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Computer Science > Computation and Language

arXiv:2307.06962 (cs)
[Submitted on 13 Jul 2023]

Title:Copy Is All You Need

Authors:Tian Lan, Deng Cai, Yan Wang, Heyan Huang, Xian-Ling Mao
Download a PDF of the paper titled Copy Is All You Need, by Tian Lan
and 4 other authors
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    Abstract: The dominant text generation models compose the output
    by sequentially selecting words from a fixed vocabulary. In this
    paper, we formulate text generation as progressively copying text
    segments (e.g., words or phrases) from an existing text
    collection. We compute the contextualized representations of
    meaningful text segments and index them using efficient vector
    search toolkits. The task of text generation is then decomposed
    into a series of copy-and-paste operations: at each time step, we
    seek suitable text spans from the text collection rather than
    selecting from a standalone vocabulary. Experiments on the
    standard language modeling benchmark (WikiText-103) show that our
    approach achieves better generation quality according to both
    automatic and human evaluations. Besides, its inference
    efficiency is comparable to token-level autoregressive models
    thanks to the reduction of decoding steps. We also show that our
    approach allows for effective domain adaptation by simply
    switching to domain-specific text collection without extra
    training. Finally, we observe that our approach attains
    additional performance gains by simply scaling up to larger text
    collections, again without further training.\footnote{Our source
    codes are publicly available at \url{this https URL}.}

Subjects:          Computation and Language (cs.CL); Artificial
                   Intelligence (cs.AI)
Cite as:           arXiv:2307.06962 [cs.CL]
                   (or arXiv:2307.06962v1 [cs.CL] for this version)
                   https://doi.org/10.48550/arXiv.2307.06962
                   Focus to learn more
                   arXiv-issued DOI via DataCite
Journal reference: The Eleventh International Conference on Learning
                   Representations (ICLR 2023)

Submission history

From: Tian Lan [view email]
[v1] Thu, 13 Jul 2023 05:03:26 UTC (2,171 KB)
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