Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

REFERENCES

1 
J. McAuley and A. Yang, ``Addressing complex and subjective product-related queries with customer reviews,'' arXiv preprint arXiv:1512.06863, Dec. 2015.DOI
2 
M. Gupta, N. Kulkarni, R. Chanda, A. Rayasam, and Z. C. Lipton, ``AmazonQA: A review-based question answering task,'' arXiv preprint arXiv:1908.04364, Aug. 2019.DOI
3 
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, ``SQuAD: 100,000+ questions for machine comprehension of text,'' arXiv preprint arXiv:1606.05250, Jun. 2016.DOI
4 
P. Rajpurkar, R. Jia, and P. Liang, ``Know what you don't know: Unanswerable questions for SQuAD,'' arXiv preprint arXiv:1806.03822, Jun. 2018.DOI
5 
M. Dunn, L. Sagun, M. Higgins, V. U. Guney, V. Cirik, and K. Cho, ``SearchQA: A new Q&A dataset augmented with context from a search engine,'' arXiv preprint arXiv:1704.05179, Apr. 2017.DOI
6 
H. Xu, B. Liu, L. Shu, and P. S. Yu, ``BERT post-training for review reading comprehension and aspect-based sentiment analysis,'' arXiv preprint arXiv:1904.02232, Apr. 2019.DOI
7 
M. Pontiki, D. Galanis, H. Papageorgiou et al., ``SemEval-2016 task 5: Aspect based sentiment analysis,'' Proc. of 10th International Workshop on Semantic Evaluation (SemEval), pp. 19-30, Jan. 2016,.DOI
8 
M. Gupta, N. Kulkarni, R. Chanda, A. Rayasam, and Z. C. Lipton, ``AmazonQA: A review-based question answering task,'' Proc. of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 4996-5002, Aug. 2019.DOI
9 
R. He and J. McAuley, ``Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,'' Proc. of the 25th International Conference on World Wide Web, pp. 507-517, Apr. 2016.DOI
10 
J. Bjerva, N. Bhutani, B. Golshan, W.-C. Tan, and I. Augenstein, ``SUBJQA: A dataset for subjectivity and review comprehension,'' Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5480-5494, Jan. 2020.DOI
11 
H. Wang, Y. Lu, and C. Zhai, ``Latent aspect rating analysis on review text data: A rating regression approach,'' Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Ddata Mining, pp. 783-792, Jul. 2010.DOI
12 
D. Yang, W. Zhang, Y. Qian, and W. Lam, ``Product question answering in E-Commerce: A survey,'' arXiv preprint arXiv:2302.08092, Feb. 2023.DOI
13 
W. Wang, N. Yang, F. Wei, B. Chang, and M. Zhou, ``Gated self-matching networks for reading comprehension and question answering,'' Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 189-198, Jan. 2017.DOI
14 
J. Bjerva, N. Bhutani, B. Golshan, W.-C. Tan, and I. Augenstein, ``SUBJQA: A dataset for subjectivity and review comprehension,'' Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5480-5494, Jan. 2020.DOI
15 
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, ``BERT: Pre-training of deep bidirectional transformers for language understanding,'' arXiv preprint arXiv:1810.04805, Oct., 2018.DOI
16 
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le, ``XLNET: Generalized autoregressive pretraining for language understanding,'' arXiv preprint arXiv:1906.08237, Jun. 2019.DOI
17 
J. Li, ``Fine-grained sentiment analysis with a fine-tuned BERT and an improved pre-training BERT,'' Proc. of IEEE International Conference on Image Processing and Computer Applications (ICIPCA), pp. 1031-1034, 2023.DOI
18 
W. Yu et al., ``A technical question answering system with transfer learning,'' Proc. of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 92-99, Jan. 2020DOI
19 
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, ``ALBERT: A lite BERT for self-supervised learning of language representations,'' arXiv preprint, arXiv:1909.11942S, Sep. 2019.DOI
20 
C. Wu, L. Li, Z. Liu, and X. Zhang, ``Machine reading comprehension based on SpanBERT and dynamic convolutional attention,'' Proc. of the 4th International Conference on Advanced Information Science and Syste, pp. 1-5, Nov. 2022.DOI
21 
X. Li, Z. Cheng, Z. Shen, H. Zhang, H. Meng, X. Xu, and G. Xiao, ``Building a question answering system for the manufacturing domain,'' IEEE Access, vol. 10, pp. 75816-75824, 2022.DOI
22 
X. Shen, A. Asai, B. Byrne, and D. G. Adrià, ``xPQA: Cross-lingual product question answering across 12 languages,'' arXiv preprint arXiv:2305.09249, May 2023.DOI
23 
D. Dashenkov, K. Smelyakov, nad O. Turuta, ``Methods of multilanguage question answering,” Proc. of IEEE 8th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Oct. 05, 2021.DOI
24 
X. Liu, Y. Shen, K. Duh, and J. Gao, ``Stochastic answer networks for machine reading comprehension,'' arXiv preprint arXiv:1712.03556, Dec. 2017.DOI
25 
W. Wang, M. Yan, and C. Wu, ``Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering,'' Proc. of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1705-1714, Jan. 2018DOI