I'm currently analysing the paper Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (Post, Vilar 2018): https://arxiv.org/abs/1804.06609 I have understanding problems how the data is processed. For example: the paper is writing about beams, banks and hypothesises and I have no idea what these terms mean. How would you describe these terms and are there any tutorial sources you would recommend for understanding the dynamic beam allocation?

I am new to text processing and stuck on a problem to identify the similarity of columns. To detail the problem, consider we have two columns with string values: Column A | Column B ------------------------------- abcd | xyz foo | bar xyzzy | acct xyz | world onex | foo ... | ... ... | ... The length of columns can be in order of thousands. Is there an approach to identify how similar the columns are? Currently, I am …