I have some feedback after read your code

[koliaok]

I have some feedback after read your NADST code review

first, your NADST paper page 6, and 3.3 State Decoder

you explained like this "where Wgen ∈ R3d×1 and Zexp is the expanded vector of Z to match dimensions of Zds×fert."

I think more explained Z_exp, don't enough explain it in paper

your real code :

• generators.py
• line 57: context_vec = (encoded_context.unsqueeze(1).expand_as(expanded_pointer_attn) * expanded_pointer_attn).sum(2)

I think, need to more explain above code from "expanded_pointer_attn variable" for multiply expanded encoding context vector

How about that?

[henryhungle]

Hi @koliaok, sorry for the confusion. In the paper, we try to describe our models in a concise way as there is the page limit. The representation encoded_context is expanded by simply stacking by |Zdsxfert| times as we want the resulting stack to be compatible with the attention matrix pointer_attn. The stacked vector is used to obtain the weighted sum of dialogue context embedding. https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L57 The weighted sum is then concatenated to the other 2 representations to obtain a 3xd dimensional representation. https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L58 This code is not a major part of our idea anyway. It is mainly to address the OOV problem in DST generation using a traditional pointer network.

[koliaok]

Ok Thank you for response

Have nicely Henry

On May 18, 2020, at 18:39, (Henry) Hung Le notifications@github.com wrote:

Hi @koliaok https://github.com/koliaok, sorry for the confusion. In the paper, we try to describe our models in a concise way as there is the page limit. The representation encoded_context is expanded by simply stacking by |Zdsxfert| times as we want the resulting stack to be compatible with the attention matrix pointer_attn. The stacked vector is used to obtain the weighted sum of dialogue context embedding. https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L57 https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L57 The weighted sum is then concatenated to the other 2 representations to obtain a 3xd dimensional representation. https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L58 https://github.com/henryhungle/NADST/blob/afdc1d1f7ecb855b03933e441c0b2fcefbc28feb/model/generators.py#L58 This code is not a major part of our idea anyway. It is mainly to address the OOV problem in DST generation using a traditional pointer network.

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