diff --git a/examples/babi_rnn.py b/examples/babi_rnn.py
index 2499447e4005..2b08cb8f4d83 100644
--- a/examples/babi_rnn.py
+++ b/examples/babi_rnn.py
@@ -1,5 +1,6 @@
 from __future__ import absolute_import
 from __future__ import print_function
+from functools import reduce
 import re
 import tarfile
 
@@ -21,6 +22,29 @@
 "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"
 http://arxiv.org/abs/1502.05698
 
+Task Number                  | FB LSTM Baseline | Keras QA
+---                          | ---              | ---
+QA1 - Single Supporting Fact | 50               | 52.1
+QA2 - Two Supporting Facts   | 20               | 37.0
+QA3 - Three Supporting Facts | 20               | 20.5
+QA4 - Two Arg. Relations     | 61               | 62.9
+QA5 - Three Arg. Relations   | 70               | 61.9
+QA6 - Yes/No Questions       | 48               | 50.7
+QA7 - Counting               | 49               | 78.9
+QA8 - Lists/Sets             | 45               | 77.2
+QA9 - Simple Negation        | 64               | 64.0
+QA10 - Indefinite Knowledge  | 44               | 47.7
+QA11 - Basic Coreference     | 72               | 74.9
+QA12 - Conjunction           | 74               | 76.4
+QA13 - Compound Coreference  | 94               | 94.4
+QA14 - Time Reasoning        | 27               | 34.8
+QA15 - Basic Deduction       | 21               | 32.4
+QA16 - Basic Induction       | 23               | 50.6
+QA17 - Positional Reasoning  | 51               | 49.1
+QA18 - Size Reasoning        | 52               | 90.8
+QA19 - Path Finding          | 8                | 9.0
+QA20 - Agent's Motivations   | 91               | 90.7
+
 For the resources related to the bAbI project, refer to:
 https://research.facebook.com/researchers/1543934539189348
 
@@ -67,7 +91,7 @@ def parse_stories(lines, only_supporting=False):
     data = []
     story = []
     for line in lines:
-        line = line.strip()
+        line = line.decode('utf-8').strip()
         nid, line = line.split(' ', 1)
         nid = int(nid)
         if nid == 1:
@@ -137,7 +161,7 @@ def vectorize_stories(data):
 train = get_stories(tar.extractfile(challenge.format('train')))
 test = get_stories(tar.extractfile(challenge.format('test')))
 
-vocab = sorted(reduce(lambda x, y: x | y, (set(story + q) for story, q, answer in train + test)))
+vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train + test)))
 # Reserve 0 for masking via pad_sequences
 vocab_size = len(vocab) + 1
 word_idx = dict((c, i + 1) for i, c in enumerate(vocab))