SlowGuess/NAACL_2021_OCR huggingface dataset:
Scenario 1: Domain adaptation for dialects of the same region. Tables 1 and 2 present the results obtained for Arabic cross-domain and cross-dialect sentiment Analysis using ArSentD-LEV.
The overall obtained results for cross-dialect sentiment analysis (Table 1) show that ZS-BERT, the
SOTA	Our Results
Source	Target	DANN\( _{BOW} \)	ADRL	ZS-BERT	CORAL	MMD	DANN	Ours
Jordan	Lebanon	29	30	47	50	50.9	49.3	52
Palestine	34.5	35	47.5	50.3	51.1	51.2	52.8
Syria	32	33	51.7	53.3	53.2	51.9	54.2
Lebanon	Jordan	29	32	45	46.8	47.1	47.4	48.8
Palestine	31	35	42.7	50.5	50.7	51	52.4
Syria	37	37.5	49.6	50.7	51.1	50	52
Palestine	Jordan	32	32.5	45	50.6	49.7	47.4	52.4
Lebanon	31	31	42	50	50.5	50.5	51.9
Syria	28.5	27.5	51.7	52.4	52.4	51.3	53.7
Syria	Jordan	30.5	32	44.7	48.5	49.1	49.4	51
Lebanon	35	35.5	46.1	51.5	51.1	50.6	52
Palestine	31.5	37.5	47.1	49.7	49.8	51.3	52.9
zero-shot transfer-based method, outperforms both  $\mathrm{DANN}_{\textit{BOW}}$  and ADRL, the state-of-the-art domain adaptation methods that are based on the bag-of-words representation. Moreover, training the state-of-the-art domain adaptation methods, including CORAL, MMD and DANN, on top of BERT module has improved BERT transfer performance across dialects. Besides, these three methods achieve comparable performance for most source and target dialects and outperform both  $\mathrm{DANN}_{\textit{BOW}}$  and ADRL. Furthermore, our method, which is based on BERT and ALDA's losses, surpasses the existing state-of-the-art methods and ZS-BERT with average improvements of  $19\%$  and  $5.5\%$  respectively. Additionally, it shows better performance than the other domain adaptation methods that are implemented on top of BERT (CORAL, MMD, and DANN).
In accordance with the results obtained for cross-dialect, Table 2 shows that the ZS-BERT method outperforms both  $\mathrm{DANN}_{\textit{BOW}}$  and ADRL in most test cases of cross-domain sentiment analysis (14 out of 20 cases). Besides, the results show that the three domain adaptation methods CORAL, MMD, and DANN outperform both  $\mathrm{DANN}_{\textit{BOW}}$  and ADRL, and improve the transfer performance of BERT model. On average, the latter three methods (CORAL, MMD, and DANN) are on a par with each other in terms of accuracy. Similarly, our proposed method outperforms both state-of-the-art methods  $(\mathrm{DANN}_{\textit{BOW}}$  and ADRL) as well as ZS-BERT by an average increment of  $19\%$  and  $10.7\%$ , respectively. Moreover, it achieves a better performance than CORAL, MMD, and DANN for most source and target
Table 1: The results of accuracy measurement of Arabic cross-dialect sentiment analysis using the ArSentD-LEV dataset. The SOTA results are taken from (Khaddaj et al., 2019).
SOTA	Our Results
Source	Target	DANNbow	ADRL	ZS-BERT	CORAL	MMD	DANN	Ours
Politics	Personal	28.7	33.3	28.7	41.6	41.3	43	44.3
Religious	20.3	25.3	10	33.6	33.3	34.2	46.3
Sport	35.1	35.1	36.7	46.6	32.8	46.8	46.8
Other	22.5	24.2	38.2	49.7	50	39.7	46.1
Personal	Politics	41.7	36.8	46.3	49.7	49.4	47.5	49.7
Religious	22.8	23.4	41	44.3	44.7	43.5	44.2
sport	26.8	25.8	43.5	49.7	49.5	48.2	46.6
Other	33.8	35.4	53	57.4	57.7	49.6	58
Religious	Politics	15.5	15.5	12	42	42	37.6	40.8
Personal	24.1	26.1	25	35.1	37	36.8	38
Sport	25.8	26.8	21.6	38.1	32.8	28.5	34.8
Other	30.6	27.4	26.4	46.4	43.2	43.2	48.4
Sport	Politics	36.4	30.7	46.9	48.7	48.3	43.1	44.6
Personal	25.3	24.5	40.7	43.8	42.3	43.6	44.5
Religious	20	19	30.8	29.2	31	40.2	44
Other	35.5	35.5	48.3	49	49.6	49	54.2
Other	Politics	23.2	23.2	46.8	46.5	46.4	34.4	46.8
Personal	30.3	24.9	40.2	46.2	44.3	40.3	45.5
Religious	41.8	43	39.5	45.8	47.6	48.6	48.9
Sport	23.7	27.8	46.7	48.4	51.1	47.7	50.9
Table 2: The results of accuracy measurement of Arabic cross-domain sentiment analysis using the ArSentD-LEV dataset. The SOTA results are taken from (Khaddaj et al., 2019).
domains (12 out of 20 cases).
