019).To leverage → 2019. To leverage // Space needed between the period and the word “To”\n* In Table 2, the abbreviations \"BS\" and \"G\" are not explicitly defined, leaving readers uncertain about their meanings. It appears that \"G\" possibly represents the geometric mean of ACC, BERTscore, 434, and 1/PPL. \n* However, the rationale behind choosing the geometric mean of these specific metrics remains unclear. It would be beneficial for the authors to provide further explanation and intuition behind this choice to help readers better understand the motivation and significance of using the geometric mean as a composite metric."}}, {"review_id": "LTvE66R8hh", "reviewer": "Reviewer_Jb8X", "summary": "", "questions": "See Reasons To Reject", "limitations": "", "rating": 2, "confidence": 5, "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 feedback and questions. We hope that we have comprehensively addressed the concerns and questions you presented. Based on the valuable discussion, we will make the necessary updates to our paper.\n\n---\n\n***W1**: The contribution of the whole paper is limited. Using adapter-based PLMs is a common strategy for text generation tasks, even those involving multi-attribute-based generation tasks, such as [2][3]. At the same time, the adapter used in this paper is not significantly different from the previous common methods, nor is it optimized for tasks.*\n\n**[Response to W1]**\n\nWhile we concur that adapter-based PLMs have seen their utilization in text generation tasks, it's pertinent to delineate the distinctions and innovations brought forth by our Adapter-TST method. Indeed, the cited works [2] and [3] have employed prompt learning-based techniques, and while they share some methodological similarities with our approach, the application and end goal differ considerably.\n\n**Our primary contribution stems from the specialized application of adapters to manage multi-attribute text style transfer, a domain that, to the best of our knowledge, hasn't been fully explored with this methodology.** The innate strength of our model rests in the two distinct connection methods tailored to cater to a variety of multi-attribute TST tasks.\n\nFurthermore, the nuance of our approach materializes in its parallel architecture, allowing different adapters to specialize in disparate attributes simultaneously within a single sentence. This not only promotes efficiency but also ensures that each attribute is treated with the required depth and precision.\n\nWe understand that the advancement of any scientific field is predicated upon building upon prior knowledge, and while our approach derives inspiration from previous methods, it introduces nuances that we believe make a meaningful contribution to the area of multi-attribute text style transfer.\n\n---\n\n***W2**: This paper designed the method only with BART as the backbone. For this reason, I think it might be difficult to call this particular approach a \"parameter-efficient framework\" because its generality has yet to be proven.*\n\n**[Response to W2]**\n\nOur framework is designed to be compatible with various models, highlighting its flexibility. While we initially opted for BART-Large due to its known effectiveness in similar tasks, we agree that tests with models such as T5 and LLaMA would further demonstrate AdapterTST's versatility. We sincerely regret that due to computational constraints and time limitations, we could only include the results for applying AdapterTST on T5. Below table shows the benchmarking results on Yelp dataset.\n\n|Model| ACC| Bertscore| PPL| G-Score|\n|-|-|-|-|-|\n|BackTrans |94.5 |0.88 |11.3 |1.95|\n|CrossAlign |74.3| 0.89| 35.3| 1.23|\n|DeleteOnly |87.6 |0.91 |36.4 |1.30|\n|Del&Retri |90.2| 0.91| 34.0| 1.34|\n|DualRL |88.9| 0.95| 27.1 |1.46|\n|Template |83.7| 0.92| 47.2| 1.18|\n|Unpaired |50.6| 0.91| 53.1| 0.95|\n|UnsuperMT |96.2| 0.93| 33.5| 1.39|\n|Style Transformer |85.8 |0.95 |10.1 |2.00|\n|Adapter-TST_BART-Large| 90.1| 0.91| 8.2| 2.15|\n|Adapter-TST_T5-Large| 97.3|0.89|4.8|2.62 |\n\nWe noted that applying Adapter-TST_T5-Large yield the best performance on the Yelp dataset. We will complete the experiments and apply Adapter-TST on other LLMs such as LLaMA, and test the models on multiple stylistic attribute dataset. The updated results will be reported in the camera-ready version of our paper.\n\n---\n\n***W3**: There are serious deficiencies and possible unfair comparisons in the experimental part. The main reasons are as follows: 3.1 Baselines selected in the experiment are seriously missing. The experiment only compares the proposed method with the non-parameter-efficient method Styletransformer, while other parameter-efficient methods are ignored, especially prompt-learning-based methods, such as[1][2][3]. Although these methods are not applied to style transfer, it is clear that they are general-purpose and can be easily migrated to this task, and [2][3] also perform a similar multi-attribute text generation task while using adapter-based PLMs as baselines. 3.2 Unfair comparison with baselines. The backbone of Adapter-TST is BART-Large while the backbone of the baseline is Transformer, it is difficult to determine whether the performance gains are due to the use of PLMs or the proposed approach.\n\nReferences:\n- [1] Prefix-Tuning: Optimizing Continuous Prompts for Generation.\n- [2] Controllable Natural Language Generation with Contrastive Prefixes.\n- [3] Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation.