We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.

The task of Statistical Machine Translation depends on large amounts of training corpora. Despite the availability of several parallel corpora, these are typically composed of declarative sentences, which may not be appropriate when the goal is to translate other types of sentences, e.g., interrogatives. There have been efforts to create corpora of questions, specially in the context of the evaluation of Question-Answering systems. One of those corpora is the UIUC dataset, composed of nearly 6,000 questions, widely used in the task of Question Classification. In this work, we make available the Portuguese version of the UIUC dataset, which we manually translated, as well as the translation guidelines. We show the impact of this corpus in the performance of a state-of-the-art SMT system when translating questions. Finally, we present a taxonomy of translation errors, according to which we analyze the output of the automatic translation before and after using the corpus as training data.