# SASICM A Multi-Task Benchmark For Subtext Recognition

arXiv: https://arxiv.org/abs/2106.06944 · HTML: https://mlsanity.com/paper/2106.06944/

## 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.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Reasoning with Sarcasm by Reading In-between](https://mlsanity.com/paper/1805.02856/) (2018)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks](https://mlsanity.com/paper/1808.09238/) (2018)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Resolving Gendered Ambiguous Pronouns with BERT](https://mlsanity.com/paper/1906.01161/) (2019)
- [SemEval-2016 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.01973/) (2019)
- [Harnessing Cognitive Features for Sarcasm Detection](https://mlsanity.com/paper/1701.05574/) (2017)
- [Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection](https://mlsanity.com/paper/1709.00575/) (2017)
- [An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese](https://mlsanity.com/paper/1905.09642/) (2019)
- [Context-aware Embedding for Targeted Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1906.06945/) (2019)

## 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.