Transformer-XL: Attentive Language Models Beyond A Fixed-Length ...

Computer Science > Machine Learning arXiv:1901.02860 (cs) [Submitted on 9 Jan 2019 (v1), last revised 2 Jun 2019 (this version, v3)] Title:Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Authors:Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov View a PDF of the paper titled Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, by Zihang Dai and 5 other authors View PDF

Abstract: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 dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.

Comments:	ACL 2019 long paper. Code and pretrained models are available at this https URL
Subjects:	Machine Learning (cs.LG); Computation and Language (cs.CL); Machine Learning (stat.ML)
Cite as:	arXiv:1901.02860 [cs.LG]
(or arXiv:1901.02860v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.1901.02860 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Zihang Dai [view email] [v1] Wed, 9 Jan 2019 18:28:19 UTC (2,124 KB) [v2] Fri, 18 Jan 2019 18:38:00 UTC (2,126 KB) [v3] Sun, 2 Jun 2019 21:21:48 UTC (2,457 KB) Full-text links:

Access Paper:

View a PDF of the paper titled Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, by Zihang Dai and 5 other authors
• View PDF
• TeX Source

view license Current browse context: cs.LG < prev | next > new | recent | 2019-01 Change to browse by: cs cs.CL stat stat.ML

References & Citations

• NASA ADS
• Google Scholar
• Semantic Scholar

4 blog links

(what is this?)

DBLP - CS Bibliography

listing | bibtex Zihang DaiZhilin YangYiming YangJaime G. CarbonellQuoc V. Le … export BibTeX citation

BibTeX formatted citation

× loading... Data provided by:

Bookmark

Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?)

• Author
• Venue
• Institution
• Topic

About arXivLabs arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)