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.

通过基于 LSTM 的 Byte-to-Span 模型，我们能够读取以 byte 形式表示的文本，并输出 [start, length, label] 的 span annotations，由于直接操作 unicode bytes 而非语言特定的单词或字符，所以我们能够用单个模型分析多种语言。这些多语言模型非常紧凑且不需要任何自然语言处理的标准流程（包括分词），因此能够独立运行于原始文本上，并在命名实体识别和词性标注等方面产生类似或更好的结果。