1,989 research outputs found
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
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments like mobile phones, we showcase simple techniques for obtaining
such small neural network models, and investigate different tradeoffs when
deciding how to allocate a small memory budget.Comment: EMNLP 2017 short pape
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