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#introduction Hi, I am swan314. I'm from Japan. I am a natural language processing engineer. I'm interested in applied mathematics, especially, mathematical modeling and that's why I choose this instance. I am interested in seeing a wide range of information on mathematics. Maybe I will post less frequently, but I think I will post something about computer science. Nice to meet you.

The book is Japanese translation version of "Mathematics: Frontiers and Perspectives" from AMS. It is written at Roger Penrose section.

This central affiliation bias results in the DK effect, which is no longer the explanation for psychological effects such as "overconfident people are less capable".

There are two variables, self evaluation(x) and real evaluation(y). DK effect assumes that the difference x-y negatively correlate y itself. If x and y are uniform distribution, it is artificially correlate negatively. And then, adding an assumption that "x is distributed around max(x)/2", the correlation is stronger.

The rule base that just stores sentence pairs that match perfectly against their company-made test data don't have generalization performance.

One day, a company claimed that they had achieved BLEU 88. Their method simply used a translation memory to perfectly match the sentences in the test data, so it is not an accurate representation of translation performance.

Originally it was a system presented at an event called NLP2021, but the source code is not yet available, so I am trying to reproduce it personally.

https://www.anlp.jp/proceedings/annual_meeting/2021/pdf_dir/E9-2.pdf

Just a tech. I'm from Japan.

Joined Apr 2021