A Parallel Corpus for Vietnamese Central-Northern Dialect Text Transfer
Authors: Thang Le
Anh Tuan Luu
Venue: EMNLP 2023 Findings (2023)
Keywords: Vietnamese
Dialect
Text Style Transfer
TL;DR: We introduce a new parallel corpus for Vietnamese central-northern dialect text style transfer.

Abstract: The Vietnamese language embodies dialectal variants closely attached to the nation's three macro-regions: the Northern, Central and Southern regions. As the northern dialect forms the basis of the standard language, it's considered the prestige dialect. While the northern dialect differs from the remaining two in certain aspects, it almost shares an identical lexicon with the southern dialect, making the textual attributes nearly interchangeable. In contrast, the central dialect possesses a number of unique vocabularies and is less mutually intelligible to the standard dialect. Through preliminary experiments, we observe that current NLP models do not possess understandings of the Vietnamese central dialect text, which most likely originates from the lack of resources. To facilitate research on this domain, we introduce a new parallel corpus for Vietnamese central-northern dialect text transfer. Via exhaustive benchmarking, we discover monolingual language models' superiority over their multilingual counterparts on the dialect transfer task. We further demonstrate that fine-tuned transfer models can seamlessly improve the performance of existing NLP systems on the central dialect domain with dedicated results in translation and text-image retrieval tasks.

A Parallel Corpus for Vietnamese Central-Northern Dialect Text Transfer
Thang Le VinAI Research, Vietnam
Luu Anh Tuan*
Nanyang Technological University, Singapore anhtuan. anhtuan.luuentu. edu.sg
v. thangld1 www.diiieeiiiii..m io
Abstract
putational linguistics, have limitations in that they solely focus on the standard Vietnamese text.
The Vietnamese language embodies dialec- tal variants closely attached to the nation's ' three macro-regions: the Northern, Central and Southern regions. As the northern dialect forms the basis of the standard language, it's consid- ered the prestige dialect. While the northern di- alect differs from the remaining two in certain aspects, it almost shares an identical lexicon with the southern dialect, making the textual at- tributes nearly interchangeable. In contrast, the central dialect possesses a number of unique vocabularies and is less mutually intelligible to the standard dialect. Through preliminary experiments, we observe that current NLP mod- els do not possess understandings of the Viet- namese central dialect text, which most likely originates from the lack of resources. To fa- cilitate research on this domain, we introduce a new parallel corpus for Vietnamese central- northern dialect text transfer. Via exhaustive benchmarking, we discover monolingual lan- guage models' superiority over their multilin- gual counterparts on the dialect transfer task. We further demonstrate that fine-tuned trans- fer models can seamlessly improve the perfor- mance of existing NLP systems the central dialect domain with dedicated results in trans- lation and text-image retrieval tasks.
Vietnemese Input Text
ráng nhiēn ngó
ty ngaie luōn (Northern Otelect)
Gold Translation
luón [Central Dialect)
TV
Goople Translate
natural tooth very yown
Why so itchy sudden?
Oh feel so Itdhy
Yandex Translate
natural extromelly toothed
why does naturally Itch extremely well
ChatGPT
My natural beeth are extremely sharp
why does suddenlly itch so much all the time
Figure 1: Industry- translation systems respond differently regarding the central dialect
Geographically, Vietnamese provinces are cat- egorized into three macro- maaro-regions: the Northern, Central and Southern regions bringing forth differ- ent dialects (Hip, 2009). Among these, the northern dialect is often treated as the standard i.e. the de- facto text style of the language (Pham and Mcleod, 2016). Compared the northern, the southern di- alect differs in terms of pronunciation but is mostly similar in terms of lexicon, making the two mutu- ally intelligible (Shimizu and Masaaki, 2021 Son, 2018). In contrast, the central dialect, besides the phonology deviation, possesses a significant num- ber of vocabulary differences and is thus less mu- tually intelligible to the remaining two (Michaud et al., 2015, Pham, 2019). In fact, the central di- alect is often perceived as "peculiar" or even "dif- ficult to understand" by speakers of other regions due to the existence of various unique words (Hip. 2009, Pham, 2019). With population comprising of nearly one-third of the country, the central re- gion, along with its dialect, remains important and idiosyncratic part of the nation's culture (Han- dong al., 2020, Pham and Mcleod, 2016, Pham, 2005). However, existing research works, despite massive advancement in recent years, have mainly focused on the standard dialect so far (Truong et al.,
1 Introduction
Owing to the rapid development of natural lan- guage processing in the past few years, research on Vietnamese NLP have also benefited in terms of resources and modelling techniques. In particular, many task-specifie Vietnamese datasets and dedi- cated language models have been released to the community (Dao et al., 2022a, Nguyen et al., 2018, Nguyen al., 2022, Nguyen al., 2020b, Nguyen and Nguyen, 2021, Tran et al., 2022a, Nguyen and Nguyen, 2020). These benchmarks and models, as they facilitate the research on Vietnamese com-
"Corresponding Author

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2021, Nguyen al., 2020a, Nguyen et al., 2017, Nguyen et al., 2020c, Lam et al., 2020), neglect- ing other variants with potentially rich linguistic values. Furthermore, state-of-the industry-lewe NLP systems, regardless of being production-ready. do not possess understandings of the non-standard dialect. An illustrative example is presented in Fig- ure I where text utterance in the central dialect is input into several renowned translation systems including Google Translate' & Yandex Translate*. Here the -entral-style utterance differs from the northern variety at two words rāng and ngd, which corresponds to the words sao and ngiia in the north- ern counterpart. The word räng means why/how in the central dialect, but it also means teeth in the northern (standard) dialect. In contrast, the word ngd means itchy and is lexically unique to the cen- tral dialect. Taken together, the input text can be translated as For some reasons I start to feel really itchy or simply Oh feel so itchy. We can observe that while the translation outputs of the northern- style utterance are highly relevant, the predicted outputs with respect to the central-style utterance are inapposite. In particular, all systems seem to mistake the meaning of the word räng teeth and do not properly grasp the meaning of the word ngá. Despite a slight lexical difference between the two dialects, no system manages to correctly translate the central-style utterance, or even close, producing completely unconnected contents. This phenomenon can be considered a well-known bias in current NL.P research, where developed models only function towards particular, majority group of users, ignoring the needs of other minor commu- nities e.g. translation for high-resource languages versus low-resource ones (Lignos al., 2019, ter Hoeve et al., 2022, Ghosh and Caliskan, 2023). In the case of Vietnamese language, manifested as the dialectal bias where the non-prestige (i.e. cen- tral) is not comprehended, even by well-tailored systems conceivable issue stemming from the lack of appropriate training data.
models that are not only potent towards the stan- dard text but can also handle the central-style word- ings. We extensively evaluate several monolingual and multilingual language models on their abil- ity to shift the dialect of an input utterance, which requires deep linguistics understandings of the Viet- namese language. In addition, we provide experi- ments and discussions on the corpus's applications beyond mere dialect transfer, that is, adapting pre- vailing NLP models to the central dialect domain without the needs to re-train.
Our contributions can be summarized as follows:
We introduce a new parallel corpus for central- northern dialect text transfer. We extensively benchmark the capacities of several monolin- gual and multilingual language models the task, as well as their abilities to discern be- tween the two dialects.
We find that for the dialect transfer task, the monolingual models consistently and signif- icantly outperform the multilingual models. In addition, we observe that all experimented generative models suffer from a fine-tuning- pre-training mismatch.
We show that the competencies of existing models on downstream tasks, including trans- lation and text-image retrieval, degrade when confronting central-style expressions. We fur- ther demonstrate that through fine-tuning di- alect transfer adapters, the efficacies of these models in the central dialect domain can be tremendously improved without the needs to re-train.
2 Dataset Construction
In this section, we describe the procedure to con- struct a parallel corpus for Vietnamese central- northern dialect text transfer.
2.1 Procedure
Inclined to provide a remedy to this dilemma, we introduce a new parallel corpus encompassing the central and northern dialect. Constructed through manual effort under strict quality- -oontty, the cor- pus is specifically designed for the dialect transfer task, with meaningful applications towards facili- tating the development of more well-rounded NLP
Since there are subtle deviations among provinces in the same region, to improve the annotation con- sistency, we recruited central annotators whose hometowns are located in Ha Tinh a represen- tative province of the central region, and northern annotators who were born and grew up in Ha Noi- the country's 's capital and also major metropolitan area of the northern region (Nguyen et al., 2006, Tran et al., 2022b). We also required each annota- tor to have familiarity with the other dialect (e.g.
hhtps:/ttanslate. www.ttteeoooggggoo google.com/ hhtnss//trasslateyamdde yandex.coomm

