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Article summary:
1. The article proposes a Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism (MM-R) for Emotion-Cause Pair Extraction (ECPE).
2. The MM-R framework can model complicated relations between emotions and causes while avoiding generating the pairing matrix, which is the leading cause of label sparsity problem.
3. Extensive experiments on the benchmark emotion cause corpus demonstrate the effectiveness of the proposed framework, outperforming existing state-of-the-art methods.
Article analysis:
The article is written in a clear and concise manner, providing an overview of the proposed Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism (MM-R) for Emotion-Cause Pair Extraction (ECPE). The authors provide evidence to support their claims by citing extensive experiments on the benchmark emotion cause corpus that demonstrate the effectiveness of their proposed framework. Furthermore, they provide a detailed explanation of how their framework works and how it avoids generating the pairing matrix, which is the leading cause of label sparsity problem.
In terms of trustworthiness and reliability, there are no obvious biases or unsupported claims in this article. All claims are supported by evidence from experiments conducted on a benchmark emotion cause corpus. Additionally, all points are presented in an unbiased manner and both sides of any argument are explored equally. There is also no promotional content or partiality present in this article. However, it should be noted that possible risks associated with using this framework have not been discussed in detail in this article.