Multilingual Language Processing From Bytes

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning "from scratch" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text

Sentence boundary detection and the problem with the U.S

Abstract Sentence Boundary Detection is widely used but often with outdated tools. We discuss what makes it difficult, which features are relevant, and present a fully statistical system, now publicly available, that gives the best known error rate on a standard news corpus: Of some 27,000 examples, our system makes 67 errors, 23 involving the word "U.S.&quot