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through work, living experience, etc). Annotators first underwent one-week training provided by a linguistics expert to assimilate the textual dis- tinctions between the central and northern dialects. It's important that the annotators clearly grasp the dialectal differences in text styles. Before com- mencing the construction process, the annotators must pass eligibility test where they are tasked with annotating 10 prototype samples 3. Annota- tors whose annotation validity did not exceed 80% were re-trained and had to re-take the test until qualified. Eventually, central annotators and northern ones were recruited. We next describe the two steps employed to construct the parallel cor- pus. The first step only involves central annotators while the second requires the participation of all members.
held discussion sessions among annotators re- solve the messages with partial votes, in which the linguisties specialist also participated $. Ultimately, we obtained a set of 3761 central-style specific text utterances.
Step 2 Dialect Conversion. For the obtained central-style corpus, we need to construct match- ing northern-style utterances. For this purpose, each central annotator was first paired with dis- tinct northern annotator. We then divided the raw samples into 10 folds, and evenly distributed them to cach pair of annotators. In order to annotate a sample, a central annotator must first highlight dialect-specific words present in the utterance and convey their meanings, as well as the utterance's ss to the northern annotator, who then had to produce equivalent utterance in the northern-syyl that fluently conveys the same contents, and is as close in nuances as possible. Following this step, we acquired a compilation of 3761 parallel central- northern utterance pairs.
Step Central-style Corpus Creation. There are no publicly available contents that are solely in the style of central dialect text. In order to con- struct a parallel corpus, we need to grasp a collec- tion of raw central-style text utterances". To this end, we first divided the central annotators into groups of two people. Each group was asked to act out pre-designed conversation scenarios where they chat with each other employing the central dialect. These scenarios were manually preset, ensuring that the communications are conducted in diverse situations (e.g. friends' casual chatting on romantic stories, workers planning an afterparty, etc), adding up to number of 1 12 conversations in total. For every three rounds, we randomly swapped mem- bers between groups to maintain fresh perspectives. Upon completion, we asked every central annota- tor to pick out messages that are central-style spe- cific. A message is considered central-style specific if it contains words with meanings, or lexical ap- pearances, unique to the central dialect. Naturally communicating, not every message is central-style specific, as the central dialect also shares certain lexicon similarities with its northern counterpart. We observed that for every central-style specific message picked out, received at least 4/6 votes from the annotators, indicating high-level unifor- mity. We selected all messages with full votes, and
2.2 Statisties
Dialect
#Samples
#Avg. syll.
#Avg. word
Central
3761
10.88
10.35
Northern
3761
10.97
10.13
Table 1: Dataset statistics.
We present the corpus's base statistics in Ta- ble I. The corpus was originally constructed in a syllable-separated manner, which is the natural ap- pearance of Vietnamese text (Nguyen et al., 2018). However, for the Vietnamese language, space is also used to segment syllables of the same words (Dinh et al., 2008). For example, the text utter- ance "Tôi là nghiēn cut sinh" comprises of 5 sepa- rate syllables ["Toi", "Ià", "nghiên", "citu", "sink"] that composite 3 words ['Tōi", "lä", "nghiēn citu sinh '7. A majority of works employed the RDRseg- menter software (Nguyen et al., 2018) for auto- matic word segmentation (Dao et al., 2022a, Dao et al., 2022b. Nguyen and Nguyen, 2021, Truong et al., 2021, Nguyen et al., 2020a, Nguyen et al., 2017). As the tool was trained on the standard text, we first investigated its reliability in segment- ing central-style variants. For this purpose, we executed the tool on central-style sequences and
'The test features step 1-2 deseribed Scction 2.1
"We also experimented with converting from the northern text samples to the central dialect. However, we chose the reverse direction as the annotators had difficulties selecting which elements to convert. Since the northern dialect is stan- dard, every word/phrase present in the utterance has literally the same style, whereas with the central dialect, it's relatively easy to identify non-standard elements to conduct conversion.
'Partially voted messages constitute 2% the total mes- sages being picked out. "We follow guidelines provided by Dinh et al., 2008 to deduce word boundaries.

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randomly select a subset of 100 samples where at least 2 word-merging operations were performed. Manually evaluating the tool's precision, we find that it achieved 96.25 96.25% in precision for this par- ticular subdivision. However, correctly segmented words were mostly standard words. Since we had annotated boundaries for central-style words, we next applied the software on all central-style se- quences and calculated its recall rate as well as error rate" regarding central-style words. We found the recall rate be extremely low (3.04%) which validated our hypothesis that the segmenter was not aware of central-style mnon-standard) words and ne- cessitated manual efforts for these specific words (which we did). In contrast, the error rate was fairly small (1.81% %) which, adding up the high precision measured earlier, indicated that the software con- ducted segmentations conservatively i.e. avoiding words it did not know. These preliminary inspec- tions showed that RDRsegmenter's predictions are prudently reliable on standard words but its effectiveness on central-style words is nuga- tory. Followingly, we executed the tools to obtain automatic boundaries for standard words". This resulted in the word-segmented version of the cor- pus.
agreement with the conversion ranging from 5. For each central participant, we further asked he/she to appraise the annotation for central-style words and provide consensus scores on a similar scale. We report the Fleiss' Kappa scores (Fleiss, 1971) along with overall agreement in Table 3. Compared to the conversion task, we observed that the compromise central-style word label was slightly lower. We later held a meeting with the annotators to investigate the cause and found that emanated from ambiguities in determining word boundaries. Nevertheless, the statistics signified substantial inter-annotator agreement in each com- misson.
3 Task Formulation
Given a text utterance x provided in the style of dialect a, we need to con- vert it to targeted dialect b while preserving the meaning xf
Dialect Text Transfer.
Formally, denote quence of input tokens and y = [wo: 31 9mm] as the desired output tokens, we would like to model the conditional distribution P(u]=). Train- ing involves minimizing the negative log- ligeliod L = -= -E log (Po((e x)) where 0 repre- sents the model's parameters.
[mo, x11....pp] as the se-
Type
1-gram
2-gram
3-gram
4-gram
5-gram
Syll.
42.17
64,34
77.68
86.04
91.34
In this work, we consider two settings: central- to-northern and northern-to-central. Tackling the prior means that we can readily adapt existing Vietnamese NLP models to handle the central di- alect domain whereas the latter aids in synthesizing central-style data from existing standard corpus. Both directions have widespread applications and can facilitate building intelligent agents with more inclusive comprehension and capabilities.
Word
44.93
67.76
80.97
88.74
93.41
Table 2: Percentage of novel n-grams (%).
We report the percentage of novel n-grams in the central-style samples with respect to the northern counterparts. At the unigram level, the two dialects have a nearly 50% lexical distinction, bespeaking the uniqueness of the central dialect's vocabulary.
Experiments & Discussions
2.3
Quality Control
Experiment Settings. We partitioned the dataset with 80%/10% 100% ratios form the train- ing/validation/test splits. For benchmarking the di- alect transfer task, we fine-tuned a set of pre-trained generative language models: mBART (multilingual BART) (Liu et al., 2020), BARTpho-syllahe (Viet- namese BART, operating on syllable-level data), BARTpho-word (similar to the prior but uses word- level data), BART MAATppoo-yylabll-bass and BARTpho- word-base (the base variants) (Tran al., 2022a). During training, we used a batch size of 32 and a learning rate of le - 6 along with the Adan W (Loshchilov and Hutter, 2017) optimizer with lin-
Fleiss. Kappa
Overall Agroc. (%)
Avg. rating 4.45
Conversion
0.7164
84.9
C-Word Label
0.6320
78.1
4.16
Table 3: Inter-annotator agreement.
To validate the dataset's quality, we randomly designated 100 pair of samples and requested each annotator duo (central & northern) to rate their
"This refers to percentage of sequences where the auto- matically obtained central-style word boundaries violated the manually annotated ones
"We manually resolved cases where the tool violated anno- tations of central-style words (1863...)

