See's Word Generation Probability Function
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A See's Word Generation Probability Function is a Word Generation Probability Function that is defined as:
[math]\displaystyle{ p_{gen} = \sigma(w^T_{h^∗} h^∗_t +w^T_s s_t +w^T_x x_t +b_{ptr}) }[/math]
where vectors $w_{h^∗}$ , $w_s$ , $w_x$ and scalar $b_{ptr}$ are learnable parameters and $\sigma$ is the sigmoid function.
- Example(s):
- …
- Counter-Example(s):
- See: Pointer-Generator Network, Neural Machine Translation, Encoder-Decoder Neural Network, Artificial Neural Network, Natural Language Processing Task, Language Model, Summarization NLP Task.
References
2017
- (See et al., 2017) ⇒ Abigail See, Peter J. Liu, and Christopher D. Manning. (2017). “Get To The Point: Summarization with Pointer-Generator Networks.” In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). DOI:10.18653/v1/P17-1099.
- QUOTE: Our pointer-generator network is a hybrid between our baseline and a pointer network (Vinyals et al., 2015), as it allows both copying words via pointing, and generating words from a fixed vocabulary. In the pointer-generator model (depicted in Figure 3) the attention distribution $a^t$ and context vector $h^∗_t$ are calculated as in section 2.1. In addition, the generation probability $p_{gen} \in [0,1]$ for timestep $t$ is calculated from the context vector $h^∗_t$ , the decoder state $s_t$ and the decoder input $x_t$ :[math]\displaystyle{ p_{gen} = \sigma(w^T_{h^∗} h^∗_t +w^T_s s_t +w^T_x x_t +b_{ptr})\quad\quad }[/math] (8)
where vectors $w_{h^∗}$ , $w_s$ , $w_x$ and scalar $b_{ptr}$ are learnable parameters and $\sigma$ is the sigmoid function (...)
- QUOTE: Our pointer-generator network is a hybrid between our baseline and a pointer network (Vinyals et al., 2015), as it allows both copying words via pointing, and generating words from a fixed vocabulary. In the pointer-generator model (depicted in Figure 3) the attention distribution $a^t$ and context vector $h^∗_t$ are calculated as in section 2.1. In addition, the generation probability $p_{gen} \in [0,1]$ for timestep $t$ is calculated from the context vector $h^∗_t$ , the decoder state $s_t$ and the decoder input $x_t$ :
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