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Re-implementation of Attention-Based Convolutional Neural Network (ABCNN) by Chainer

Python 16.15% Jupyter Notebook 83.85%

chainer convolutional-neural-networks

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kurtespinosa dhvfanny

chainer-abcnn's Issues

Question about vocab variable in the dataprocessor.py file

Dear Butsugiri,

Thank you for sharing your code. I just have a clarification about dataprocessor.vocab variable. After running the following lines:

data_processor = DataProcessor(args.data, args.vocab, args.test, args.max_length)
data_processor.prepare_dataset()
data_processor.compute_max_length()
train_data = data_processor.train_data
dev_data = data_processor.dev_data
test_data = data_processor.test_data

dataprocessor.vocab variable only has 2 entries and and hence this will be the input to the model creation.

cnn = ABCNN(n_vocab=len(vocab), embed_dim=embed_dim, input_channel=input_channel,
           output_channel=50, x1s_len=x1s_len, x2s_len=x2s_len, model_type=model_type, single_attention_mat=args.single_attention_mat)  # ABCNNはoutput = 50固定らしいが．
model = Classifier(cnn, lossfun=sigmoid_cross_entropy,
                     accfun=binary_accuracy)
if args.glove:
    cnn.load_glove_embeddings(args.glove_path, data_processor.vocab)
if args.word2vec:
    cnn.load_word2vec_embeddings(args.word2vec_path, data_processor.vocab)
if args.gpu >= 0:
    cuda.get_device(args.gpu).use()
    model.to_gpu()
cnn.set_pad_embedding_to_zero(data_processor.vocab)

Sorry, I haven't finished reading the whole code but I wonder at this point if that is the intention of that variable or it should have contained all the vocab in the dataset?

Cheers,
Kurt

Clarification in the jsonify.py code

Dear Butsugiri,

Thank you for sharing your code. I have a question about the input dataset which I would need to jsonify. I download the dataset and used the respective data partitions, for example, WikiQA-test.tsv for test set which has a sample file entry below.

QuestionID Question DocumentID DocumentTitle SentenceID Sentence Label
Q0 HOW AFRICAN AMERICANS WERE IMMIGRATED TO THE US D0 African immigration to the United States D0-0 African immigration to the United States refers to immigrants to the United States who are or were nationals of Africa . 0

Now, I'm confused because in the jsonify code, the question would point to D0-0 which is the sentenceID. It seems that the question_id and the question were interchanged, am I right or did I miss out anything?

question_id = data[1]
....
question = data[-3]
answer = data[-2]
....
....
'question': question.lower().split(" "),
'answer': answer.lower().split(" "),

should have been the following?

question = data[1]
.....
question_id = data[-3]
answer = data[-2]
....
....
'question': question.lower().split(" "),
'answer': answer.lower().split(" "),

Cheers,
Kurt

Data

Could you upload the data?

Question about vocab of dataprocessor.py

Dear Butsugiri,

Thank you for sharing your code. I just have a clarification about dataprocessor.vocab variable. After running the following lines:

data_processor = DataProcessor(args.data, args.vocab, args.test, args.max_length)
data_processor.prepare_dataset()
data_processor.compute_max_length()
train_data = data_processor.train_data
dev_data = data_processor.dev_data
test_data = data_processor.test_data

dataprocessor.vocab variable only has 2 entries and and hence this will be the input to the model creation.

cnn = ABCNN(n_vocab=len(vocab), embed_dim=embed_dim, input_channel=input_channel,
           output_channel=50, x1s_len=x1s_len, x2s_len=x2s_len, model_type=model_type, single_attention_mat=args.single_attention_mat)  # ABCNNはoutput = 50固定らしいが．
model = Classifier(cnn, lossfun=sigmoid_cross_entropy,
                     accfun=binary_accuracy)
if args.glove:
    cnn.load_glove_embeddings(args.glove_path, data_processor.vocab)
if args.word2vec:
    cnn.load_word2vec_embeddings(args.word2vec_path, data_processor.vocab)
if args.gpu >= 0:
    cuda.get_device(args.gpu).use()
    model.to_gpu()
cnn.set_pad_embedding_to_zero(data_processor.vocab)

Sorry, I haven't finished reading the whole code but I wonder at this point if that is the intention of that variable or it should have contained all the vocab in the dataset?

Cheers,
Kurt

Question: unexpecting results using MPR

Hi,
first of all than you for sharing a code, I was testing your code using Microsoft Research Paraphrase corpus that was tested in the original article as well. But I have a really poor result - accuracy only about 67% that is equal to the major voting strategy, as the majority class is in about 66% cases.

Have you tried your code on this dataset?

butsugiri / chainer-abcnn Goto Github PK

chainer-abcnn's People

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chainer-abcnn's Issues

Question about vocab variable in the dataprocessor.py file

Clarification in the jsonify.py code

Data

Question about vocab of dataprocessor.py

Question: unexpecting results using MPR

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