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ear decay schedulers. All models were fine-tuned for maximum of 300 epochs with early stopping. All settings were implemented with the PyTorch (Paszke et al., 2019) framework and the Transform- ers (Wolf et al., 2019) library. For each model, the top-5 checkpoints with lowest validation losses were selected and evaluated on the test set. We used greedy decoding in all experiments unless ex- plicitly mentioned otherwise. We also considered SBERT-based (Reimers and Gurevych, 2019) re- trieval baselines as lower bounds. In particular, we adopted two multilingual SBERT models (Reimers and Gurevych, 2020) denoted as Retrieval-M/ and Retrieval-M2 along with publicly available Viet- namese SBERT model hereafter abbreviated as Retrieval-Vi Vi. In each direction, we first encoded the input utterance and retrieved the sample with the closest semantic distance in the target dialect from the training set. For details on pre-trained checkpoints, please see Appendix A.
the monolingual outputs are also chosen more fre- quently"" For the northem--t--cenraal direction, these gaps rise by large margin. In particular, the BARTpho-syllabl-bbat model outperforms the mBART model by 11 ROUGE-1 points and 22 BLEU scores. The mBART model is also least pre- ferred by human. This shows that the dialect trans- fer task requires deep understandings of the Viet- namese language, making the monolingual models more fitting and accordingly perform much better than the multilingual ones.
Mismatch between pre-training and fine- tuning. Traditionally, these generative models were pre- pre-trained on standard Vietnamese text (Tran et al., 2022a, Liu et al., 2020), conditioning them to produce Vietnamese text of the central dialect (which is non-standard) might induce an undesir- able mismatch. Given an evaluation metric P, de- note PCN (central-to to-oortheno)) and PNC (northern- to-central) as the model's performance each di- rection. We define 6p- = PCN PNC as the perfor- mance difference when conditioned to generate the northern-style text versus the central-style one. To scrutinize the mentioned phenomenon, we illustrate 8ROUGE-1 in Figure 2. It can be seen that perfor- mance drops manifest in every model type across different decoding beams. The mBART model has the largest drop roughly points, where each monolingual model exhibits a drop of around 1- points. This again betokens the inferiority of multilingual model in the dialect transfer task.
To evaluate the quality of predicted sequences, we adopted a set of automatic evaluation metrics: ROUGE (Lin, 2004), BLEU (Papineni et al., 2002) and METEOR (Banerjee and Lavie, 2005). For the predictions of generative models, we also con- ducted human evaluations in which each participant was presented with the gold sequence and predicted outputs from systems 10 conditioned on 100 ran- dom test samples. Predicted outputs were shuffled and the participants were not aware of the differ- ent models. Each participant then picked out the sequence that he/she thought was the most suitable conversion. For each direction, we employed 3 par- ticipants with approriate backgrounds (e.g. central- origin participants for northern-to-center transition and vice versa). Upon voting, we further held meeting to resolve conflicts among raters where a linguistics specialist also partook in. For the two di- rections, we obtained corresponding Fleiss' Kappa scores (Fleiss, 1971) of 0.6447 and 0.6284 which implied substantial agreement.
Dialect Transfer. We present the results for two transfer directions in Table 4 and 5. For both set- tings, the retrieval baselines perform significantly worse than the generative (GEN) models which is to be presumed. Inspecting the retrieval and GEN models, the monolingual ones consistently and re- markably outperform the multilingual counterparts on every metric. In terms of human preference,
Boam size
miAART
ByRlpi
word
Syllaac- base
BARTpho-won word base
Figure 2: ROUGE-1 difference (PON PNc) with varying decoding beams.
Zero-shot and Few-shot settings. Large lan- guage models (LLMs) have been shown to possess
"We used cosine distance. "IIn the event that two or more systems predict the same sequence, we de-duplicate the outputs to avoid hiases
I1 As there exist cases where different models produced the same output and simultancously got elected, the total number of votes are larger than the number of samples

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Model
R-1
R-2
R-L
BLEU
METEOR
Human
Retrieval-MI
2 0.00
5.47
0.00
18.00
3.86 0.00
14.58 0.00
Retrieval-M2
2.00
6.26 0.00
19.00
4.01 #
16.33 + 0.00
Retrieval-Vi Vi
2.00
0.00
20.00
4.70
18.12 0.00
mBART
91.69 0.05
83.58
0.15 9.05
90.81 0.08
19/100
BARTpho-syllable-bee
94.38 0.07
0.09
88.27 9,074
93.75 0.06
49/100
pho-slllyyllable
92.93 0.08
0.13
92.24 8.090 92.89 0.09
20/100
BARTpho-word--aee
0.11
87.90 9.17 0.15
93.19
41/100
phRTpho-word
94.11 +411+000 0.12
89.34 + 0.21
9..10
88.38 + 0.23
93.43 + 0.12
55/100
Table Results for central-to-noorheem dialect transfer (test set). We highlight results each column and underline those in second.
Model
R-1
R-2
R-L.
BLEU
METHOR
Human
Retrieval-M1
17.18 0.00
4.01 0.00
3.00
11.30 0.00
Retrieval-M2
18.57 # 0.00
4.81 # 0.00
16.28 0.00
3.00
12.73 + 0,00
Retrieval-Vi
24.41
6.68 # 0.00
0.000
17.80 0.00
mBART
22.65:0..5
72.95 0.68
6.1 0.553
83.67 0.40
6/100
BARTpho--yllablc--bas
93.43 0.09
0.16
0.110
8.18
92.92 0.10
57/100
pysyllbe
92.49 0.13
86.40 0.24
0.113
91.94 0..97
40/100
BARTpho-word-base
93.13 0.05
87.41 #
93.08 00.5
8.13
92.41 + 0.06
57/100
BARTpho-wok pood
93.29 0.04
87.59 0.08
93.22 0.04
86.26 - 0.09
92.69 + 0.05
51/100
Table Results for northern- to-en-ttt---eealll dialect transfer (test set). We highlight the best results each column and underline those in second.
emergent abilities that allow them seamlessly generalize to unseen tasks (Brown et al., 2020). As a representative trial, we conducted experiments on 12 (Ouuyang et al., 2022, Ghosh and Caliskan, 2023) multilingual agent powered by state-of-the-art LLMs, for both the zero-shot and few-shot (5 exemplars) settings the central-to- northern dialect transfer task. Since the chatbot itself possesses high expressiveness, we further asked to provide explanations for the conducted operations. For cach explanation point provided by the chatbot, we manually evaluated if it was valid (i.e. the explanation is linguistically correct, and the inferred operation also adds up). At the sequence level, evaluated three aspects: fluency, style and correctness (the predicted sequence must preserve the utterance's meaning and be flu- ent in the target dialect). We randomly selected 100 samples from the test set and also included model as an upper bound (BARTpho for short). System outputs were anonymized and shuffled, each exam- ined by 3 northern raters. As presented in Table
6, although ChatGPT could produce moderately stylized text, fewer than half of them were fluent and the correctness ratio was below 10%. The chatbot was capable of providing relatively well- grounded interpretation points with a nearly 30% valid ratio (Table 7), but its reliability still falls be- hind the fully supervised BARTpho model which achieved over 80% correctness ratio. We further observed that the few-shot setting helped improve the chatbot's performance, validating our initial hypothesis that the performance bottleneck han- dling central-style inputs can be remedied with the provision of in-domain data. Nevertheless, with an observed overwhelming gap, multilingual large language models such ChatGPT are far from replacing task specific monolingual models such as BARTpho.
Correctness
Fluency
Style
ChatGPT (Zero-shot)
5%
37%
54%
the outputs of the BARTpho-syllable-e
ChatGPT (Few-shot)
9%
39%
58%
BARTpho (Fine-tuned)
82%
86%
95%
Table 6: Human analysis of ChatGPT and BARTpho's output qualities for central-to-northem dialect transfer.
We the version powered by GPT-3.5-Ture 13 "essonses collected during period 28/05/2023 006/2023.
Adapt existing NLP models to the central di- alect domain. Re-training existing NLP models for the central-style inputs can be expensive, and thus building adapters to seamlessly adapt prevailing
WWe separately evaluated the style and fluency attributes as we noticed that certain predicted sequences did helong to the target dialect but were far from heing fluent.

