Transformer language models that are trained on vast amounts of data have achieved remarkable success at various NLP benchmarks. Intriguingly, this success is achieved by models that lack an explicit modeling of hierarchical syntactic structures, which were hypothesized by decades of linguistic research to be necessary for good generalization. This naturally leaves a question: to what extent can we further improve the performance of Transformer language models, through an inductive bias that encourages the model to explain the data through the lens of recursive syntactic compositions? Although the benefits of modeling recursive syntax have been shown at the small data and model scales, it remains an open question whether -- and to what extent -- a similar design principle is still beneficial in the case of powerful Transformer language models that work well at scale. To answer these questions, we introduce Transformer Grammars -- a novel class of Transformer language models that combine: (i) the expressive power, scalability, and strong performance of Transformers, and (ii) recursive syntactic compositions, which here are implemented through a special attention mask. We find that Transformer Grammars outperform various strong baselines on multiple syntax-sensitive language modeling evaluation metrics, in addition to sentence-level language modeling perplexity. Nevertheless, we find that the recursive syntactic composition bottleneck harms perplexity on document-level modeling, providing evidence that a different kind of memory mechanism -- that works independently of syntactic structures -- plays an important role in the processing of long-form text.

Transformer Grammars是一种新颖的语言模型，通过特殊的注意力掩码和确定性转换实现递归句法组合，提高了句子级别和句法敏感的语言建模性能，在长文本建模中，递归的句法组合对表示整个句子向量造成了瓶颈并影响了逼近度，表明一个独立于组合句法表示的不同类型的记忆机制在当前成功的模型中发挥了重要作用。

Transformer 语法：在规模上增强具有语法归纳偏见的转换语言模型