Grammar as a Foreign Language

Code

• github.com/gongshuangshuang/deep-text-corrector
  tf★ 1
• github.com/kmadathil/sanskrit_parser
  none★ 0
• github.com/KaustuvDash/DeepTextCorrector
  none★ 0
• github.com/atpaino/deep-text-corrector
  tf★ 0
• github.com/sunnysinghnitb/text_corrector_software
  tf★ 0
• github.com/sunnysinghnitb/text-corrector-software
  tf★ 0
• github.com/sambit9238/deep_text_corrector
  tf★ 0

Abstract

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.

Tasks

Benchmark Results

Reproductions