SlowGuess/NAACL_2022_OCR huggingface dataset:
Next, we compare our self-supervision approach with the cross-domain TSA work of Gong et al. (2020). To adjust their system to a multi-domain setup, we use the full SE training set (R and L) as the labeled data from the source domain (as in our system), and a random sample from the YELP unlabeled data to represent the target domain. The number of sentences in the sample equals the size of the training set, as in their experiments. The sample was also balanced across all 18 domains.
Table 4 includes the results of this comparison. On YASO, their baseline results (Gong-BASE) improve when integrating their domain adaptation components (Gong-UDA), yet they are lower than with our self-supervision results (except for on SE).
4.6 Impact of the Initial LD Model
The quality and quantity of the TSA labeled data used for training the initial TSA model are important factors for the quality of the weak labels induced by its predictions. This, in turn, affects the quality of the entire self-training process. This experiment explores this effect, by imposing restrictions on the training set of the initial TSA model.
In this context, we experimented with three variants. One model was fine-tuned with half of the SE data  $(\mathrm{SE}_h)$ , selected at random from each domain, such that overall the samples were balanced between the two domains. Two more models were fine-tuned with SE data from one domain - restaurants  $(\mathrm{SE}_R)$  or laptops  $(\mathrm{SE}_L)$ . For all models, the number of sentences in the training set was half the size of the full SE data.
Table 5 summarizes the results of our experiments with these models, focusing on the BERT
5github.com/NUSTM/BERT-UDA
LM	Train Set	YASO	MAMS	SE
P	R	F1	P	R	F1	P	R	F1
BERT-B	SE	59.1	43.9	48.7 ± 2.1	38.5	66.4	48.7 ± 1.6	63.6	72.4	67.7 ± 1.0
SE+WL	68.5	45.9	53.7 ± 1.1	46.5	62.7	53.4 ± 0.7	67.6	71.7	69.6 ± 0.7
BERT-MLM	SE	60.5	46.0	50.6 ± 1.5	38.4	69.2	49.3 ± 1.2	65.1	73.7	69.1 ± 0.8
SE+WL	65.6	47.3	54.0 ± 0.9	45.8	62.1	52.7 ± 0.6	69.6	74.4	71.9 ± 0.7
BERT-PT	SE	61.4	46.0	51.3 ± 1.5	39.6	68.3	50.1 ± 1.1	65.5	73.0	69.0 ± 1.0
SE+WL	68.6	48.1	55.4 ± 1.0	45.2	61.5	52.1 ± 0.7	69.6	74.3	71.9 ± 0.7
SENTIX	SE	62.4	47.0	51.5 ± 1.4	38.2	69.0	49.2 ± 1.0	64.8	75.4	69.7 ± 0.9
SE+WL	69.8	44.9	52.4 ± 0.7	44.7	61.1	51.6 ± 0.3	71.5	74.9	73.1 ± 0.5
Table 3: Multi-domain results comparing the fine-tuning of four  $LMs$  with labeled data only (SE) and with self-training (SE+WL), on the three evaluation datasets.
System	Train Set	YASO	MAMS	SE
P	R	F1	P	R	F1	P	R	F1
Gong-BASE	SE	66.3	43.8	50.8 ± 1.1	42.5	68.4	52.4 ± 0.7	69.6	74.4	71.9 ± 0.7
Gong-UDA	SE → YELP	60.8	48.5	52.7 ± 1.1	38.6	72.5	50.4 ± 0.2	65.1	77.4	70.7 ± 1.4
Ours	SE	59.1	43.9	48.7 ± 2.1	38.5	66.4	48.7 ± 1.6	63.6	72.4	67.7 ± 1.0
SE+WL	68.5	45.9	53.7 ± 1.1	46.5	62.7	53.4 ± 0.7	67.6	71.7	69.6 ± 0.7
Table 4: Multi-domain results with Gong et al. (2020) (baseline  $(\mathbf{BASE})$  and the UDA approach; average of 3 training runs) compared with our results (baseline (SE) and self-training  $(\mathbf{SE} + \mathbf{WL})$ ). All the results are with BERT-B.
MLM pre-trained model. As expected, training on a single domain, or with half of the data, leads to lower performance. The results on the MAMS restaurants data are typical for a cross-domain setup. When training on laptop reviews alone, recall drops almost entirely to 2.6, and self-training improves upon that poor performance to some extent. Overall, across all datasets and all training data starting points, performance consistently improves when self-supervision is used.
