diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py
index c40b01dfa..47b4f9fd0 100755
--- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py
+++ b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py
@@ -480,6 +480,7 @@ def main():
 
     # <blk> is defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py
index 6ecc8d420..0f4aad163 100644
--- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py
+++ b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py
@@ -550,8 +550,8 @@ class EmformerAttention(nn.Module):
             Cached attention value of left context from preceding computation,
             with shape (L, B, D).
           pos_emb (torch.Tensor):
-            Position encoding embedding, with shape (PE, D).
-            For infer mode, PE = L+2*U-1.
+            Position encoding embedding, with shape (PE, D),
+            where PE = L + 2 * U - 1.
 
         Returns:
           A tuple containing 4 tensors:
@@ -1264,6 +1264,10 @@ class EmformerEncoder(nn.Module):
               right_context at the end.
         """
         U = x.size(0) - self.right_context_length
+
+        # for query of [utterance] (i), key-value [utterance] (j),
+        # the max relative distance i - j is U - 1
+        # the min relative distance i - j is -(U - 1)
         x, pos_emb = self.encoder_pos(x, pos_len=U, neg_len=U)
 
         right_context = self._gen_right_context(x)
@@ -1329,8 +1333,12 @@ class EmformerEncoder(nn.Module):
             f"expected size of {self.chunk_length + self.right_context_length} "
             f"for dimension 1 of x, but got {x.size(1)}."
         )
+
         pos_len = self.chunk_length + self.left_context_length
         neg_len = self.chunk_length
+        # for query of [utterance] (i), key-value [left_context, utterance] (j),
+        # the max relative distance i - j is L + U - 1
+        # the min relative distance i - j is -(U - 1)
         x, pos_emb = self.encoder_pos(x, pos_len=pos_len, neg_len=neg_len)
 
         right_context = x[self.chunk_length :]