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Validity 27.34% (70/256)
Dialect
Input Text
Google Transiate
Gold Translation
ränng nhién bō gay cáing rüra nhō uông thêm
Natural teath are very healthy,
ChatGPT (Zero-shot)
Central
remember to take
ChatGPT (Few-shot)
28.20% (75/266)
vitamin bá sung
additional
did you
with you
and break your lag of sudden,
Table 7: Human analysis of ChatGPT's interpretation points for central-to-northerm dialect transfer.
sao nhiēn nga quay věy
Why you suddenlly fall and around,
remember
some extra vitamin
Northern
nhá uóng them
vitamin bá sung
remember to take
vitamins together
models to the central-style text is a worth-exploring enactment. We first conduct experiments the Vietnamese-English translation task with Google Translate as the base translation model We con- sider three types of input: the central-style utter- ance, the ground-truth northern-style utterance, and the one predicted with a BARTipho-syllable-bts model. A total of 100 random samples were drawn from the test set, each rated by three participants 1-5 scale. The
Dialect
Input Text
Google Translate
Gold Translation
After taking the
rúa thi xong mi
xin vé
please
Central
mát nhin sinh hai
come
so can look
Please go home soon exam finisses, your face looks terrible
dugg
you
xong may
please
Northem
xin vê sóm di chár mát nhin
soon after the
but scary
phátt scy durgc
Dialect
Input Text
Google Translate
Gold Translation
cung nå khh låm
Croschow vary
difficult, the both good
Central
mó, nghē
It's not too difficult, for this
in terms of content and fluency outputs were shuffled, and no participants were aware of the models' outputs beforehand. In the event that two or more outputs are identical, we conducted de-duplication to avoid biases. We re- port the average scores in Table 8. Here it is visible that the translation model degencrates substantially when confronting central-style inputs compared to the gold northern-style ones. In contrast, when these central-style inputs transferred to the northern dialect (BoRTpho), the translation qual- ities significantly improved and nearly matched that the ground truth standards. These findings show that even though model's performance de- grades when the input text belongs to the central (non-standard) dialect, we can construct adapters enabling existing NLP models to readily cope with the central-style inputs at high precision. We fur- ther present qualitative examples!7 in Figure 3.
män vira hpc té
easy to learn
job, you work learn the same
cünn khōng khō
It's not, too difficult, both work and study
låm d&u nggh
Northern
vúra läm vüra hoc
Dialect
Text
Google Translate
Gold Translation
cung näng kišm day char hşi no lur cáy må nya
Central
can't find hara can't harm
looking
too but I'm afraid nothing left
t8o cligg dang Umm day chūr sor khöng cón cái náo nüre
I'm also looking for this, but I'm
Northern
afraid nothing left
Figure 3: Dialect style affects Sainamesse-Englinn trans- lation.
employ multilingual CLIP model (Radford et al., 2021, Reimers and Gurevych, 2020) to trieve related images of Vietnamese text query on the COCO dataset (Lin al., 2014). We show a qualitative example on Figure where the model receives input text both in the central dialect and the northern counterpart (transferred by the BARTpho- syllable-base model). The lower two lines de- pict images retrieved with the central-style query whereas the upper lines contain those obtained with the converted standard query. We can see that the dialect of the input query largely affects the relevance of retrieved images especially when the query's 's key points stem from unique lexicons the central (non-standard) dialect. In Figure 4, the central-style query contains the unique phrase láy gdy get married which the CLIP model did not under- stand and thus retrieved irrelevant images (lower two lines). When processing the northern-style
Text Style
Content
Fluency
Northern (Gold)
4.04
4.24
Northern (BARTpho)
3.96
4.21
Central
1.72
2.6
Table Effects of dialect style on Vietnamexe-English translation for Google Translate.
As alternative application, experimented with the text-image retrieval task. Specifically,
15 "Responses collected during the period 28 28/05/2020 07 /0/622223. /2023.
16 All raters possess 7.0 IELTS proficiencies
Here the northern utterances were transferred hy the BAR RARTpho-sylabbe-bea model.
I8
B-32 1-52-multlinggullvv

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query, the model captured highly relevant outputs (upper two lines are all marriage-related images).
At the sequence-level (Table 9), it can be quite easy distinguish between the two dialects where fine-tuned models achieved near-perfect accuracy. In contrast, at the word-level (Table 10), the central-style attributes are seemingly harder to catch. Nonetheless, we find that the detection (or extraction) performance is decent in general as the accuracy (or Fl score) of every model is well above 95%. We also observe that the mono- lingual PhoBERT models typically perform better than the XLM models, with the exception of the di- alect detection task where the XLM-R- --arge model outperforms the PhoBERT models on the test set (albeit by small margin). On the central-style word extraction task, the PhoBERT models consis- tently outperform the XLM-R models on both the test and validation splits at the same architecture scale. We hypothesize that the dialect detection task only requires classifying the utterance's 's di- alect on a sequence-level which can be relatively easy, whereas the word extraction task requires more extensive linguistics knowledge including lo- cating word span and disambiguating central-style words from standard ones which the monolingual models do better at.
Northern style: Tudi may thi löy ve duae räi chů sao nus Central Tudi mi thi lay gay durde rái chi (Englishr You the age to get married)
Figure 4: Dialect style affects text-image retrieval,
How discriminative is the central dialect ?
Test Acc.
Val Acc.
To study how anomalous the central dialect is on both the word- and sequence-level, we fine-tuned a set of auto-encoder language models on two tasks: central-style word extraction and dialect detection. The prior can be treated as a sequence labelling task whereas the latter resembles a sequence clas- sification task. Note that the word extraction task is non-trivial, central-style words can have the same lexical appearance with certain northern-style words in which cases the models have disam- biguate them through context information. For ex- ample, the word räng means how/why in the central dialect but also denotes teeth in the northern dialect. In our experiments, we employed the multilingual XLM-RoBERTa models (Conneau et al., 2019) and the monolingual PhoBERT models (Nguyen and Nguyen, 2020) (large and base 19 For ev- ery model, we picked the 5 checkpoints with the best validation performance (aceuracy or F1) and accordingly reported the mean and standard deviation. Training hyperparameters (learning rate, batch size, ett) remain the same as in previous experiments.
PhoBERT- Large
99.60 0.00
99.73 4 0.00
XLM RoBERTa Large
99.68 0.07
99.87 0.00
ThoBERT-Buse-- V2
99.58 : 0.06
99.87 + 0.00
PhoBERT-Bas
99.47 2 0.00
99.89 : 0.06
XLM LLM--RBBRRTa-Bass
99.39 0.12
99.60 0.00
Table 9; Results on dialect detection.
Test Fl
Val Fl
PhoBERT- Large
97.03 0.04
98.32 2 0.02
RI.M-RoBERTa-LE Largee
96.89 0.04
97.94 0.05
PhoBERT-Base V2
97.21 0.06
97.86 + 0.04
PhoBERT-Base
96.77 + 0.09
98.02 + 0.02
LAM-RoBERTa-Bart
96.62 0.09
97.56 0.02
Table 10: Results central-style word extraction.
5 Related Works
Research on language varieties, specifically di- alects, has been actively developed among many languages including those with Latin (Demszky et al., 2020, Kuparinen, 2023, Samardić al., 2016. Kuparinen and Scherrer, 2023, Miletic and Scher- rer, 2022, Dereza et al., 2023, Ramponi and Casula, 2023, Vaidya and Kane, 2023, Aji et al., 2022) and
19 "PhoBERT-BR Base-V2 uses more pre-training data, hut its architecture is the same as PhoBERT-Basc

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non-Latin writing systems (Zaidan and Callison- Burch, 2011, Bouamor et al., 2018, Li et al., 2023). The research areas mostly revolve around dialect classification (Kanjirangat et al., 2023, Kuzman et al., 2023) and normalization (Kuparinen and Scherrer, 2023, Hämäläänen et al., 2022), but also span related downstream tasks such as hate speech detection (Castillo-López et al., 2023), sentiment analysis (Srivastava and Chiang, 2023), part-of- speech tugging (Mæhlum et al., 2022) and eye- tracking (Li et al., 2023). For the Vietnamese lan- guage, dialcct-related research remains bounded in traditional linguistics research (Pham, 2019, Hip, 2009, Tsukada and Nguyen, 2008, Son, 2018), whereas computational linguistics mainly puts the focus on the standard dialect (Nguyen et al., 2020b, Nguyen et al., 2020c, Lam al., 2020). Amid the small number of works considering dialectal differ- ences, only signal processing and specch-related tasks are explored (Hung et al., 6a, Hung et al., 2016b, Schweitzer and Vu, 2016), centering on the tonal (phonetic) deviations between dialects while the textual attributes and their effects on down- stream tasks remain unexplored. In recent years, a plethora of Vietnamese NLP datasets have been re- leased facilitate the development of downstream tasks including intent detection and slot filling (Dao et al., 2022b), speech translation (Nguyen al., 2022), machine translation (Doan et al., 2021), named entity recognition (Truong et al., 2021) and text-to-sql (Nguyen et al., 2020a). These corpora have the same limitations in that they do not take
(Voigt et al., 2018). Among them, many are devel- oped with extended applications in mind i.c. fa- cilitating the progress of other tasks such as para- phrasing (Yamshchikov et al., 2020), summarizing (Bertsch et al., 2022) or produeing style-specific translation (Wu et al., 2020). In our work, we focus on more language-oriented setting, that is, the transfer between different dialects (i.e. the central and northern dialects), with meaningful pertinence towards more inclusive NLP models.
6 Conclusion
In this paper, we tackle the dialect transfer prob- lem for the Vietnamese language. In particular, we introduce new benchmark for Vietnamese northal dialect text transfer. Through im- mense experiments, we discover the deficiencies of multilingual models for the task compared the monolingual counterparts. We also highlight the performance bias of existing NLP systems regard- ing the Vietnamese central dialect. As a prospec- tive remedy, we further demonstrate that the fine- tuned transfer modules can empower existing mod- els to address the central-style wordings without the needs for re-training.
7 Limitations
Although our work addresses practical problems specific the Vietnamese language and the central into account the (non-standard) central dialect and only target the standard text. While they accel- erate the progress on number of tasks, systems trained on these datasets potentially carry the same drawbacks in that they are not apt to confront the central-style wordings which might in turn cause the existing unfairness to become more severe.
dialect, the corpus was constructed with the partici- pation of annotators from representative provinces only (i.e. Ha Tinh and Ha Noi). While this de- cision consolidates the annotation consistencies, the corpus only represents a major portion of the idiosyneratic features of the two dialects, and not their entirety. Therefore, closer inspection the subtle deviations between other related provinces can provide more insightful looks into the charac- teristics of the two dialects. Additionally, although we put more focus on the applications of central-to- northern transfer in this work as they aid in readily adapting existing NLP models to the central -tyle text, the reverse direction also appeals as it can fa- cilitate the synthesis of central-style task-specific data for Vietnamese NLP research. As the experi- ments demonstrate decent level of performance in fine-tuned models, leveraging them to synthesize and assemble targeted resources in the central di- alect can be an impactful direction for future works.
As growing needs for text style transfer emerge, the field has been actively receiving attention from the research communities (Jin et al., 2020). The settings vastly differ per situational basis, rang- ing from business use cases such as formality (Briakou et al., 2021), politeness (Madaan et al., 2020), authorship (Carlson et al., 2017), simplicity (den Bercken et al., 2019) to scenarios that aim mitigating social issues such as toxicity (dos Santos al., 2018), sarcasm (Tay et al., 201 8b), gender (Prabhumoye et al., 2018), sentiment (He and McAuley, 2016; Tay et al., 201 20182,2a 8b,a,c), biases

