Hi,

Thank you for the very nice and interesting work. I have a question regarding the model. In the paper it s mentioned that you used the same architecture as GPT, which is a transformer decoder. However, you also talked about input encoder and how you configure the input to encoder. I was a bit confused, whether you have both encoder and decoder? or it is a language model and then it would be just decoder. And if it is a decoder, then how do you encode your [X_s, X_r] ? Could you please clarify this. Many thanks

I'm having some difficulty reproducing the conceptnet accuracy scores, could you please point me in the right direction? These are the steps I took:

1. Download pretrained COMET model from https://drive.google.com/open?id=1FccEsYPUHnjzmX-Y5vjCBeyRt1pLo8FB
2. Run generations with greedy decoding on conceptnet test set
3. Use Bilinear AVG model by Li et al. (2016) to score generated tuples and threshold by 0.5 to get accuracy

However, because I achieved an accuracy of 80.04%, which is much lower than the 95.25% reported in your Table, I'm sure I must be doing something wrong! The (short) code I used to get the scores is here: https://colab.research.google.com/drive/10oaX-_1qS75xrgbm67MDkRy_hOHEJA0s

Can this model give a knowledge graph according to the input sentence? Such as "One absolutely cardinal reason is the fact that universities offer an opportunity to intensify the knowledge in a particular field of interest." Maybe this is a long sentence, I want to ask if our model has such a function?

In the README.md you need to change the tensorflow and spacy install methods. pip tensorflow downloads a different version than what is needed and works better if you just use conda to install it. Also, the spacy 3.0 that is default install now, doesn't work with your dataloader. Proposed changes:

pip install tensorflow --> conda install tensorflow conda install -c conda-forge spacy --> conda install -c conda-forge spacy=2.3.5

Hello, Sir.

I have something to ask you about this code.

I wonder a reason of why you do not exclude 'none' target answer.

Your code seems to include such data samples like "PersonX loses personX's sight xNeed none " as a training data or test data.

I think learning to generate 'none' dose not seems to meaningful.

And I found same thing in COMET-ATOMIC 2020.

Is there any specific reasons?

Thanks.

Just want to make sure. Are we meant to change the original setting values 17, 35 for ATOMIC to bigger values in order to feed with a longer sentence than 18 words?

Or can we feed with a long sentence?

Here is the detail of the question : File "comet-commonsense/src/interactive/functions.py", line 124, in get_atomic_sequence input_event, category, data_loader, text_encoder) File "comet-commonsense/src/interactive/functions.py", line 158, in set_atomic_inputs XMB[:, :len(prefix)] = torch.LongTensor(prefix) RuntimeError: The expanded size of the tensor (18) must match the existing size (93) at non-singleton dimension 1. Tar get sizes: [1, 18]. Tensor sizes: [93] do you know how to deal with it ? @madaan

Hello!

Thank you for sharing your work! I want to generate xIntent for some sequences. Is there a way that I can give an input file and have the output be stored in another file?

Thanks for your time!

Frank

Dear author : I follow the instruction in the Readme file,when i try to make the data loaders about conceptNet,i came across the question: ~/comet-commonsense$ python scripts/data/make_conceptnet_data_loader.py 100%███████████████████████████████████████████| 100000/100000 [04:06<00:00, 405.46it/s] 100%███████████████████████████████████████████████████| 2400/2400 [00:05<00:00, 408.77it/s] 100%|████████████████████████████████████| 2400/2400 [00:05<00:00, 426.74it/s] 28 16 16 dev Traceback (most recent call last): File "scripts/data/make_conceptnet_data_loader.py", line 66, in data_loader.make_tensors(text_encoder, special, test=False) File "/home/caihanqing/comet-commonsense/src/data/conceptnet.py", line 196, in make_tensors self.data[split]['total']) if not j[3]])) RuntimeError: index out of range: Tried to access index 2306 out of table with 2305 rows. at /opt/conda/conda-bld/pytorch_1570910687230/work/aten/src/TH/generic/THTensorEvenMoreMath.cpp:418 Can you give me some useful advice to my further training ? @madaan @atcbosselut

Hello,

I have a question about MASK tokens in the input examples (Figure 3, paper). Which role do they play in training and predictions. Unfortunately, I could not find the explanation in the paper. What is the role of mask tokens between subject tokens and relation tokens (ConceptNet) when only object tokens need to be predicted? Thank you very much in advance.

Hi, I've tested a number of cases either using code&&parameter from this repo or demo from https://mosaickg.apps.allenai.org/comet_atomic. And I'm kind of confused as there have obvious differences in the results. So what's the exact difference between them? Thanks

Dear authors,

I found you set the max_eventand max_affect to 17 and 35 respectively. However, the length of my input event is much longer than 17, can I change it according to my need?

After scanning your code, I tend to change opt['data']["maxe1"] in main_atomic.py and self.max_event in atomic.py, can it work?

Gongratulations for your work! What part of the code should I use in order to give as input a text file with some new words(begin node) and receive relations with generated new words/concepts (end node)? Thank you.

When I run python -m comet2.train,I got the error : AttributeError: 'OpenAIGPTTokenizerFast' object has no attribute 'added_tokens_encoder'.And I can I fix it ?may be the issue from transformer verison?

Hi,

Seems like token level averaged NLL loss will be logged for all loss types:

https://github.com/atcbosselut/comet-commonsense/blob/070aad114600b36296ef8420325e3d4cef0be470/src/train/train.py#L118

If you can confirm that this is indeed an issue, I can submit a pull request.

Thanks