4.7 Diversifying the Training Set
An alternative to our weak-labeling approach is diversifying the TSA training set by manual labeling. To explore this option, we collected an ad-hoc TSA training dataset that contains 952 sentences of reviews from multiple domains. The collection started with reviews written by crowd annotators in a given domain, on a topic of their choice. The reviews were then annotated for TSA by asking annotators to mark all sentiment-bearing targets in each sentence. This step is similar to the candidates annotation phase described in Orbach et al. (2021). However, unlike in our previous work, the detected candidates we collected were not passed through another verification step, to reduce costs. This results in noisier data, unfit for evaluation pur
poses, yet a manual examination has shown it is of sufficient quality for training.
Table 6 shows the performance obtained using this new dataset for training. The collected multi-domain labels (henceforth MD) were combined with the SE data for fine-tuning the BERT-B and BERT-MLM models. Comparing the results of fine-tuning with data from limited domains (SE) to fine-tuning with the additional MD data, performance significantly improves on the diverse YASO evaluation set. On MAMS the improvement is small, presumably because the restaurants domain is well covered in the SE training set. On the SE test set the improvement is negligible or non-existent. When comparing our approach using the WL data to the MD alternative, there is an improvement in F1 on both MAMS and SE, yet results on YASO are somewhat lower. However, the precision achieved by our approach is consistently better on all three evaluation sets compared to the alternative method. Similar trends are observed using BERT-MLM. Overall, the results with WL are better or close to those with MD, with the advantage that no manual labeling is required.
5 Manual Error Analysis
The automatic evaluation reported above is based on exact-span matches, and may be too strict in some cases. For example, in "The best thing about this place is the different sauces," the YASO la
<sup>6</sup>We refrain from the annotation of existing proprietary data due to the legal restrictions imposed on its redistribution with additional annotation layers.
Figure 2: Per-domain precision-recall curves on YASO of fine-tuning the four LMs (numbered 1-4 at the end of each line) with self-training (solid lines, tuned on SE+WL data) and without it (dotted lines, tuned on SE data).
Figure 3: Precision-recall results on MAMS with self-training (solid lines) and without it (dotted lines). The graph uses the same legend as Figure 2.
beled data contains the target the different sauces, thus counting a prediction of sauces as an error. Alternative evaluation options may circumvent this problem. For example, the above prediction would be considered as correct using overlapping span matches. However, changing the automatic evaluation can introduce new issues and and may be
too lenient. Continuing the above example, with an overlapping span match, a prediction of the entire sentence is also considered as correct.
Due to these issues, we complement the automatic evaluation with a manual one, comparing the output of an initial LD model to its self-trained counterpart. The error analysis was performed on one experimental setup, with the BERT-MLM pretrained model and the entire SE dataset for training the LD model. We further focus on the YASO dataset: for each model, 30 predictions considered as errors by the automatic evaluation were randomly sampled from each of the 6 domains. One of the authors categorized these predictions into one of four options: invalid target, correct target identified with wrong sentiment or span, borderline target that can be accepted, and a clearly correct target. The latter are presumably due to the strictness of the exact-matches based evaluation.
Table 7 presents the results of this manual analysis. Overall, the self-trained model (SE+WL) predicts less non-targets. Moreover, it identifies more
LM	Train Set	YASO	MAMS	SE
P	R	F1	P	R	F1	P	R	F1
BERT-MLM	SE	60.5	46.0	50.6 ± 1.5	38.4	69.2	49.3 ± 1.2	65.1	73.7	69.1 ± 0.8
SE+WL	65.6	47.3	54.0 ± 0.9	45.8	62.1	52.7 ± 0.6	69.6	74.4	71.9 ± 0.7
SEh	58.3	46.0	49.9 ± 1.3	35.6	67.7	46.7 ± 1.0	62.5	72.4	67.1 ± 1.0
SEh+WL	66.0	42.4	50.3 ± 1.2	44.2	61.0	51.3 ± 0.7	68.7	67.4	68.0 ± 0.8
SER	61.8	40.5	47.1 ± 2.1	36.5	68.7	47.6 ± 1.4	58.1	58.8	57.9 ± 1.3
SER+WL	69.1	40.2	49.4 ± 1.1	42.8	64.9	51.6 ± 0.7	64.2	59.3	61.2 ± 0.9
SEL	60.4	21.7	27.9 ± 2.5	37.3	2.6	4.9 ± 2.1	70.0	36.5	37.9 ± 2.2
SEL+WL	67.0	22.5	30.6 ± 1.5	51.2	4.4	8.1 ± 1.0	71.5	36.8	40.5 ± 1.0
Table 5: Multi-domain results comparing the fine-tuning of BERT-MLM with labeled data only (SE) and with self-training  $(\mathbf{SE} + \mathbf{WL})$ , with four initial models trained with data from: the entire SE data (SE), half the data from each of the SE domains  $(\mathbf{SE}_h)$ , or a single SE domain - restaurants  $(\mathbf{SE}_R)$  or laptops  $(\mathbf{SE}_L)$ .