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Acknowledgement
Keith Carlson, Allen Riddell, and Daniel N. Rock- more. 2017. Evaluating prose style transfer with the bible. Royal Society Open Science, 5.
We thank the anonymous reviewers for their con- structive feedback and helpful suggestions.
Galo Castillo-López. Arij Riabi, and Djamé Seddah. 2023. Analyzing zero-shot transfer scenarios across spanish variants for hate speech detection. In Work- shop on NLP for Similar Languages, Varieties and Dialects.
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A Pre-trained Checkpoints
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The list of pre-trained models can be found in Table 11.
B Additional Results
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---

Model
Namespace
Rctrioval-MI
sentence-tramm
Retrieval-M2
Retrieval-Vi
kocpitraalliiccnaneeesssett
mBART
ficebook/mBBAKTHlagee-c333
BARTpho-syllable-bass
Vinai/BARKThho-yyllallc---a
BARTpho-syllable
vinai/BARIIphoo-yllable
BARTpho-word--oa
vinai/BARTTpho-wordd base
BARTpho-word
vinai/BARTphhowoord
XLM-ROBERTa--Bas
xlm-roberta- base
XLM-RoBERTi-Larre
xlm-roberta-large
PhoBERT-Base
vinai/phobert-base
PhoBERT-Base-V
vinai/phobert-base--2
PhoBERT-Large
vinai/phoheert-lrge
Multilingual CLIP
sentence-
Table 11: List of pre-trained models and their according namespaces in Huggingface.
R-1
R-2
R-L
BLEU
METEOR
mBART
Beam 1 (Greedy)
91.69 : 0.05
85.58 = 0.100
91.59 = 0.05
84.42 0.15
90.81 0.08
Beam 4
91.81 1 0.09
85.77 0.12
91.71 + 0.09
90.99 0.15
Beam 8
91.82 0.07
8.078
91.72 0.07
84.62
91.00 0.08
Beam 16
91.78 # 0.06
8
91.68 # 0.06
8.15
90.97 0.06
Beam 32
91.74 0.05
85.66 # 0.07
91.65 = 0.05
8..52
90.88 = 0.07
BART Sho-pphosyee saahbos-lllbbebbase
Beam (Greedy)
94.38 0.07
89.82 0.09
94.34 0.07
88.27
93.75 0.06
Bcam 4
94.46 0.06
89.84 0.08
94.42
8.13
93.80 = 0.05
Beam 8
94.47 = 0.05
0.06
93.83 8.12 9.05
Beam 16
94.47 0.05
89.85 0.08
0.11 9.05
93.81 = 0.05
Beam 32
94.49 0.05
89.90 0.08
94.45 0.05
88.23 0.13
93.82 # 0.05
BARTpho-syllable
Beam (Greedy)
92.93 # 0.08
87.34 0.13
92.89 0.09
85.90 0.08
92.24 0.09
Beam 4
93.00 0.06
0.13
92.95
0.18
0.07
Beam 8
93.00
0.08
92.27 0.13 9.05
Beam 16
92.93 # 0.09
0.11
8.26 9.09
9.12
Beam 32
93.01 0.07
87.39
92.96 0.07
85.84 0.17
92.28 2 0.08
BARTpho-wordd hiolo--ord-bass.
Beam (Greedy)
93.75 0.11
88.75 0.15
93.73 0.11
87.80 0.19
93.19 1.19 0.11
Beam 4
93.79 0.14
0.19
93178
88.01 1 0.20
93.27 0.15
Beam 8
93.80 2 0.07
93.78 0.10
88.02 0.16
03.27
Beam 16
93.78 0.08
93.76 0.11
88.00 0.17
93.25
Beam 32
93.78 + 0.12
0.16
0.12
88.00 0.19
0.13
wooTphowword
Beam (Greedy)
94.11 0.12
89.34 0.21
94.10 0.12
88.38 0.23
93.43 0.12
Beam 4
94.09 0.14
89.30
94.07 + 0.14
93.41 8.23 0.15
Beam 8
94.09 + 0.15
94.07 0.27
93.41 8.23 0.17
Beam 16
94.05 0.11
89.26
0.10
88225
93.37 0.12
Beam 32
94.07 0.14
89.27 + 0.25
94.05 + 0.14
88.29 0.23
93.38 + 0.16
Table 12: Effects of different beam sizes (central-to-northem)

---

R-1
R-2
R-L
BLEU
METEOR
mBART
Beam I (Greedy)
82.65 # 0.53
72.95 = 0.68
82,41 # 0.53
64.64 1.18
83.67 = 0.40
Beam 4
83.84 : 0.54
74.23 # 0.56
83.67 = 0.51
66.92 1.12
84.45 0.42
Beam 8
83.88 # 0.52
74.28 = 0.62
83.70 0.52
66.94
84.53 = 0).45
Beam 16
83.89 + 0.47
74.28
= 0.60
83.68 = 0.50
66.82 1.04
84.61 = 0.41
Beam 32
83.89 = 0.51
74.35 # 0.64
83.69 = 0.53
66.88 # 1.13
84.61 = 0.42
BART
Beam (Greedy)
93.43 0.09
88.16 0.16
93.40 = 0.10
86.83 0.17
92.92 = 0.10
Beam 4
93.07 # 0.08
93.04 = 0.09 87.49 # 0.20
8.11
=
Beam 8
93.20 # 0.12
87.76
93.17
92.55 0.24
Beam 16
93.28 # 0.13
87.88 # 0.25
93.25
92.64 0.25
Beam 32
93.32 # 0.16
87.94 + 0.30
0
86.61 + 0.27
92.72 = 0.20
BAR
Beam (Greedy)
92.49 = 0.13
92.44
84.97 0.39
91.94 0.17
Beam 4
92.45 0.06
8.10
9
84.69
91.88 = 0.09
Beam 8
92.41
0.23
91.83 0.35 9..36
Beam 16
92.42 0.13
86.22 +
92.38
84.61
91.85 - 0.15
Beam 32
92.41 # 0.11
86.21 # 0.23
92.36 0.12
84.55 0.34
91.83 = 0.15
HARTpho-word-baae
Beam (Greedy)
93.13 0.05
87.41 # 0.14
93.08 = 0.05
86.32 0.11
92.41 = 0.06
Bcam 4
93.04 0.04
87.37 0.13
92.99
8.11
92.31 = 0.05
Beam 8
93.02
0.11
8.11 0.07
92.31 0.08 0.08
Beam 16
93.05 # 0.05
93.00 0.15
92.34 0.06 0.11
Beam 32
93.05 0.05
8.13
92.99 = 0.05
86.16
92.34 + 0.06
BARTpho-word
Beam (Greedy)
93.29 0.04
87.59 0.08
93.22 = 0.04
86.26 0.09
92.69 + 0.05
Beam 4
93.22 0.06
93.15 0.13
0.12
92.61 = 0.07
Beam 8
93.21 + 0.05
0.13
0.12 9..14
92.61 0.07
Beam 16
93.21 0.05
0.12
8.12 9.05
92.61 0.07
Beam 32
93.21 + 0.06
$7.50
93.14 0.06
86.24
92.61 # 0.07
Table 13; Effects of different beam sizes (northern-to-centte
C Attention Visualization
the token ngudi. Still, we can perceive that in the cases of BARTpho models, there are more attention heads pointing to the token ngudi than the mBART model.
To better understand the generative process of the trained models, we visualize the last decoder layer's cross-attention maps of random test sam- ples with BERTViz (Vig, 2019). Here pick the central-to-northernn transfer direction and choose the outputs of three models: BARTpho-syllable base, BARTpho-word-buss and mBART. Figure 5, the token dô (central-style) corresponds to the tokens các kiéu (northern-style). We can observe that the attention maps of the BARTpho models are more accurate, aligning with the target tokens, whereas the mBART model's attention maps vague, and not quite correct. In Figure 6, the token ngåi (central-style) corresponds the token ngudi in the northern dialect. The attention maps the three models are however, a bit off, with main focus on the preceding token bôn instead of
D Prompts
For the zero-shot experiments with ChatGPT, we used the following template:
Convert the following Vietnamese central dialect text utterance into the northern dialect. Explain the difference and how you do it.
Central Text: (Central-style test input) Northern Text:
For the few-shot experiments, we randomly sam- ple 5 exemplars from the training set for cach prompt and used a similar template:

