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Further consideration of efficiency? #25

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XiaoqingNLP opened this issue Feb 8, 2023 · 0 comments
Open

Further consideration of efficiency? #25

XiaoqingNLP opened this issue Feb 8, 2023 · 0 comments

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@XiaoqingNLP
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In this paper, the author considered efficiency, while Inference is still time-consuming. This is because each sample x with a length of n will create 8n * k templates.

The source sequence of the model is an input text X = {x1, . . . , xn} and the target sequence Tyk,xi:j = {t1, . . . , tm} is a template filled by candidate text span xi:j and the entity type yk.

For efficiency, we restrict the number of n-grams for a span from one to eight, so 8n templates are created for each sentence.

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