LM	Train Set	YASO	MAMS	SE
P	R	F1	P	R	F1	P	R	F1
BERT-B	SE+MD	62.2	50.2	54.2 ± 1.8	38.8	67.6	49.3 ± 1.0	62.5	73.7	67.6 ± 1.0
SE+WL	68.5	45.9	53.7 ± 1.1	46.5	62.7	53.4 ± 0.7	67.6	71.7	69.6 ± 0.7
BERT-MLM	SE+MD	63.1	51.1	55.1 ± 1.3	39.3	67.2	49.6 ± 0.8	64.9	74.5	69.4 ± 1.0
SE+WL	65.6	47.3	54.0 ± 0.9	45.8	62.1	52.7 ± 0.6	69.6	74.4	71.9 ± 0.7
Table 6: A comparison of fine-tuning two  $LMs$  with data augmented through self-training  $(\mathbf{SE} + \mathbf{WL})$  or combined with a multi-domain TSA dataset  $(\mathbf{SE} + \mathbf{MD})$ .
Error Analysis	SE	SE+WL
Invalid target	27.2%	17.8%
Wrong sentiment span	18.3%	18.3%
Borderline target	14.4%	16.1%
Correct target	40.0%	47.8%
Table 7: Error analysis results on randomly selected wrong predictions on YASO evaluation. Predictions are obtained by the MLM baseline model fine-tuned with the SE data (left) and with  $\mathrm{SE + WL}$  data (right).
valid targets than the baseline model. As for the other two categories of errors, the borderline and wrong span/sentiment, the two models are on par. These results emphasize the importance of manual error analysis, and show that even in this detailed analysis, which goes beyond the labeling information available in the YASO evaluation set, we find that the multi-domain model with the WL is better.
This work addressed a multi-domain TSA setting in which a system is trained on data from a small number of domains, and is applied to texts from any domain. Our proposed method has employed self-learning to augment an existing TSA dataset with weak labels obtained from a large corpus.
An empirical evaluation of our approach has demonstrated that the self-supervision technique,
often used when having a training set of limited size, is also effective for enhancing the diversity of the training data. Specifically, our results show that the self-trained multi-domain model consistently improves performance, for various underlying LMs, and with different starting points: data from two domains, removing half of the data, or restricting to only one domain. Interestingly, even in the presence of a diverse TSA labeled data, our approach was comparable to the performance obtained with that data. This allows avoiding the burden and costs associated with manual TSA data collection.
In addition to finding targets and their sentiments, other related tasks aim to extract the corresponding opinion term (Peng et al., 2020), identify the relevant aspect category (Wan et al., 2020), or both (Cai et al., 2021). As future work, our approach may be applied to these more complex tasks as well. Similarly, it may be useful for developing a multilingual TSA system, by utilizing weak labels produced on unlabeled reviews data in non-English languages.
We wish to thank Artem Spector for the development of the experimental infrastructure. We also thank the anonymous reviewers for their insightful comments and feedback.
Roy Bar-Haim, Lilach Edelstein, Charles Jochim, and Noam Slonim. 2017. Improving claim stance classification with lexical knowledge expansion and context utilization. In Proceedings of the 4th Workshop on Argument Mining, pages 32-38, Copenhagen, Denmark. Association for Computational Linguistics.
Eric Breck, Yejin Choi, and Claire Cardie. 2007. Identifying expressions of opinion in context. In *IJCAI*, volume 7, pages 2683-2688.
Hongjie Cai, Rui Xia, and Jianfei Yu. 2021. Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 340-350, Online. Association for Computational Linguistics.
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. 2009. Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]. IEEE Transactions on Neural Networks, 20(3):542-542.
Zhuang Chen and Tieyun Qian. 2021. Bridge-based active domain adaptation for aspect term extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 317-327, Online. Association for Computational Linguistics.
Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, and Xipeng Qiu. 2021a. Does syntax matter? a strong baseline for aspect-based sentiment analysis with RoBERTa. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1816-1829, Online. Association for Computational Linguistics.
Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, and Xiaodan Zhu. 2021b. Preview, attend and review: Schema-aware curriculum learning for multi-domain dialogue state tracking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 879-885, Online. Association for Computational Linguistics.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT, pages 4171-4186.
Mark Dredze and Koby Crammer. 2008. Online methods for multi-domain learning and adaptation. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 689-697.
Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010. Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 340-348. Association for Computational Linguistics.
Chenggong Gong, Jianfei Yu, and Rui Xia. 2020. Unified feature and instance based domain adaptation for aspect-based sentiment analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7035-7045, Online. Association for Computational Linguistics.
Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '04, page 168-177, New York, NY, USA. Association for Computing Machinery.
Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, and Min Yang. 2019. A challenge dataset and effective models for aspect-based sentiment analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6280-6285, Hong Kong, China. Association for Computational Linguistics.
Mahesh Joshi, Mark Dredze, William Cohen, and Carolyn Rose. 2012. Multi-domain learning: when do domains matter? In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1302-1312.
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, and Ahmed Hassan Awadallah. 2021. Self-training with weak supervision. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 845-863, Online. Association for Computational Linguistics.
Phillip Keung, Yichao Lu, György Szarvas, and Noah A Smith. 2020. The multilingual amazon reviews corpus. arXiv preprint arXiv:2010.02573.
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization.
Dong-Hyun Lee. 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, volume 3, page 2.
Xin Li, Lidong Bing, Piji Li, and Wai Lam. 2019a. A unified model for opinion target extraction and target sentiment prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6714-6721.
Xin Li, Lidong Bing, Piji Li, Wai Lam, and Zhimou Yang. 2018. Aspect term extraction with history attention and selective transformation. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pages 4194-4200. International Joint Conferences on Artificial Intelligence Organization.
Xin Li, Lidong Bing, Wenxuan Zhang, and Wai Lam. 2019b. Exploiting BERT for end-to-end aspect-based sentiment analysis. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 34-41, Hong Kong, China. Association for Computational Linguistics.
Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, and Xin Li. 2019c. Exploiting coarse-to-fine task transfer for aspect-level sentiment classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4253-4260.
Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, et al. 2020. XGLUE: A New Benchmark Dataset for Cross-lingual Pretraining, Understanding and Generation. arXiv preprint arXiv:2004.01401.
Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1-167.
Hong Liu, Jianmin Wang, and Mingsheng Long. 2021. Cycle self-training for domain adaptation.
David McClosky, Eugene Charniak, and Mark Johnson. 2006. Effective self-training for parsing. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 152-159, New York City, USA. Association for Computational Linguistics.
Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2021. YASO: A targeted sentiment analysis evaluation dataset for open-domain reviews.
Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs/0506075.
Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, and Luo Si. 2020. Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 8600-8607. AAAI Press.
OrenPereg,DanielKorat,and Moshe Wasserblat.2020. Syntactically aware cross-domain aspect and opinion terms extraction.In Proceedings of the 28th International Conference on Computational Linguistics,
pages 1772-1777, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Minh Hieu Phan and Philip O. Ogunbona. 2020. Modelling context and syntactical features for aspect-based sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3211-3220, Online. Association for Computational Linguistics.
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27-35, Dublin, Ireland. Association for Computational Linguistics.
Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, and Ting Liu. 2020. Dynamic fusion network for multidomain end-to-end task-oriented dialog. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6344-6354, Online. Association for Computational Linguistics.
Alexander Rietzler, Sebastian Stabinger, Paul Opitz, and Stefan Engl. 2020. Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspect-target sentiment classification. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4933-4941, Marseille, France. European Language Resources Association.
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005. Semi-supervised self-training of object detection models. In Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01, WACV-MOTION '05, page 29-36, USA. IEEE Computer Society.
Sebastian Ruder, Parsa Ghaffari, and John G. Breslin. 2017. Data selection strategies for multi-domain sentiment analysis.
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA. Association for Computational Linguistics.
Xin Su, Yiyun Zhao, and Steven Bethard. 2021. The University of Arizona at SemEval-2021 task 10: Applying self-training, active learning and data augmentation to source-free domain adaptation. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 458-466, Online. Association for Computational Linguistics.
Thakur Ashutosh Suman and Abhinav Jain. 2021. ASTarTwice at SemEval-2021 task 5: Toxic span detection using RoBERTa-CRF, domain specific pretraining and self-training. In Proceedings of the