Gold annotation: (i) using gold candidates and verb type vs. automatic; (ii) performance comparison by error type Learning Framework There is a lot of understanding for which algorithmic methods work best for ESL correction tasks, how they compare among themselves, and how they compare to ngram based methods.
despite their intuitive appeal, language models were shown to not work well on these tasks, while the discriminative learning framework has been shown to be superior to other approaches and thus is commonly used for error correction tasks (see Sec.