Compressive Transformers for Long-Range Sequence ModellingDownload PDF

Sep 25, 2019 (edited Feb 10, 2022)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • Code: [![Papers with Code](/images/pwc_icon.svg) 4 community implementations](https://paperswithcode.com/paper/?openreview=SylKikSYDH)
  • Data: [PG-19](https://paperswithcode.com/dataset/pg-19), [Billion Word Benchmark](https://paperswithcode.com/dataset/billion-word-benchmark), [BookCorpus](https://paperswithcode.com/dataset/bookcorpus), [CBT](https://paperswithcode.com/dataset/cbt), [Hutter Prize](https://paperswithcode.com/dataset/hutter-prize), [LAMBADA](https://paperswithcode.com/dataset/lambada), [NarrativeQA](https://paperswithcode.com/dataset/narrativeqa), [WikiText-103](https://paperswithcode.com/dataset/wikitext-103)
  • TL;DR: Long-range transformer using a compressive memory, achieves sota in wikitext-103 and enwik8 LM benchmarks, release a new book-level LM benchmark PG-19.
  • Abstract: We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19.
  • Keywords: memory, language modeling, transformer, compression
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