Page 369 - 《软件学报》2025年第10期
P. 369

4766                                                      软件学报  2025  年第  36  卷第  10  期


                     18653/v1/D15-1007]
                  [2]   Peng HY, Xu L, Bing LD, Huang F, Lu W, Si L. Knowing what, how and why: A near complete solution for aspect-based sentiment
                     analysis. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 8600–8607. [doi: 10.1609/aaai.v34i05.6383]
                  [3]   Wan H, Yang YF, Du JF, Liu Y, Qi KX, Pan JZ. Target-aspect-sentiment joint detection for aspect-based sentiment analysis. In: Proc. of
                     the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 9122–9129. [doi: 10.1609/aaai.v34i05.6447]
                  [4]   Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I. SemEval-2015 task 12: Aspect based sentiment analysis. In:
                     Proc. of the 9th Int’l Workshop on Semantic Evaluation (SemEval 2015). Denver: ACL, 2015. 486–495. [doi: 10.18653/v1/S15-2082]
                  [5]   Song LF, Xin CL, Lai SP, Wang AT, Su JS, Xu K. CASA: Conversational aspect sentiment analysis for dialogue understanding. Journal
                     of Artificial Intelligence Research, 2022, 73: 511–533. [doi: 10.1613/jair.1.12802]
                  [6]   Li BB, Fei H, Li F, Wu YH, Zhang JS, Wu SQ, Li JY, Liu YJ, Liao LZ, Chua TS, Ji DH. DiaASQ: A benchmark of conversational aspect-
                     based sentiment quadruple analysis. In: Findings of the Association for Computational Linguistics: ACL 2023. Toronto: ACL, 2023.
                     13449–13467. [doi: 10.18653/v1/2023.findings-acl.849]
                  [7]   Shen CL, Sun CL, Wang JJ, Kang YY, Li SS, Liu XZ, Si L, Zhang M, Zhou GD. Sentiment classification towards question-answering
                     with hierarchical matching network. In: Proc. of the 2018 Conf. on Empirical Methods in Natural Language Processing. Brussels: ACL,
                     2018. 3654–3663. [doi: 10.18653/v1/D18-1401]
                  [8]   Ji  ZW,  Lee  N,  Feiseke  R,  Yu  TZ,  Su  D,  Xu  Y,  Ishii  E,  Bang  YJ,  Madotto  A,  Fung  P.  Survey  of  hallucination  in  natural  language
                     generation. ACM Computing Surveys, 2023, 55(12): 248. [doi: 10.1145/3571730]
                  [9]   He ZW, Liang T, Jiao WX, Zhang ZS, Yang YJ, Wang R, Tu ZP, Shi SM, Wang X. Exploring human-like translation strategy with large
                     language models. Trans. of the Association for Computational Linguistics, 2024, 12: 229–246. [doi: 10.1162/tacl_a_00642]
                 [10]   Zhao H, Huang LT, Zhang R, Lu Q, Xue H. SpanMlt: A span-based multi-task learning framework for pair-wise aspect and opinion terms
                     extraction. In: Proc. of the 58th Annual Meeting of the Association for Computational Linguistics. ACL, 2020. 3239–3248. [doi: 10.
                     18653/v1/2020.acl-main.296]
                 [11]   Zhang WX, Deng Y, Li X, Yuan YF, Bing LD, Lam W. Aspect sentiment quad prediction as paraphrase generation. In: Proc. of the 2021
                     Conf. on Empirical Methods in Natural Language Processing. Punta Cana: ACL, 2021. 9209–9219. [doi: 10.18653/v1/2021.emnlp-main.
                     726]
                 [12]   Gou  ZB,  Guo  QY,  Yang  YJ.  MvP:  Multi-view  prompting  improves  aspect  sentiment  tuple  prediction.  In:  Proc.  of  the  61st  Annual
                     Meeting of the Association for Computational Linguistics, Vol. 1 (Long Papers). Toronto: ACL, 2023. 4380–4397. [doi: 10.18653/v1/
                     2023.acl-long.240]
                 [13]   Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou YQ, Li W, Liu PJ. Exploring the limits of transfer learning with a
                     unified text-to-text Transformer. The Journal of Machine Learning Research, 2020, 21(1): 5485–5551.
                 [14]   Bao XY, Wang ZQ, Jiang XT, Xiao R, Li SS. Aspect-based sentiment analysis with opinion tree generation. In: Proc. of the 31st Int’l
                     Joint Conf. on Artificial Intelligence. 2022. 4044–4050. [doi: 10.24963/ijcai.2022/561]
                 [15]   Bao  XT,  Jiang  XT,  Wang  ZQ,  Zhang  Y,  Zhou  GD.  Opinion  tree  parsing  for  aspect-based  sentiment  analysis.  In:  Findings  of  the
                     Association for Computational Linguistics: ACL 2023. Toronto: ACL, 2023. 7971–7984. [doi: 10.18653/v1/2023.findings-acl.505]
                 [16]   Xu L, Li H, Lu W, Bing LD. Position-aware tagging for aspect sentiment triplet extraction. In: Proc. of the 2020 Conf. on Empirical
                     Methods in Natural Language Processing (EMNLP). ACL, 2020. 2339–2349. [doi: 10.18653/v1/2020.emnlp-main.183]
                 [17]   Wang JJ, Sun CL, Li SS, Liu XZ, Si L, Zhang M, Zhou GD. Aspect sentiment classification towards question-answering with reinforced
                     bidirectional attention network. In: Proc. of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: ACL,
                     2019. 3548–3557. [doi: 10.18653/v1/P19-1345]
                 [18]   Wang F, Li YC, Zhang WJ, Zhong SH. A more fine-grained aspect-sentiment-opinion triplet extraction task. arXiv:2103.15255, 2021.
                 [19]   Chen  H,  Zhai  ZP,  Feng  FX,  Li  RF,  Wang  XJ.  Enhanced  multi-channel  graph  convolutional  network  for  aspect  sentiment  triplet
                     extraction. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics, Vol. 1 (Long Papers). Dublin: ACL,
                     2022. 2974–2985. [doi: 10.18653/v1/2022.acl-long.21]
                 [20]   Liang S, Wei W, Mao XL, Fu YY, Fang R, Chen DY. STAGE: Span tagging and greedy inference scheme for aspect sentiment triplet
                     extraction.  In:  Proc.  of  the  37th  AAAI  Conf.  on  Artificial  Intelligence.  Washington:  AAAI,  2023.  13174–13182.  [doi:  10.1609/aaai.
                     v37i11.26547]
                 [21]   Mao Y, Shen Y, Yang JC, Zhu XY, Cai LJ. Seq2Path: Generating sentiment tuples as paths of a tree. In: Findings of the Association for
                     Computational Linguistics: ACL 2022. Dublin: ACL, 2022. 2215–2225. [doi: 10.18653/v1/2022.findings-acl.174]
                 [22]   Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A,
                     Grave E, Lample G. LLaMA: Open and efficient foundation language models. arXiv:2302.13971, 2023.
                 [23]   Du  ZX,  Qian  YJ,  Liu  X,  Ding  M,  Qiu  JZ,  Yang  ZL,  Tang  J.  GLM:  General  language  model  pretraining  with  autoregressive  blank
                     infilling. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics, Vol. 1 (Long Papers). Dublin: ACL,
                     2022. 320–335. [doi: 10.18653/v1/2022.acl-long.26]
   364   365   366   367   368   369   370   371   372   373   374