采用多个门控模块来选择性地提取交互语义信息 为了减轻差异表示对预训练模型的损害,我们利用filter gates来自适应地过滤掉噪声信息,最后生成更好描述句子匹配细节的向量 6. Experimental and Results analysis 6.1 Datasets 作者主要做了语义匹配和模型鲁棒性的实验,用到的数据集分别如下 Semantic Matching GLUE的6个句对...
采用多个门控模块来选择性地提取交互语义信息 为了减轻差异表示对预训练模型的损害,我们利用filter gates来自适应地过滤掉噪声信息,最后生成更好描述句子匹配细节的向量 首先,通过affinity-guided attention来更新差异向量,用a_i和d_i分别表示A和D的第i维,提供每个相似向量a_i与差异表示矩阵D进行交互,并获得新的差异...
采用多个门控模块来选择性地提取交互语义信息 为了减轻差异表示对预训练模型的损害,我们利用filter gates来自适应地过滤掉噪声信息,最后生成更好描述句子匹配细节的向量 首先,通过affinity-guided attention来更新差异向量,用 和分别表示A和D的第维,提供每个相似向量与差异表示矩阵D进行交互,并获得新的差异特征。然后,基...
首先先获得我们所有变量的名称name 比如一个变量<tf.Variable 'rnn/gru_cell/gates/kernel:0' shape=(6, 8) dtype=float32_ref>, 可以得到它的name为'rnn/gru_cell/gates/kernel',上面的正则表达式是为了获取变量名称,看不懂可以参考re链接1链接2。 得到变量名称后,可以获得一个变量名称到变量的字典name_to_...
Cloze task(完形填空)其实这就是bert预训练的一种任务。 SQuAD(Standford Question Answering Dataset) task SQuAD任务传入的是D, Q,其实D是该篇文章,Q是问题,返回的结果是答案开始的位置s以及答案结束的位置e。例如上图第一个问题的答案是gravity, 它的位置是文章中第17个单词,即s=17, e=17 ...
xt is the input variable at time t,ht-1 is the hidden state of the layer at time t-1,ht is the hidden state at time t,ct is the cell state at time t, the notations it,ft,ot,c̃t are the activation vector of input, forget, output and cell gates, and σ is the sigmoid fu...
We got a spot pretty close up to the entrance gates, and headed to the park. We knew it was gonna be a special day tho, when I stopped to take a pic of the Holiday in the Park sign and asked Nick to then take one of me – and a guy with his family stopped and offer...
2,496,505 to Thompson, 2,725,873 to Walter, 3,267,928 to Spooner, 3,580,237 to Barsby, 3,913,663 to Gates, 4,044,820 to Nobles and 4,083,398 to Fallon. As described, the power unit consists of a dual compartment housing assembly which accommodates twin fans powered by a doubl...
the view of Fun Spot Atlanta, after stepping thru the main gates from the parking area Screaming Eagle coaster. . this was such a ton of fun, and a great start to the TPR trip for me. I love me a Paratrooper. I think the cycle at Knoebel's was slightly better (more forces when ...
This requirement opened the gates for Transfer Learning. Carrying out sentiment analysis with BERT Bidirectional Encoder Representations from Transformers Bert is a Contextual model. Instead of generating a single word embedding representation for each word in the vocabulary. It generates the ...