mlsanityData to 14 Oct 2026

SASICM A Multi-Task Benchmark For Subtext Recognition

arXiv 2106.06944 ↗First posted 13 Jun 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

Subtext is a kind of deep semantics which can be acquired after one or more rounds of expression transformation. As a popular way of expressing one's intentions, it is well worth studying. In this paper, we try to make computers understand whether there is a subtext by means of machine learning. We build a Chinese dataset whose source data comes from the popular social media (e.g. Weibo, Netease Music, Zhihu, and Bilibili). In addition, we also build a baseline model called SASICM to deal with subtext recognition. The F1 score of SASICMg, whose pretrained model is GloVe, is as high as 64.37%, which is 3.97% higher than that of BERT based model, 12.7% higher than that of traditional methods on average, including support vector machine, logistic regression classifier, maximum entropy classifier, naive bayes classifier and decision tree and 2.39% higher than that of the state-of-the-art, including MARIN and BTM. The F1 score of SASICMBERT, whose pretrained model is BERT, is 65.12%, which is 0.75% higher than that of SASICMg. The accuracy rates of SASICMg and SASICMBERT are 71.16% and 70.76%, respectively, which can compete with those of other methods which are mentioned before.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 13 Jun 2021.

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References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2018Deep Learning for Sentiment Analysis : A Surveycited by 139
  4. 2017A Large Self-Annotated Corpus for Sarcasmcited by 64
  5. 2018Reasoning with Sarcasm by Reading In-betweencited by 38
  6. 2018CASCADE: Contextual Sarcasm Detection in Online Discussion Forumscited by 26
  7. 2018Practical Text Classification With Large Pre-Trained Language Modelscited by 24
  8. 2020A Report on the 2020 Sarcasm Detection Shared Taskcited by 24
  9. 2016Automatic Sarcasm Detection: A Surveycited by 21
  10. 2019SemEval-2014 Task 9: Sentiment Analysis in Twittercited by 20
  11. 2018Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networkscited by 19
  12. 2019Sentiment and Sarcasm Classification with Multitask Learningcited by 19
6 more
  1. 2019Resolving Gendered Ambiguous Pronouns with BERTcited by 16
  2. 2019SemEval-2016 Task 4: Sentiment Analysis in Twittercited by 14
  3. 2017Harnessing Cognitive Features for Sarcasm Detectioncited by 8
  4. 2017Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detectioncited by 7
  5. 2019An Investigation of Transfer Learning-Based Sentiment Analysis in Japanesecited by 7
  6. 2019Context-aware Embedding for Targeted Aspect-based Sentiment Analysiscited by 6

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