*\n\n**[Response to W3]**\n\nYour concerns regarding the experimental section of our paper are noted. Firstly, we'd like to address the misconception regarding the works [2] and [3]. The objective of these two studies is controllable text generation, in which the models are trained using language modeling objectives with original text serving as the ground truth. During evaluation, these models are instructed to complete input sentences while controlling for specific attributes. Our Adapter-TST, on the other hand, is tailored for multi-attribute TST tasks where the primary goal is to edit multiple attributes of the input text in the absence of parallel data. Essentially, our model carries out sequence-to-sequence generation tasks without the aid of parallel data. This distinction necessitates the employment of reconstruction loss for content retention and classification loss for attribute guidance. Therefore, our approach substantially deviates from the methods presented in [2] and [3], making their seamless migration to our task challenging.\n\nTo provide more clarity on the two data settings in TST:\n\n- Parallel Setting: Under this framework, TST models are trained utilizing known pairs of text, each having distinct styles.\n-Non-parallel Setting: Here, TST models aim to modify the style of text without the benefit of knowing the matching text pairs in alternate styles.\n\nFor a more in-depth understanding of the differences between our proposed methodology and the cited works, we encourage a thorough reading of the following survey papers on TST:\n- [a] Deep Learning for Text Style Transfer: A Survey\n- [b] Text Style Transfer: A Review and Experimental Evaluation.", "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 paper was well-written and easy to follow.\n2. How to use parametric efficient methods in style transfer tasks is an important field worth exploring.", "reasons_to_reject": "1. The contribution of the whole paper is limited.  Using adapter-based PLMs is a common strategy for text generation tasks, even those involving multi-attribute-based generation tasks, such as [2][3]. At the same time, the adapter used in this paper is not significantly different from the previous common methods, nor is it optimized for tasks.\n2. This paper designed the method only with BART as the backbone. For this reason, I think it might be difficult to call this particular approach a \"parameter-efficient framework\" because its generality has yet to be proven.\n3. There are serious deficiencies and possible unfair comparisons in the experimental part. The main reasons are as follows:\n3.1 Baselines selected in the experiment are seriously missing. The experiment only compares the proposed method with the non-parameter-efficient method Styletransformer, while other parameter-efficient methods are ignored, especially prompt-learning-based methods, such as[1][2][3]. Although these methods are not applied to style transfer, it is clear that they are general-purpose and can be easily migrated to this task, and [2][3] also perform a similar multi-attribute text generation task while using adapter-based PLMs as baselines.\n3.2 Unfair comparison with baselines.  The backbone of Adapter-TST is BART-Large while the backbone of the baseline is Transformer,  it is difficult to determine whether the performance gains are due to the use of PLMs or the proposed approach.\n\nReferences:\n\n[1] Prefix-Tuning: Optimizing Continuous Prompts for Generation. \n\n[2] Controllable Natural Language Generation with Contrastive Prefixes.\n\n[3] Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation.", "questions_to_authors": "See Reasons To Reject", "paper_topic_and_main_contributions": "The paper introduces an adapter-based approach for the multiple-attribute text style transfer task.  In short, the processed method Adapter-TST utilizes a series of adapters to model different types of attribute information. As a parameter-efficient method, Adapter-TST achieves better performance with a very low number of training parameters compared to the previous method.", "excitement": 2, "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": {"Missing_References": "See Reasons To Reject", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "tvbDgCH8Ns", "reviewer": "Reviewer_f2rF", "summary": "", "questions": "", "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 insightful feedback. We hope that we have comprehensively addressed the concerns and questions you presented. Based on the valuable discussion, we will make the necessary updates to our paper.\n\n---\n\n***W1**: The experiments only utilized one backbone model, potentially leading to an evaluation bias concerning the model's effectiveness. To address this concern, the author should investigate and assess the impact of employing various backbones on the performance of the adapters.*\n\n**[Response to W1]**\n\nWe value your feedback on improving the evaluation of our AdapterTST’s generalizibility to other backbones. Our framework is designed to be compatible with various models, highlighting its flexibility. While we initially opted for BART-Large due to its known effectiveness in similar tasks, we agree that tests with models such as T5 and LLaMA would further demonstrate AdapterTST's versatility. Due to computational constraints and time limitations, we could only include the results for applying AdapterTST on T5. Below table shows the benchmarking results on Yelp dataset.\n\n|Model| ACC| Bertscore| PPL| G-Score|\n|-|-|-|-|-|\n|BackTrans |94.5 |0.88 |11.3 |1.95|\n|CrossAlign |74.3| 0.89| 35.3| 1.23|\n|DeleteOnly |87.6 |0.91 |36.4 |1.30|\n|Del&Retri |90.2| 0.91| 34.0| 1.34|\n|DualRL |88.9| 0.95| 27.1 |1.46|\n|Template |83.7| 0.92| 47.2| 1.18|\n|Unpaired |50.6| 0.91| 53.1| 0.95|\n|UnsuperMT |96.2| 0.93| 33.5| 1.39|\n|Style Transformer |85.8 |0.95 |10.1 |2.00|\n|Adapter-TST_BART-Large| 90.1| 0.91| 8.2| 2.15|\n|Adapter-TST_T5-Large| 97.3|0.89|4.8|2.62 |\n\nWe noted that applying Adapter-TST_T5-Large yield the best performance on the Yelp dataset. We will complete the experiments and apply Adapter-TST on other LLMs such as LLaMA, and test the models on multiple stylistic attribute dataset. The updated results will be reported in the camera-ready version of our paper.