model.py 文件源码

def _word_repre_layer(self, input):
        """
        args:
            - input: (q_sentence, q_words)|(a_sentence, a_words)
              q_sentence - [batch_size, sent_length]
              q_words - [batch_size, sent_length, words_len]
        return:
            - output: [batch_size, sent_length, context_dim]
        """
        sentence, words = input
        # [batch_size, sent_length, corpus_emb_dim]
        s_encode = self.corpus_emb(sentence)

        # [batch_size, sent_length, word_lstm_dim]
        w_encode = self._word_repre_forward(words)
        w_encode = F.dropout(w_encode, p=self.dropout, training=True, inplace=False)

        out = torch.cat((s_encode, w_encode), 2)
        return out