diff --git a/icefall/shared/make_kn_lm.py b/icefall/shared/make_kn_lm.py
index 58b721d21..c2edd823e 100755
--- a/icefall/shared/make_kn_lm.py
+++ b/icefall/shared/make_kn_lm.py
@@ -165,7 +165,9 @@ class NgramCounts:
                 n1 += stat[1]
                 n2 += stat[2]
             assert n1 + 2 * n2 > 0
-            self.d.append(n1 * 1.0 / (n1 + 2 * n2))
+            self.d.append(max(0.1, n1 * 1.0) / (n1 + 2 * n2))   # We are doing this max(0.001, xxx) to avoid zero discounting constant D due to n1=0, 
+                                                                # which could happen if the number of symbols is small.
+                                                                # Otherwise, zero discounting constant can cause division by zero in computing BOW.
 
     def cal_f(self):
         # f(a_z) is a probability distribution of word sequence a_z.
@@ -241,7 +243,11 @@ class NgramCounts:
                         for u in a_counts_for_hist.word_to_count.keys():  # Should be careful here: what is Z1
                             sum_z1_f_z += _counts_for_hist.word_to_f[u]
 
-                        counts_for_hist.word_to_bow[w] = (1.0 - sum_z1_f_a_z) / (1.0 - sum_z1_f_z)
+                        if sum_z1_f_z < 1:
+                            # assert sum_z1_f_a_z < 1
+                            counts_for_hist.word_to_bow[w] = (1.0 - sum_z1_f_a_z) / (1.0 - sum_z1_f_z)
+                        else:
+                            counts_for_hist.word_to_bow[w] = None
 
     def print_raw_counts(self, info_string):
         # these are useful for debug.