---

<g>
<g>
chúr
gio
ho
ch
thanh
phó
må
thänh
trôi
phó
tôi
la
trôi
phäi
tūi
cân
<g>


thän
phäi
kě
cån
sa0
räng
gian
thän
cho
cho
câc
kè
d0
d3
kiēu
gian
bön
bón


dó
nguöi
ngai


än
(a) BARTpho-syllbll-har




chū
cho
<>>
<g>
gió
con
&
6
căn
thånh_phô
thänh_phô
580
räng
må
cho
cho
trbi
trbi
63
tói
túi
bón
nguó
ngäi
phäi
phai
än
cán than
cán cån_thän
äy
a
ké pian
kč kē_gian




cac
dô
(h) BARTpho-wordd-a
</g>


cán
căn
(b) woRd
s80
räng
chú
cho
cho
giò
ho
bón
bón
thanh
úr
nguōi
ngåi
phó
thânh
by
trôi
phó




XX
tói
mà
trôi
(c) mBART
phäi
tûi
Figure 6: Visualization of attention map- Transferring from the central (right) to northern (left) dialect (2)
cán
là
than
phäi
kě
cân
gian
thân
các
kiēu
gian


en eX_
mBART
Figure 5: Visualization of attention map Transferring from the central (right) to northern (left) dialect (1)

---

Here are few examples of parallel Vietnamese central-northor theer dialect text utterance pairs.
ChaiGPT
Central Text: (Central-style input 1} Northern Text: (Northern-style output 1}
Cancett the lowinn Vimmammnn centrol dialect notthon dillet
Central Text: (Central-style input 2} Northern Text: (Northern-style output 2}
finin
the mon difereece between the owntrll and nenther di dislects Vietnameed cen is protundaatio and socabuileyy in cer ciaiect the unsunt couviss aee ersee insted and the toinet oon more didllcct whill in the no trim dislect, the vowal sou ind Fatier and the tonwe lass distirct iinict,, Addit by notther cillect other rre atflower vershi clalect.
owntral
Central Text: (Central-style input 5} Northern Text: (Northern-style output 5}
convont central clulect the following changes mader
diulaci,
1. "in"t was changed to "Mor This s beceuse in the norther
diddes, "Jor mone commentl meneeyyly uned insieed of "'m "n-
Convert the following Vietnamese central dialect text utterance into the northern dialect. Explain the difference and how you do it.
his coe' wse ceenrtagge
dhes" The bocause
the nothern cistert "ew
"1o
"la duae". fisped Traja duyu
enought?
Central Text: (Central-style test input} Northern Text:
Chared conwertigg a and from disdect anetthe moshes understeniing g the diferr in pronurcation vocauubry bey. and grenmel betwae the lwv siccss. and marking sugecpelate dhanges effect hoss dfferences.
Queries were performed via Poe's 2nterface We observed that although the chatbot provided well-structured answers, the outputs were not al- ways consistent (i.e. the explanations might differ from the predicted sequence), and were prone to hallucinations. We give an example of this phe- nomenon Figure In this example, the chathot explains that it will change the word mi to tói, but in the predicted output, the word mi is simply omitted instead of being replaced by the word tói. Taken together, the predicted sequence is also wrong as the ground-truth sequence (northern-style) should be: "thôi mày dimg nói linh tinh nìta là dutcc côn gi" (It will be fine long you stop talking sense), while the model's predicted output: "thōi dimg nói dài dong thêm nıa lå duge chuta" means Let's not talk about it any longer, okay? (it's also an inarticulate northern utterance).
Figure 7: example of ChatGPT's response (zero- shot). Here the explanations deviate from the predicted output.
Central: Quê mi nuôi tru có mô to nhu con ni không
Northern: Quê mày nuôi trâu có con nào to nhu con nåy không ?
(Is there any buffalo as big as this one in your hometown ?)
Central: Go mi mån chi liéu rıa !?
Northern: Úa may lam gi mà lièu våy !?
E Examples
(Why you reckless !?)
We present few examples from the corpus in Table
Central: Tau ngōi că buoi no nghich chi hêt trón
14
Northern: Tao ngôi cå buôi không nghjch gi hét luôn
stayed quiet the whole time)
Table 14: Examples from the corpus.
"hhtpss//poe.com//hatii