\n\n---\n\n***W2**: The human evaluation lacks specific quality details, such as the number of examples used for evaluation and the number of workers hired to conduct the evaluations. Including these details is essential to ensure transparency and replicability of the evaluation process.*\n\n**[Response to W2]**\n\nYou're absolutely right in emphasizing the importance of detailed transparency in the human evaluation process. We apologize for any oversight. To clarify, we have included these specific details between Lines 500-508. The study involved a random sampling of 200 sentences from the Tense-Voice dataset. For each of these sentences, both Adapter-TST and the baselines were utilized to generate outputs based on multiple stylistic attributes and to execute compositional editing. Two linguistic researchers were then engaged to assess these generated sentences using three specific criteria, similar to the ones utilized in automated evaluations. We hope this provides clearer insights into our evaluation process.\n\n---\n\n***W3**: The comparison with prior works is insufficient, raising concerns about the effectiveness of the proposed model.*\n\n**[Response to W3]**\n\nThank you for pointing out the importance of using stronger baselines for our Yelp dataset evaluation. We are dedicated to refining our experimental design to maintain rigor. To address this, we're incorporating newer, state-of-the-art baselines relevant to the Yelp dataset. This ensures a more robust comparison of Adapter-TST's performance.\n\nWe've included the recent baselines [1] and [2] for the Yelp dataset and assessed their performance using the authors' original outputs. The table below details the results of Adapter-TST alongside these added baselines. Notably, comparable results for PromptAndRerank are documented in [3], based on human evaluations.\n\n|Model|Data|Acc|BERTScore|Fluency (PPL)| G-score |\n|-|-|-|-|-|-|\n| Adapter-TST| Yelp | 90.1 |    0.91   |   8.2   |   2.15  |\n| CRF [2]| Yelp | 92.0 |    0.96   |   26.8  |   1.49  |\n| PromptAndRerank 0-shot [1,3] | Yelp | 53.0 |    0.94   |   20.5  |   1.34  |\n| PromptAndRerank 4-shot [1,3] | Yelp | 63.6 |    0.95   |   20.6  |   1.43  |\n\n- [1] Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (Suzgun et al., EMNLP 2022)\n- [2] Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer (Shuo, WASSA 2022)\n- [3] Fluency Matters! Controllable Style Transfer with Syntax Guidance (Han & Sohn, WASSA 2023) \n\n---\n\n***Others**: Missing References*\n\nWe will add the suggested reference and perform a more thorough literature review in the updated version of our paper.", "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": "The paper reads very smoothly and is an enjoyable read. The model is explained clearly and the paper is well-structured.\n\nThe method proposed is a new application scenario extension of the Adapter framework.", "reasons_to_reject": "The experiments only utilized one backbone model, potentially leading to an evaluation bias concerning the model's effectiveness. To address this concern, the author should investigate and assess the impact of employing various backbones on the performance of the adapters.\n\nThe human evaluation lacks specific quality details, such as the number of examples used for evaluation and the number of workers hired to conduct the evaluations. Including these details is essential to ensure transparency and replicability of the evaluation process. \n\nThe comparison with prior works is insufficient, raising concerns about the effectiveness of the proposed model. \n\nMissing some related references, e.g., FGIM, Wang et al., 2019.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes an adapter-based multi-attribute text style transfer model with the parallel and stacked connection configurations. The authors focusing on an issue of the current PLM fine-tuning where the training examples are limited and the PLM contains a huge amount of parameters which may lead to overfitting.", "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": {"Missing_References": "Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper introduces "Adapter-TST," an adapter-based framework for multiple-attribute text style transfer, aiming to address the challenge of fine-tuning large language models with limited data. Adapter-TST utilizes neural adapters and offers parallel and stacked connection configurations, allowing simultaneous control of various stylistic attributes. The proposed method is evaluated on two datasets and compared to baseline models.

**Main Criticisms by Reviewers:**

- **Limited Backbone Models:** Reviewers expressed concerns about the evaluation bias resulting from using only one backbone model (BART-Large). They recommended assessing Adapter-TST with multiple backbone models to ensure generalizability.

- **Lack of Specifics in Human Evaluation:** Reviewers noted the need for more transparency in the human evaluation process, including the number of examples used and the number of workers involved.


The authors have made commendable efforts to address the reviewers' concerns. They provided results for the T5 model to assess generalizability, enhancing the paper's rigor. They also clarified the human evaluation process, offering specific details. Moreover, the authors incorporated newer, relevant baselines for comparison, strengthening their argument for the effectiveness of Adapter-TST.

In summary, the authors have made significant strides in addressing the reviewers' concerns. While some improvements can still be made, their responses demonstrate a commitment to enhancing the paper's quality and addressing potential limitations.
Decision: Accept-Findings