Code Captioning Task

[natalymr]

Hello!

May I ask you about applying your approach to the code captioning task?

You wrote in the article that used CodeNN's dataset and achieved ~23% bleu score. Am I right that the part of dataset for training contains 52812 examples of code snippets? How many epochs had passed before such accuracy was obtained?

[urialon]

Hi Nataly, Sure. Yes, the number of training examples is indeed around 53000, and it took us 31 epochs.

I must warn you that this dataset was very difficult to train the model on, and I recommend not using this dataset. It is small and very noisy. It served us to compare our model to codeNN, but even though our model performed better than the baselines - our trained model wasn't really "good" and useful as the Java method names dataset (presented in code2seq.org demo).

If you are looking for good code captioning datasets, I suggest checking the following: The CONCODE dataset This one from NAACL'19 This MSR one from ICLR'19

Maybe @sriniiyer knows - what is the currently best code-to-NL dataset? Is it CONCODE?

In any case, here are the parameters that we used on the CODENN dataset:

config.WORDS_MIN_COUNT = 20

config.TARGET_WORDS_MIN_COUNT = 2

config.EMBEDDINGS_SIZE = 128 * 4

config.RNN_SIZE = 128 * 4

config.DECODER_SIZE = 512

config.NUM_EXAMPLES = 52299

config.MAX_TARGET_PARTS = 37

config.EMBEDDINGS_DROPOUT_KEEP_PROB = 0.3

config.RNN_DROPOUT_KEEP_PROB = 0.75

[sriniiyer]

Umm, those are good pointers. I'm not aware of other datasets. You can do it on CONCODE, but there isn't a test set with multiple references. Although it would be interesting to see if external class information from CONCODE helps at all in captioning.

[natalymr]

Thanks a lot, @urialon for your answer! And thank you for the really quick response!

I am trying to apply code2seq to the task of generating commit messages, so I collected my own dataset from git. Thanks for the warning about other datasets. I saw this https://github.com/tech-srl/code2seq/issues/15 issue, where you said that it is always good to increase the amount of data for training. I've increased my dataset several times, training began to take about 4 hours for one epoch, so I decided to ask you about your amount of data.

Also, I could not find the following fields: WORDS_MIN_COUNT, TARGET_WORDS_MIN_COUNT, NUM_EXAMPLES in config class. Did you use those fields just to describe the dataset, or are they actually used in the code?

[urialon]

Ah, you're right. WORDS_MIN_COUNT and TARGET_MIN_COUNT were replaced by SUBTOKENS_VOCAB_MAX_SIZE and TARGET_VOCAB_MAX_SIZE. I.e., instead of specifying the minimal count for inserting a word to the vocabulary, we currently specify the overall size, and take only the top-occurring words. I don't have concrete guidelines here, except - limit the vocabulary size such that only words that appear only 1-2 times in the training data will be left outside.

Regarding NUM_EXAMPLES - you can ignore it, it is counted automatically now.

[natalymr]

I'm sorry to bother you again, but could you please tell me how the blue score was changing during training?

Thanks for the help with the fields and vocabulary!

[urialon]

Hmmm, I can't find the original log, but if I remember correctly (on the validation set): After the first epoch - BLEU was about 13-14, and it increased to 20 in about 10 epochs. From 10-30 it mostly fluctuated, with minor improvements every few epochs. But that's very specific to that CODENN dataset.

[villmow]

Hi, I just wondered if you have any BLUE scores of the Code2Seq model trained on the FUNCOM dataset: This one from NAACL'19

Thanks for your help!

[urialon]

No, sorry, code2seq was published earlier.