Wrong transition in crf when doing a sequence labeling task

[SefaZeng]

I use the ChainCRF.py as the CRF Layer in my model to do a sequence labeling task using the OBIE as the tags ,but I meet a problemthat there are some unexpected transition in the predict like E to I. And it doesn't show up in the train data. The keras version is 2.2.2.And tensorflow is 1.10.0 the code:

from keras.preprocessing import text, sequence
from keras.layers import *
from keras.models import *
from keras.callbacks import EarlyStopping,ModelCheckpoint
from ChainCRF import ChainCRF
from keras import backend as K

def Bilstm_CNN_Crf(maxlen,nb_words,class_label_count,embedding_weights=None,is_train=True):
    word_input=Input(shape=(maxlen,),dtype='int32',name='word_input')
    word_emb=Embedding(nb_words+1,output_dim=100,\
                    input_length=maxlen,\
                    embeddings_initializer = 'uniform',
                    name='word_emb')(word_input)
    # bilstm
    bilstm=Bidirectional(LSTM(64,return_sequences=True))(word_emb)
    bilstm_d=Dropout(0.1)(bilstm)

    # cnn
    half_window_size=2
    padding_layer=ZeroPadding1D(padding=half_window_size)(word_emb)
    conv=Conv1D(nb_filter=50,filter_length=2*half_window_size+1,\
            padding='valid')(padding_layer)
    conv_d=Dropout(0.1)(conv)
    dense_conv=TimeDistributed(Dense(50))(conv_d)

    # merge
    rnn_cnn_merge=concatenate([bilstm_d,dense_conv])
    dense=TimeDistributed(Dense(class_label_count))(rnn_cnn_merge)

    # crf
    crf = ChainCRF(name='CRF_Layer')
    crf_output=crf(dense)

    # build model
    model=Model(inputs=[word_input],outputs=[crf_output])

    model.compile(loss=crf.loss,optimizer='adam',metrics=['accuracy'])

    # model.summary()

    return model

model = Bilstm_CNN_Crf(maxlen, nb_words, 5)
earlystop = EarlyStopping(monitor='val_acc',patience=2,verbose=1)
checkpoint = ModelCheckpoint('best_model.hdf5',monitor='val_acc',verbose=1,save_best_only=True,period=1,save_weights_only=True)
model.fit(x_train_1, y, epochs=epochs, batch_size=64, verbose=1,validation_data=(x_train_1,y),callbacks=[earlystop,checkpoint])
model.load_weights('best_model.hdf5')
pred_prob = model.predict(x_train_1)
pred = np.argmax(pred_prob, axis=2)

Is there something wrong with the model?Or somet badcase that i didnt find in the data? Any help is appreciate!Thx!

[nreimers]

Hi @SefaZeng This issue also happens with my code: in-valid transitions (e.g. O I-PER) are produced by the BiLSTM-CRF model.

The issue is sadly not trivial and I don't know how to fix it.

The CRF is initialized with random probabilities for the transitions, i.e. O I-PER can be as likely as O B-PER. Of course, the CRF does not know anything from the encoding and about allowed transitions.

During training, these transition probabilities are updated, so that the CRF learns that O I-PER is unlikely. However, it converges rather slowly to a 0 probability. This makes sense, as how should the CRF be able to distinguish that O I-PER is not possible at all and 'it is rare but I haven't seen enough data'.

With more epochs, the number of invalid tags usually converge to a low number or even to zero in my experiments.

As I solution what I use is a post-processing step: The code checks whether the tags from the CRF are valid BIO-encoded. If it finds an invalid tag, it sets this tag to O.

[SefaZeng]

Hi @SefaZeng This issue also happens with my code: in-valid transitions (e.g. O I-PER) are produced by the BiLSTM-CRF model.

The issue is sadly not trivial and I don't know how to fix it.

The CRF is initialized with random probabilities for the transitions, i.e. O I-PER can be as likely as O B-PER. Of course, the CRF does not know anything from the encoding and about allowed transitions.

During training, these transition probabilities are updated, so that the CRF learns that O I-PER is unlikely. However, it converges rather slowly to a 0 probability. This makes sense, as how should the CRF be able to distinguish that O I-PER is not possible at all and 'it is rare but I haven't seen enough data'.

With more epochs, the number of invalid tags usually converge to a low number or even to zero in my experiments.

As I solution what I use is a post-processing step: The code checks whether the tags from the CRF are valid BIO-encoded. If it finds an invalid tag, it sets this tag to O.

Can I set the initial states to zero to avoid this problem?

[nreimers]

@SefaZeng I think that could work, however, you would need to ensure to get the mapping right. Especially when the number of tags changes (e.g. you add B-LOC and I-LOC to your tagset), you must ensure that you set the zeros at the right place. Otherwise it can easily happen that B-LOC => I-LOC is initialized with a zero probability.

Further, the CRF is bi-directional, i.e. not only the previous label is important but also the next label determines which label is produced. This can make it rather complicated to initialize the CRF correctly.

[SefaZeng]

@nreimers Emmm.. I set the initializer of U, b_start, b_end and initial state in the viterbi_decode to zeros,but it doesn't work.Maybe post-processing is the only way. But I am still confusing why it will happen.Because in statistic opinion, if the in-valid transitions never appear in the data,the probability or maybe the weights in the neural network should be very low or only zero.