Scenario 2: Domain adaptation across regional dialects. Table 3 summarizes the results obtained for cross-domain and cross-dialect as well as cross-domain and cross-dialect Arabic sentiment analysis using two regional dialects (Gulf and Levantine) and MSA data, covering three domains (books reviews, hotels reviews and Twitter).
The overall obtained results show that the zero-shot transfer from AraBERT (ZS-BERT) outperforms previous state-of-the-art methods (PBLM and HTAN). Moreover, the evaluated domain adaptation methods on top of BERT improve AraBERT's performance for all evaluated scenarios. Besides, the results demonstrate that the performance of ZS-BERT method drops significantly in the cases of cross-domain as well as in cross-domain and cross-dialect scenarios. Nevertheless, the domain adaptation methods show more important improvements (an increment of  $7.4\%$  on average) in the scenarios mentioned above. The obtained results clearly show that our method surpasses the other methods for most target datasets and scenarios, except for some cases but the gap remains small.
Scenario 3: Domain adaptation from MSA to Arabic dialects using social media data. Table 4 presents the domain adaptation results obtained
Scenario	Target data	Gulf	Levantine	Modern Standard Arabic
BRAD	HARD	TEAD	Avg	BRAD	HARD	TEAD	Avg	BRAD	HARD	TEAD	Avg
Cross-dialect	PBLM	53.5	83.38	66.37	67.75	57.25	78.12	62.62	66.0	50.87	80.0	64.75	65.21
HATN	58.05	75.5	62.2	65.25	58.75	74.6	61.72	65.02	58.22	77.35	57.98	64.52
ZS-BERT	72.7	92.1	72.5	79.1	80.1	95.3	73.8	83.1	75.3	95.3	73.0	81.2
CORAL	77.0	94.1	73.3	81.5	81.1	95.4	73.6	83.4	80.3	96.6	75.5	84.1
MMD	77.1	94.4	73.1	81.5	81.9	96.3	74.1	84.1	80.1	97.1	76.8	84.7
DANN	77.6	94.3	72.9	81.6	81.4	95.8	75.6	84.3	79.1	96.4	74.5	83.3
Ours	78.4	94.3	73.0	81.9	82.3	96.5	76.6	85.1	79.6	96.4	74.9	83.6
Cross-domain	PBLM	51.25	51.63	48.88	50.58	59.62	50.25	48.25	52.71	64.0	51.75	50.0	55.25
HATN	57.35	54.45	52.6	54.8	52.77	49.32	48.88	50.32	60.7	52.97	54.68	56.12
ZS-BERT	55.7	70.9	58.8	61.8	60.6	69.9	58.0	62.8	64.5	76.8	61.8	67.7
CORAL	62.9	82.3	60.6	68.6	66.1	74.1	59.9	66.7	66.3	78.3	66.9	70.5
MMD	62.3	73.0	61.5	65.6	64.4	75.8	59.4	66.5	67.4	82.6	66.1	72.0
DANN	62.9	80.1	59.8	67.6	62.4	77.1	62.5	67.3	66.6	78.6	66.4	70.5
Ours	65.3	85.1	62.5	71.0	64.5	79.1	62.3	68.6	69.8	93.3	66.8	76.6
Cross-dialect & Cross-domain	PBLM	51.56	50.62	49.81	50.67	50.0	52.44	48.81	50.42	53.19	49.44	49.56	50.73
HATN	55.24	50.91	52.29	52.81	56.78	50.24	50.2	52.4	55.35	52.36	53.06	53.59
ZS-BERT	57.8	72.9	59.9	63.5	63.0	74.5	59.1	65.5	60.2	71.6	60.8	64.2
CORAL	63.8	82.8	60.8	69.1	64.8	78.3	60.8	67.9	66.8	79.4	63.9	70.0
MMD	63.6	82.0	60.5	68.7	64.4	79.4	60.9	68.3	66.4	77.9	64.1	69.5
DANN	63.2	75.8	62.3	67.1	63.7	77.2	62.1	67.7	65.2	80.2	65.2	70.2
Ours	64.9	85.6	61.7	70.8	64.6	86.5	61.2	70.8	66.8	79.4	65.8	70.6
from MSA to Arabic dialects. In agreement with the previously obtained results, all domain adaptation methods outperform the ZS-BERT method for all evaluated datasets by an average increment of  $4.9\%$ . CORAL, MMD, and DANN achieve comparable performances for most dialectal datasets. Moreover, the overall comparison results show that our method outperforms all other domain adaptation methods.
