Question about obtaining language model priors

[seongminp]

Hello again. Thank you for sharing your work!

I have carefully read your paper (and looked through your code), but I fail to understand how LM priors are actually calculated. (Going from lstm logits -> distribution over vocab)

It seems to be calculated in this code snippet: https://github.com/cindyxinyiwang/deep-latent-sequence-model/blob/8a798582b1af5ef7f6ac4ca1f2138fd382a1cb06/src/model.py#L339

When you obtain the gumbel logits and log_softmax them, I guess they become probability distributions of input x. What I fail to grasp is the exact format of the distribution.

For every logit dimension (hidden dim), do we get a separate distribution? Or do we get a single distribution (over all possible tokens in the vocab) for each word in sequence? If so why is there a sum function..?

I’d appreciate it greatly if you could shed some light on this.

Thank you!

[jxhe]

Hi,

log_p0 is a single distribution (over all possible tokens in the vocab) for each word in sequence, tgt is one-hot vectors at its last dimension, thus in:

https://github.com/cindyxinyiwang/deep-latent-sequence-model/blob/8a798582b1af5ef7f6ac4ca1f2138fd382a1cb06/src/model.py#L354

log_p0 is of shape (batch_size, seq_len, vocab_size), the first sum (sum(dim=2)) actually indexes the distribution with tgt as the index, to obtain a tensor with shape (batch_size, seq_len), which is the log likelihood of each token. The second sum (sum(dim=1)) sums all the log likelihood on the seq_len dimension, to return a tensor ll0 of shape (batch_size) which represents the log likelihood of each sentence.

[seongminp]

Thank you so much for the explanation!

So when training the inference network q(latent y given observed x), do you not use teacher forcing during the forward pass that obtains latent y? (Meaning there is no pre-completed decoder input and latent y is created autoregressively by the inference network).

We want inference network to generate latent y in a similar way as the lm prior, but to obtain what the lm prior has to say about latent y, we need latent y in the first place. So I was wondering non-autoregressive forward pss of the inference network was at all possible.

I did not understand what to put as the decoder input in this case. I understand that we do give the transfer direction ‘c’ to the decoder.