Reviews:
[{"review_id": "H90njOwV1Y", "reviewer": "Reviewer_rL5s", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "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": "Dear reviewer,\n\nWe greatly appreciate your efforts in reviewing our paper and providing positive feedback on our paper. Thank you for the suggestions. We will update the abstract to remove unnecessary details and condense information to better reflect the study’s findings.", "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's main objective is to fill the gap in Vietnamese NLP research by focusing on the central dialect, introducing a new corpus, evaluating different language models for dialect transfer tasks, and showcasing the benefits of fine-tuned transfer models for enhancing NLP performance in the context of the central dialect.", "reasons_to_reject": "The abstract is long, lacking conciseness and a comprehensive overview of the paper's primary points. It lacks specific summaries about the extent of the improvements and the results of the translation and text-image retrieval tasks. Removing unnecessary information and including more specific information about the experimental outcomes would provide a clearer picture of the study's findings.", "questions_to_authors": "", "paper_topic_and_main_contributions": "- The paper proposes to address the linguistic variations within the Vietnamese language, particularly focusing on its central dialect, which has received relatively less attention in existing natural language processing (NLP) research. While prior works have largely concentrated on the standard dialect and its close relationship with the southern variant, the paper acknowledges the unique characteristics of the central dialect, including distinct vocabularies and expressions, as well as its reduced mutual intelligibility with the standard dialect.\n\n- To bridge this gap and enhance NLP capabilities for the central dialect, the paper introduces a new parallel corpus specifically designed for transferring text between the central and northern dialects. Through a series of extensive experiments, the study explores the effectiveness of different language models for dialect transfer tasks.\n\n- The paper demonstrates that dedicated monolingual language models exhibit superior performance compared to their multilingual counterparts for handling dialect transfers. Additionally, the paper investigates the potential of fine-tuned transfer models, which act as adapters to improve the performance of existing NLP models on central dialect text. These fine-tuned models show promising outcomes in tasks such as translation and text-image retrieval.\n", "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": {}}, {"review_id": "NXSpXT0O6N", "reviewer": "Reviewer_64HP", "summary": "", "questions": "- Will the corpus and models  that you created be made available for public use?", "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": "Dear reviewer,\n\nWe greatly appreciate your detailed and constructive comments and suggestions. Below we address the main concerns raised in your review.\n\n**Question from the reviewer**\n> *Will the corpus and models that you created be made available for public use?*\n\nThe constructed corpus will be publicly released to the community under a Creative Commons license. The fine-tuned models and their outputs as well as translated responses (including those from the ChatGPT system) will also be made available for public use. In addition, we will upload the corpus and fine-tuned models to the HuggingFace Hub [1] so that everyone can re-use them conveniently. We will include all details in the GitHub repository of the paper.\n\n[1] https://huggingface.co/\n\n**Regarding the paper’s weaknesses**\n\n> *The paper is not always easy to follow and some of the terminology used is confusing.*\n\nWe apologize for the confusion. We will check the paper thoroughly and update the writing for better readability. We would also like to hear from you if there are any particular sections/terminologies that could have been expressed better so that we can clarify them to you and also make them easier to follow.\n\n> *It is not clear whether the corpus and models developed will be availed for public use.*\n\nWe apologize for the lack of clearness. We resolved this issue in your above question.\n\n> *The details of how the authors finetuned various models, are vague*\n\nWe mentioned details of the fine-tuning process in Section 4 (Line 344-356) and Appendix A which already included the hyperparameters (batch size, learning rate, epochs, etc.), optimizer/library choices as well as checkpoints information. In the revision, with one additional page, we will move all the information into the main paper. We would also like to hear from you if there are any particular details that need further clarity so that we can address them for you.\n\nIn addition, we will make the training/evaluation codes publicly available in the GitHub repository of the paper so that these experiments can be conveniently reproduced.\n\n> *The paper addresses a localized issue of particular significance. However, the authors have not demonstrated how this work can be of interest to a wide audience base, even in Vietnam.*\n\nWe study and address the dialectal bias in Vietnamese NLP models where the central-style texts are not well comprehended. As discussed in Section 1, the central region accounts for one-third of the country’s population and forms an important, idiosyncratic part of its culture. By providing remedies to the problem, we make NLP models more functionable towards a large group of audience (i.e. native users of the central dialect) and the work therefore not only benefits the research community but only promotes usability towards prospective end-users. For example, we demonstrated that the corpus could facilitate higher translation quality in the central dialect domain through fine-tuned adapters, which you also acknowledged. This will practically help the increasing number of tourists [2] who often rely on translation technologies better navigate their way through the central region in Vietnam. As a result, the work is also of importance to the development of industrial applications. Ultimately, the work should be of interest to a wider audience base, including not only Vietnamese people but also companies developing applications that are currently hindered by the dialectal bias.\n\n[2] Baum, A. (2020). Vietnam's Development Success Story and the Unfinished SDG Agenda. TransportRN: Transportation & Sustainability (Topic).\n\n**Typos Grammar Style**\n> - *Page 2 , line 072 : However, exist research works -> existing* \n> - *Page 2, line 102-103: Albeit a slight lexical difference between the two dialect, no system manage to correctly -> Despite a slight lexical difference between the two dialects, no system manages to correctly* \n> - *Page 3, since the procedure described in section 2.1 outlines what has actually been done, all the verbs in this section should be changed to past tense.* \n> - *Further to the previous comment on section 2.1, throughout the paper the authors seem to use the present tense, when in fact they should be using the past tense.*\n\nWe agree with the reviewer on these grammar errors. We will fix them.\n\n**Presentation Improvement**\n> *Page 5, line 343: We partition the original dataset with... -> It is not clear here what is meant by the 'original dataset'*\n\nWe apologize for the confusion. Here *'the original dataset'* literally means the constructed dataset (all samples, without separate train/val/test splits). We will change it to *'the dataset'*.\n\n> *Text in table 5 is too small making it very difficult to read*\n\nWe will update the table’s size and enlarge the text.\n\n> *Page 7, not sure why you refer to the results in table 10 before those of 9. Why not just switch the 2 tables?*\n\nWe apologize for the confusion. There was an error in the latex file’s reference order. We will fix it in the revision.\n\n\n***\n\\\nWe hope that you can reconsider the review score. Please let us know if you would like us to do anything else.", "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 work presents a new resource and models that can positively impact NLP tasks for Vietnamese Central dialect text.", "reasons_to_reject": "- The paper is not always easy to follow and some of the terminology used is confusing. \n- It is not clear whether the corpus and models developed will be availed for public use. \n- The details of how the authors finetuned various models, are vague\n- The paper addresses a localized issue of particular significance. However, the authors have not demonstrated how this work can be of interest to a wide audience base, even in Vietnam.", "questions_to_authors": "- Will the corpus and models  that you created be made available for public use?", "paper_topic_and_main_contributions": "This work aims to address the scarcity of resources for the Vietnamese central dialect. It introduces a curated parallel corpus encompassing central and northern dialects, facilitating the development of more inclusive NLP models capable of handling central-style text. Through experimentation the study shows that monolingual models achieve better results for dialect transfer tasks. It also proposes fine-tuning techniques to enhance existing NLP models' performance in the central dialect domain.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Page 2 , line 072 :  However, *exist* research works -> *existing*\n- Page 2, line 102-103: *Albeit* a slight lexical difference between the two *dialect*, no system *manage* to correctly -> *Despite* a slight lexical difference between the two *dialects*, no system *manages* to correctly \n- Page 3, since the procedure described in section 2.1 outlines what has actually been done, all the verbs in this section should be changed to past tense. \n- Further to the previous comment on section 2.1, throughout the paper the authors seem to use the present tense, when in fact they should be using the past tense. \n- Page 5, line 343: We partition the original dataset with... -> It is not clear here what is meant by the 'original dataset'\n- Text in table 5 is too small making it very difficult to read\n- Page 7, not sure why you refer to the results in table 10 before those of 9. Why not just switch the 2 tables?"}}, {"review_id": "0bqQn4qK46", "reviewer": "Reviewer_sxaD", "summary": "", "questions": "## Questions\n- QA: Line 085, the authors use commercial translators to showcase examples; in what day or version did they use them? Recording this information for future reference is important as these services often change (to a better or worse version). \n- QB: It is unclear whether the dataset for the central Vietnamese dialect was manually annotated for word boundaries (segmentation by merging syllables). Related to this question, Table 2 is not clear. What does \"both\" mean in this context? And I am confused by what mistakes mean; I could not comprehend that from the text either. \n- QC: The section about mismatch between pretraining and finetuning is not clear. Either I am missing something or the experiment/task is not well presented, so understanding Figure 2 is hard.\n\n## Comments\n- CA: BART is not an autoregressive model. It is more of a seq-2-seq model.\n- CB: The captions under all the tables and figures are very brief, and they barely convey anything. For example, it is not clear in Tables and 5 and 6 it is not clear whether the reported numbers are on dev or test. Or what do the underlined numbers exactly mean. One can only guess, and that is not very helpful.", "limitations": "", "rating": 4, "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": "Dear reviewer,\n\nWe greatly appreciate your detailed and constructive comments and suggestions. Below we address the main concerns raised in your review.\n\n**Question from the reviewer**\n\n> *Line 085, the authors use commercial translators to showcase examples; in what day or version did they use them? Recording this information for future reference is important as these services often change (to a better or worse version).*\n\nResponses from commercial translators were collected during the period from May 28th - June 7th. In addition, responses from the ChatGPT system were also gathered during this time. We thank the reviewer for the reminder. We will update these details in the paper. \n\n> *It is unclear whether the dataset for the central Vietnamese dialect was manually annotated for word boundaries (segmentation by merging syllables). Related to this question, Table 2 is not clear. What does \"both\" mean in this context? And I am confused by what mistakes mean; I could not comprehend that from the text either.*\n\nWe apologize for the lack of clearness. The corpus contains pairs of text sequences, each belonging to the central and northern dialect, respectively.