Table 3: The results of accuracy measurement of cross-dialect and cross-domain as well as cross-domain & cross-dialect Arabic sentiment analysis using two regional dialects and MSA data, covering three domains (books, hotels, and Twitter). Each target dataset's performance is the average accuracy obtained using its corresponding domain and/or dialect source data for each scenario. For example, in the cross-dialect scenario, the result of Gulf_BRAD is the average accuracy obtained from Levantine_BRAD and MSA_BRAD as source dialect.
Target	ZS-BERT	CORAL	MMD	DANN	Ours
MSAC	85	88	88.2	87.7	89.5
TSAC	81.4	84.9	84	86.2	87.5
ASTD	87.3	91.1	90.2	90.2	91.5
AJGT	83.8	90	88.8	85.5	90.5
TweetSYR	83.8	85.8	86.9	84.7	87.5
AraSenti-Tweet	79.6	80.4	80.7	81.5	83.9
Table 4: The results of accuracy measurement of domain adaptation results from MSA (ArSAS source dataset) to Arabic dialects.
4.4 Result discussion
The overall obtained results of the evaluated scenarios show that our method improves the transfer performance from contextualized word embedding. Moreover, it achieves far better trans
fer performance than the state-of-the-art methods that are based on the bag-of-words representation or pretrained word embedding. Indeed, all BERT-based domain adaptation methods yield a far better transfer learning performance than both DANN_BOW and ADRL methods. Besides, our method achieves better performance than CORAL, MMD, and DANN, which are implemented on top of BERT module. These results can be explained by the fact that BERT captures a high-level representation of the input text (Devlin et al., 2019; Antoun et al., 2020) as well as the effectiveness of ALDA. In fact, the latter aligns both domain output distributions using adversarial training and captures the discriminative features of the target domain inputs throughout self-training (Chen et al., 2020). Moreover, using BERT as a feature generator allows the model to extract high-level shared features of the input data that are transferable across domains and dialects. For instance, the results show that training DANN on top of BERT model outperforms  $\mathrm{DANN}_{\text{BOW}}$ , trained using the bag-of-words text representation, or even state-of-the-art methods that are based on pivot features extraction (HATN and PBLM), by a large margin for both cross-domain and cross-dialect sentiment analysis (Table 2 and
Table 1).
To understand why our proposed method outperforms the previous methods, we perform an error analysis. In this error analysis we focus on two aspects: the misclassified instances by our system and the instances correctly predicted by our method which the other approaches fail.
For the first aspect, the majority of misclassified samples correspond to very short sentences in the target dialect. Most of them are either idiomatic, offensive or sarcastic expressions that are specific to the target dialect and contains words that are distant from MSA :/wAer/, /gAr/ gAr Allh yEfw wSAfy/, /mLA THAn/ and /crf xArf/. It is worth mentioning that the other evaluated methods also misclassify these samples.
For the second aspect, we have checked the cases where our method correctly predicts the instances labels while the other methods fail. Overall, we notice that the zero-shot predictions were biased to the distribution of the source data, as example the ArSAS dataset contains  $63\%$  of negative instances. MMD, CORAL and DANN overcome this issue by aligning the distribution of source and target features, which improves the results on the target domain. Meanwhile, they tend to misclassify reviews that convey multiple sentiment polarities, as the case for hotel reviews or books reviews, where users tend to express their negative and positive sentiments in the same review. Table 8 (Section B) shows a sample of these instances. Our method outperforms these DA methods since it relies on a noise-correcting discriminator that generates different noise vectors for the source and the target domain and learns a confusion matrix in an adversarial manner. By correcting the noise in pseudo labels of the target domain using the confusion matrix, we can achieve a class-wise feature alignment of the source and the target domains. Nevertheless, the other evaluated DA methods align the output features of the source and the target domain in class agnostic fashion.
In this work, we have introduced an unsupervised domain adaptation method for Arabic cross
domain and cross-dialect sentiment analysis based on the pretrained AraBERT language model and the Adversarial-Learned Loss for Domain Adaptation (ALDA). We have performed several experiments to investigate the performance of our method as well as several state-of-the-art methods, adopting both the coarse-grained and the fine-grained taxonomies of Arabic dialects. Moreover, we have studied the performance of domain adaptation from the MSA to Arabic dialects using social media data. The overall obtained results showed that domain adaptation methods outperform zero-shot transfer from BERT model by a large margin. Furthermore, our method achieved a very promising performance and surpassed the evaluated methods on most test datasets.
In future work, we plan to investigate domain adaptive pre-training by collecting unlabeled data for target domains and fine-tuning AraBERT using the MLM objective. The aim is to study the performance of our method using domain aware language model. Since the zero-shot transfer performance using BERT model drops significantly in cross-domain sentiment analysis experiments, we believe that training domain adaptation methods on top of domain aware BERT model will lead to improved performance. We also plan to study domain adaptation from rich-resource languages such as English to Arabic language and its dialects.
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