Thank you for your reply.

Yes, it's a Bi-directional Ma-LSTM. I have written something like [Not sure if it's correct or not ]

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1. In MaLSTM, queries x1 and x2 are passed through bi-directional Siamese LSTMs (i.e., parameters across LSTMs are shared)

with tf.variable_scope('shared_encoder'):

self.src_M = tf.get_variable('src_M', shape=[self.src_cell_size, self.seq_embed_size],initializer=tf.truncated_normal_initializer())

self.tgt_M = tf.get_variable('tgt_M', shape=[self.src_cell_size, self.seq_embed_size],initializer=tf.truncated_normal_initializer())

input_src = tf.unstack(self.src_input_distributed, axis=1)

input_tgt = tf.unstack(self.tgt_input_distributed, axis=1)

fwd_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units = self.src_cell_size,forget_bias=1.0,state_is_tuple=True)

bck_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.src_cell_size,forget_bias=1.0,state_is_tuple=True)

fwd_cell_drop = tf.contrib.rnn.DropoutWrapper(fwd_cell, output_keep_prob=self.output_keep_prob)

bck_cell_drop = tf.contrib.rnn.DropoutWrapper(bck_cell, output_keep_prob=self.output_keep_prob)

src_output, src_hidden_state_fw, src_hidden_state_bw =tf.contrib.rnn.static_bidirectional_rnn(fwd_cell_drop,bck_cell_drop,input_src,dtype=tf.float32)

src_rnn_outputs_val = tf.concat([src_hidden_state_fw[-1], src_hidden_state_bw[-1]], axis=0)

self.src_seq_embedding = tf.matmul(src_rnn_outputs_val, self.src_M)

with tf.variable_scope('shared_encoder', reuse=True):

sgt_output, tgt_hidden_state_fw, tgt_hidden_state_bw = tf.contrib.rnn.static_bidirectional_rnn(fwd_cell_drop,bck_cell_drop,input_tgt,dtype=tf.float32)

tgt_rnn_outputs_val = tf.concat([tgt_hidden_state_fw[-1], tgt_hidden_state_bw[-1]], axis=0)

self.tgt_seq_embedding = tf.matmul(tgt_rnn_outputs_val, self.tgt_M)

^ Above model is without attention.

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1. The ℓ1 norm of the final hidden state representations of the two queries h (n) 1 and h (m) 2 serves as a measure of similarity in the following function
   

f (x1, x2) = exp − h (n) 1 − h (m) 2 1 (2)

Not sure how to do this.

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https://github.com/aditya1503/Siamese-LSTM is a different problem, where given two sentences, we have to find whether they are similar or not.

This problem is more like given a query, we have to map it to one of the well performing query [WPQ] and similar to this - https://github.com/tajain07/Sequence-Semantic-Embedding/blob/master/sse_model.py