Transformer-XL: Language Modeling with Longer-Term Dependency
We propose a novel neural architecture, Transformer-XL, for modeling longer-term dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning…
ArticlesLanguage ModelingLanguage ModellingDocTime: A Document-level Temporal Dependency Graph Parser
We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8% on three…
Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages
Despite the fact that Transformers perform well in NLP tasks, recent studies suggest that self-attention is theoretically limited in learning even some regular and context-free languages. These findings motivated us to t…
Blockwise Parallel Transformer for Large Context Models
Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the sel…
Language ModelingLanguage ModellingDependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding constituency-based structures to Transfo…
ARCInductive BiasLanguage ModelingLanguage Modelling