\n\nFor the text sequences belonging to the central dialect, we manually annotated word boundaries for words specifically unique to this dialect as we wanted to examine its distinctive properties. However, there were standard words mixed in these sequences as well (since the two dialects shared the lexicon to a certain degree). Thus, we conducted trials to see if these words could be segmented with a public software [1]. In particular, we fed the whole central-style inputs to the software and verified the obtained word boundaries. If the tool’s precision were sufficiently high, it would indicate that the predictions were reliable (i.e. the word boundaries contained few mistakes). To confirm this, we randomly sampled 100 segmented sequences where at least two word-merging operations were conducted and calculated the tool’s precision accordingly. The results correspond to line 2 of Table 2, and the “both” here indicates that the precision score was calculated regarding both the central-style and standard words present in the inputs. As we observed that the precision was fairly high (96.2%), it indicated that the tool’s outputs contained few errors.\n\nNext, we examined the tool’s behavior with respect to the central-style words. Since we had manually annotated boundaries for these words, we could directly evaluate the automatic obtained spans based on the manually annotated ones. We use two criteria: recall (the percentage of groundtruth boundaries discovered by the tool) and mistakes (the percentage of sequences where the tool violated the groundtruth boundaries with wrong predictions, e.g. the groundtruth span could be <1,2,3> but the tool’s output was <2,3,4>). The results were presented in line 3 and 4 of Table 2. (Central) indicated that we only considered central-style words here, and (All) indicated that we evaluated on all central-style sequences. We noticed that the recall was low (3.04%) which supported our suspects that the tool could not detect central-style words properly. Meanwhile, the mistake ratio was small (1.81%), indicating that the tool was quite conservative and refrained from making uncertain decisions (i.e. those involving central-style words) which was a favorable characteristic.\n\nUltimately, for the text sequences belonging to the central dialect, the central-style words were manually annotated whereas the other (standard) words were automatically segmented. \n\nFor the text sequences which belong to the northern dialect (a.k.a standard text), we can safely apply the public software [1] to automatically obtain word boundaries as they have been properly benchmarked on the standard input domain.\n\n[1] Dat Quoc Nguyen, Dai Quoc Nguyen, Thanh Vu, Mark Dras, Mark Johnson: A Fast and Accurate Vietnamese Word Segmenter. LREC 2018\n\n> *The section about mismatch between pretraining and finetuning is not clear. Either I am missing something or the experiment/task is not well presented, so understanding Figure 2 is hard.*\n\nWe apologize for the lack of clearness. We experimented with two transfer directions:  northern-to-central (NC) and central-to-northern (CN). Given an evaluation metric $P$, denote $P_{NC}$ and $P_{CN}$ as the model’s performance in each direction accordingly. We computed $\\delta_P=P_{NC}-P_{CN}$ as the difference in model’s performance when conditioned to generate the central-style text versus the northern-style text. In our experiments, we observed that $\\delta_P$ was huge across different models and (decoding) beam sizes. As an illustration, we visualized $\\delta_{ROUGE-1}$ of each model in Figure 2 with varying (decoding) beam sizes. It could be inferred that all models faced increasing difficulties in generating central-style texts i.e. struggled in the northern-to-central direction, and it was especially the case for the mBART model. \n\nWe linked this phenomenon to the pre-training phase of these models and conjectured that this was due to a pre-training and fine-tuning mismatch. In particular, the BARTpho model was pre-trained with denoising objectives on the Vietnamese Wikipedia and a news corpus [2], all of which were standard texts. The mBART model also underwent similar pre-training on standard texts but with higher volume (i.e. more data). As a result, these models had a higher tendency to generate (northern) standard texts rather than central-style texts, which caused the high values of $\\delta_{ROUGE-1}$ as shown in Figure 2.\n\n[2] https://github.com/binhvq/news-corpus\n\n\n**Comments**\n\n> *CA: BART is not an autoregressive model. It is more of a seq-2-seq model.*\n\nWe used the term autoregressive simply to denote that the model generated texts autoregressively. We agree with the reviewer that BART is more of a seq2seq model. We will update the terms to avoid confusion.\n\n> *CB: The captions under all the tables and figures are very brief, and they barely convey anything. For example, it is not clear in Tables and 5 and 6 it is not clear whether the reported numbers are on dev or test. Or what do the underlined numbers exactly mean. One can only guess, and that is not very helpful.*\n\nWe apologize for the lack of clearness. The reported numbers in Table 5 and 6 are on the test set. The highlighted numbers denote the highest scores and the ones close to these are underlined. We will update Table 5 and 6 for better interpretability.\n\n**Regarding the paper’s weaknesses**\n\n> *Sometimes, it is hard to follow the discussion of the results, especially since the table captions are very brief.*\n\nWe apologize for the confusion. We will reformat the table and update more descriptive captions for better interpretability.\n\n> *In the contributions, the authors mention text-image retrieval, which is not discussed anywhere in the main body of the paper, only in the appendix. Anything discussed in the main body of the paper should be presented there.*\n\nWe agree with the reviewer. We will reorganize the sections and include text-image retrieval in the paper’s main body.\n\n> *The terminology of some know models and approaches are misleading (see comment).*\n\nWe apologize for the confusion. We are happy to clarify any terminology used in the paper.\n\n**Missing References**\n\n> *There has been a lot more work on Arabic since Zaidan and Callison-Burch, 2011. For example, the MADAR corpus: https://aclanthology.org/L18-1535.pdf*\n\nWe agree with the reviewer that this is a related work. We will include it in the related work section.\n\n> *Even though it is not open source (but only $25), the LDC has a corpus of Vietnamese that has multiple dialects, including the central dialect: https://catalog.ldc.upenn.edu/LDC2017S01 If the authors can not access it to explore and compare to it, I believe it is worth mentioning as an existing corpus.*\n\nWe agree with the reviewer that the corpus is relatable. However, this corpus was developed for speech-related tasks (we discussed speech-related works in line 584-591 of Section 5) and did not contain parallel input pairs for central and northern dialects . In contrast, our corpus focuses on the textual attributes and their effects on downstream tasks, and also provides parallel input pairs. We will explicitly mention this corpus for better clarity.\n\n**Typos Grammar Style**\n\n> - *Any mention of Table and Figure should be capitalized.*\n> - *Line 082 Table A --> Figure 1*\n> - *Line 072 exits --> existing*\n> - *Line 103 dialect --> dialects*\n\nWe agree with the reviewer on these typos and grammar errors. We will fix them.\n\n\n**Presentation Improvement**\n\n> *There is plenty of space in the paper, so it is worth making Tables 5 and 6 larger.*\n\nWe agree with the reviewer. We will enlarge Table 5 and 6 for better readability.\n\n> *Adding examples from the corpus would be helpful.*\n\nWe will update additional examples in the appendix section.\n\n\n**Regarding reproducibility**\n\nWe will publicly release the corpus under a Creative Common license. All fine-tuned models as well as their outputs and translated responses (including those from the ChatGPT system) will be publicly accessible as well. In addition, the training and evaluation codes will be made public. We will include all details in the GitHub repository of the paper. We look forward to hearing any additional concern that you might have regarding the paper’s reproducibility.", "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": "- A new dataset for a language variety, central Vietnamese, not present in the NLP scene. This is always welcomed, as it will enable more research to be conducted on central Vietnamese.\n- A thorough process of creating the dataset and benchmarking experiments.\n- Highlighting the need for dialect specific data for NLP research and that multilingual models are insufficient.\n- The findings of annotation decisions and some experimentation results align with previous work in dialectal research, specifically Arabic.", "reasons_to_reject": "- Sometimes, it is hard to follow the discussion of the results, especially since the table captions are very brief.\n- In the contributions, the authors mention text-image retrieval, which is not discussed anywhere in the main body of the paper, only in the appendix. Anything discussed in the main body of the paper should be presented there.\n- The terminology of some know models and approaches are misleading (see comment).", "questions_to_authors": "## Questions\n- QA: Line 085, the authors use commercial translators to showcase examples; in what day or version did they use them? Recording this information for future reference is important as these services often change (to a better or worse version). \n- QB: It is unclear whether the dataset for the central Vietnamese dialect was manually annotated for word boundaries (segmentation by merging syllables). Related to this question, Table 2 is not clear. What does \"both\" mean in this context? And I am confused by what mistakes mean; I could not comprehend that from the text either. \n- QC: The section about mismatch between pretraining and finetuning is not clear. Either I am missing something or the experiment/task is not well presented, so understanding Figure 2 is hard.\n\n## Comments\n- CA: BART is not an autoregressive model. It is more of a seq-2-seq model.\n- CB: The captions under all the tables and figures are very brief, and they barely convey anything. For example, it is not clear in Tables and 5 and 6 it is not clear whether the reported numbers are on dev or test. Or what do the underlined numbers exactly mean. One can only guess, and that is not very helpful.", "paper_topic_and_main_contributions": "In this paper, the authors tackle the issue of dialectal representation in NLP for the Vietnamese language. The dialect of focus in this paper is central Vietnamese since it is more distinct than the \"standard\" northern dialect compared to the southern dialect. The main contribution of this paper is creating a parallel corpus of northern-central Vietnamese, with dialect transfer being the main purpose of this corpus. Other contributions include benchmarking \"dialect transfer\" using pretrained BART models and using them as \"adaptors\" for downstream NLG tasks.", "excitement": 4, "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": "- There has been a lot more work on Arabic since Zaidan and Callison-Burch, 2011. For example, the MADAR corpus: https://aclanthology.org/L18-1535.pdf\n- Even though it is not open source (but only $25), the LDC has a corpus of Vietnamese that has multiple dialects, including the central dialect: https://catalog.ldc.upenn.edu/LDC2017S01 If the authors can not access it to explore and compare to it, I believe it is worth mentioning as an existing corpus.", "Typos_Grammar_Style_and_Presentation_Improvements": "- Any mention of Table and Figure should be capitalized.\n- Line 082 Table A --> Figure 1\n- There is plenty of space in the paper, so it is worth making Tables 5 and 6 larger.\n- Line 072 exits --> existing\n- Line 103 dialect --> dialects\n- Adding examples from the corpus would be helpful."}}]

Author rebuttal:

Meta-review: This paper introduces a novel parallel corpus that transfers text between the northern and central Vietnamese dialects, addressing a gap in NLP research with respect to the central Vietnamese dialect. While the potential impact of this work, the corpus creation process, and the inclusion of benchmarking experiments deserve praise, there are concerns over style and presentation. Certain sections are difficult to follow such as the Abstract and Discussion, and sometimes the terminology used is unclear. Some details are also vague or missing, e.g. how were the models fine-tuned and what were the detailed results from the text-image retrieval task. These concerns were addressed by the authors in their rebuttals, however, where they acknowledged the areas that need to be reworked and that many of the missing details can be found in the appendices (but should and would be moved into the main body of the paper).

To summarize, the revisions required to address paper content concerns can be accomplished quite readily, but substantial revisions may be required to ensure the writing is clear and up to standard.
Decision: Accept-Findings