model performance on long word sequences

[tech-srl]

model performance on long word sequences

[urialon]

Hi,
Please provide more details:

1. Which data did you use
2. Which model did you use
3. Did you trained the model yourself? If so, did you change any parameters from the default values?
4. What is the problem?

[shangqing-liu]

Hi, I used my own data on C projects and trained the model by myself. The best f1 seems to reach only 23% and the blue score is much slower.

[shangqing-liu]

Sir, may I ask you a question what is the meaning of --max_path_length 8 --max_path_width 2 in your preprocess.sh
thanks very much

[urialon]

You can try increasing the number of decoder layers (config.NUM_DECODER_LAYERS) to 2 and their size ( config.DECODER_SIZE ) to 512 in the config file.
Adding more data always helps...

Regarding path length and width -
Please see the paragraph "Path length and width" in Section 4.2 in the PLDI paper

[shangqing-liu]

sir, have you tested your method on large functions, the experimental results seem to be worse than small functions.

[urialon]

Yes, please see Section 4.1, paragraph "Sensitivity to input length," in the code2seq paper.