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Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example (actually most ML LM examples looked better than any RNN generated text I've read so far) and this tiny ML LM was trained on a simpler corpus but you get the idea: straightforward conditional probability generates better texts than far more complex char-based RNN.

Char-based RNN LMs can mimic grammatically correct sequences for a wide range of languages, require bigger hidden layer and computationally more expensive while word-based RNN LMs train faster and generate more coherent texts and yet little of the text these models generate makes actual sense.

Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example (actually most ML LM examples looked better than any RNN generated text I've read so far) and this tiny ML LM was trained on a simpler corpus but you get the idea: straightforward conditional probability generates better texts than far more complex char-based RNN.

Char-based RNN LMs can mimic grammatically correct sequences for a wide range of languages, require bigger hidden layer and computationally more expensive while word-based RNN LMs train faster and generate more coherent texts and yet even these generated texts are far from making actual sense.

Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example and this tiny ML LM was trained on a simpler corpus but you get the idea.

Char-based RNN LMs can mimic grammatically correct sequences for a wide range of languages, require bigger hidden layer and computationally more expensive while word-based RNN LMs train faster and generate more coherent text.

Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example (actually most ML LM examples looked better than any RNN generated text I've read so far) and this tiny ML LM was trained on a simpler corpus but you get the idea: straightforward conditional probability generates better texts than far more complex char-based RNN.

Char-based RNN LMs can mimic grammatically correct sequences for a wide range of languages, require bigger hidden layer and computationally more expensive while word-based RNN LMs train faster and generate more coherent texts and yet little of the text these models generate makes actual sense.

Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example and this tiny ML LM was trained on a simpler corpus but you get the idea.  Char-based RNN LM can mimic grammatically correct sequences for a wide range of languages and yet they fall short when it comes to making actual sense.

Here is what I learnt recently.

Obviously, when talking about text generation RNNs we are talking about RNN language models. When asking about word/char-based text generation RNNs, we are asking about word/char-based RNN language models (LM).

Word-based LMs display higher accuracy and lower computational cost than char-based LMs.

This drop of performance is unlikely due to the difficulty for character level model to capture longer short term memory, since also the Longer Short Term Memory (LSTM) recurrent networks work better with word-based input.

This is because char-based RNN LMs require much bigger hidden layer to successfully model long-term dependencies which means higher computational costs.

Therefore, we can say that

one of the fundamental differences between the word level and character level models is in the number of parameters the RNN has to access during the training and test. The smaller is the input and output layer of RNN, the larger needs to be the fully connected hidden layer, which makes the training of the model expensive.

However, char-based RNN LMs better model languages with a rich morphology such as Finish, Turkish, Russian etc. Using word-based RNN LMs to model such languages is difficult if possible at all and is not advised.

The above analysis makes sense especially when you look at the output text, generated by char-based RNNs:

The surprised in investors weren’t going to raise money. I’m not the company with the time there are all interesting quickly, don’t have to get off the same programmers.

While simple char-based Maximum Likelihood LM with a 13-character window delivers this:

And when she made many solid bricks. He stacked them in piles and stomped her feet. The doctor diagnosed him with a bat. The girl and her boyfriend asked her out.

Of course I cherry-picked the example and this tiny ML LM was trained on a simpler corpus but you get the idea. 

Char-based RNN LMs can mimic grammatically correct sequences for a wide range of languages, require bigger hidden layer and computationally more expensive while word-based RNN LMs train faster and generate more coherent text.