Seq2Edits
Seq2Edits
**Seq2Edits** is an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts. In this approach, each sequence-to-sequence transduction is represented as a sequence of edit operations, where each operation either replaces an entire source span with target tokens or keeps it unchanged. For text normalization, sentence fusion, sentence splitting & rephrasing, text simplification, and grammatical error correction, the approach improves explainability by associating each edit operation with a human-readable tag.

Rather than generating the target sentence as a series of tokens, the model predicts a sequence of edit operations that, when applied to the source sentence, yields the target sentence. Each edit operates on a span in the source sentence and either copies, deletes, or replaces it with one or more target tokens. Edits are generated auto-regressively from left to right using a modified [Transformer](https://paperswithcode.com/method/transformer) architecture to facilitate learning of long-range dependencies.
Seq2Edits: Sequence Transduction Using Span-level Edit Operations
https://paperswithcode.com/paper/seq2edits-sequence-transduction-using-span
Seq2Edits: Sequence Transduction Using Span-level Edit Operations