soujanyaporia / mustard Goto Github PK
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Home Page: https://www.aclweb.org/anthology/P19-1455/
License: MIT License
Multimodal Sarcasm Detection Dataset
Home Page: https://www.aclweb.org/anthology/P19-1455/
License: MIT License
Hi,
Please convert the code into a Google Collab notebook and drop a link in the README. Love to play with it but it's not clear what the sizes of dataset and model would be and I have a tiny MBP so would be great to run on Collab.
Can
Hi, I am working with a project that uses your methods for feature extraction on facial features. I am wondering how to extract the 512 resnet features using your model. When I extract features using the process described in the visual folder I get 2048 values.
Kind regards
Below is the error i get when i try to run the file train_svm.py
_pickle.UnpicklingError: the STRING opcode argument must be quoted
Can yu please suggest ?
Why is there no sound in some videos in the data? This means that some data is missing audio.
Is it possible for you to release the annotator-level annotation?
It might be very helpful for the research community :)
Hi
Thank you for your work and code
Could I get the context_final data by following the Visual Feature Extraction steps?
It seems that I could only get the features/utterances_final hdf5 data.
Did I miss anything in the process?
Thanks.
It seems that there is no validation set for model optimization.
Though I can find the argument of val_split = 0.1
in the config.py
file, I cannot find where it is called to form the validation set in other files.
For the Speaker-dependent setting, I can get the idea of Cross Validation. But for Speaker-independent setting, how would you select the model?
Hi,
I was trying to play around with the data set. But I was confused regarding the shape of the dataset.
I understand that the shape of the feature-vectors for context
is
(#samples x #sentences x feature-vector-size)
But on the other hand this was true only for Text and Visual. I was not sure for audio.
Can you please clarify regarding the same.?
Also I would like to know is there anyway I could get the features word wise?
Thank you.
Hi
Thank you for your work and code
I tried to reproduce the results shown in th paper but noticed large degradations of performance among all configs.
For example, I got
weighted avg 0.574 0.584 0.573 356
for independent T+A
weighted avg 0.602 0.587 0.589 356
for independent T+V
Weighted Precision: 0.483 Weighted Recall: 0.472 Weighted F score: 0.472
for dependent T
Weighted Precision: 0.629 Weighted Recall: 0.626 Weighted F score: 0.626
for dependent T+V
Did I miss anything or could you suggest some training tricks?
Thanks
Is it possible to see the audio extraction python script to fully analyze how it works in detail?
As well, how were you able to reduce the laugh track as per your paper "Then we remove background noise from the signal
by applying a heuristic vocal-extraction method."
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