diff --git a/utils/grounding.py b/utils/grounding.py
index 62ed497..99d23d9 100644
--- a/utils/grounding.py
+++ b/utils/grounding.py
@@ -6,10 +6,8 @@
 import json
 import string
 
-
 __all__ = ['create_matcher_patterns', 'ground']
 
-
 # the lemma of it/them/mine/.. is -PRON-
 
 blacklist = set(["-PRON-", "actually", "likely", "possibly", "want",
@@ -17,7 +15,6 @@
                  "one", "something", "sometimes", "everybody", "somebody", "could", "could_be"
                  ])
 
-
 nltk.download('stopwords', quiet=True)
 nltk_stopwords = nltk.corpus.stopwords.words('english')
 
@@ -37,10 +34,12 @@ def load_cpnet_vocab(cpnet_vocab_path):
 
 
 def create_pattern(nlp, doc, debug=False):
-    pronoun_list = set(["my", "you", "it", "its", "your", "i", "he", "she", "his", "her", "they", "them", "their", "our", "we"])
+    pronoun_list = set(
+        ["my", "you", "it", "its", "your", "i", "he", "she", "his", "her", "they", "them", "their", "our", "we"])
     # Filtering concepts consisting of all stop words and longer than four words.
     if len(doc) >= 5 or doc[0].text in pronoun_list or doc[-1].text in pronoun_list or \
-            all([(token.text in nltk_stopwords or token.lemma_ in nltk_stopwords or token.lemma_ in blacklist) for token in doc]):
+            all([(token.text in nltk_stopwords or token.lemma_ in nltk_stopwords or token.lemma_ in blacklist) for token
+                 in doc]):
         if debug:
             return False, doc.text
         return None  # ignore this concept as pattern
@@ -81,7 +80,6 @@ def create_matcher_patterns(cpnet_vocab_path, output_path, debug=False):
 
 
 def lemmatize(nlp, concept):
-
     doc = nlp(concept.replace("_", " "))
     lcs = set()
     # for i in range(len(doc)):
@@ -108,13 +106,15 @@ def load_matcher(nlp, pattern_path):
 
 
 def ground_qa_pair(qa_pair):
-    global nlp, matcher
-    if nlp is None or matcher is None:
+    s, a, pattern_path, cpnet_vocab_path = qa_pair
+
+    global nlp, matcher, CPNET_VOCAB
+    if nlp is None or matcher is None or CPNET_VOCAB is None:
         nlp = spacy.load('en_core_web_sm', disable=['ner', 'parser', 'textcat'])
         nlp.add_pipe(nlp.create_pipe('sentencizer'))
-        matcher = load_matcher(nlp, PATTERN_PATH)
+        matcher = load_matcher(nlp, pattern_path)
+        CPNET_VOCAB = load_cpnet_vocab(cpnet_vocab_path)
 
-    s, a = qa_pair
     all_concepts = ground_mentioned_concepts(nlp, matcher, s, a)
     answer_concepts = ground_mentioned_concepts(nlp, matcher, a)
     question_concepts = all_concepts - answer_concepts
@@ -131,7 +131,6 @@ def ground_qa_pair(qa_pair):
 
 
 def ground_mentioned_concepts(nlp, matcher, s, ans=None):
-
     s = s.lower()
     doc = nlp(s)
     matches = matcher(doc)
@@ -233,10 +232,13 @@ def hard_ground(nlp, sent, cpnet_vocab):
     return res
 
 
-def match_mentioned_concepts(sents, answers, num_processes):
-    res = []
+def match_mentioned_concepts(sents, answers, num_processes, pattern_path, cpnet_vocab_path):
+    input_args = []
+    for s, a in zip(sents, answers):
+        input_args.append((s, a, pattern_path, cpnet_vocab_path))
+
     with Pool(num_processes) as p:
-        res = list(tqdm(p.imap(ground_qa_pair, zip(sents, answers)), total=len(sents)))
+        res = list(tqdm(p.imap(ground_qa_pair, input_args), total=len(sents)))
     return res
 
 
@@ -296,10 +298,10 @@ def prune(data, cpnet_vocab_path):
 
 
 def ground(statement_path, cpnet_vocab_path, pattern_path, output_path, num_processes=1, debug=False):
-    global PATTERN_PATH, CPNET_VOCAB
-    if PATTERN_PATH is None:
-        PATTERN_PATH = pattern_path
-        CPNET_VOCAB = load_cpnet_vocab(cpnet_vocab_path)
+    # global PATTERN_PATH, CPNET_VOCAB
+    # if PATTERN_PATH is None:
+    #     PATTERN_PATH = pattern_path
+    #     CPNET_VOCAB = load_cpnet_vocab(cpnet_vocab_path)
 
     sents = []
     answers = []
@@ -324,7 +326,7 @@ def ground(statement_path, cpnet_vocab_path, pattern_path, output_path, num_proc
                 print(ans)
             answers.append(ans)
 
-    res = match_mentioned_concepts(sents, answers, num_processes)
+    res = match_mentioned_concepts(sents, answers, num_processes, pattern_path, cpnet_vocab_path)
     res = prune(res, cpnet_vocab_path)
 
     # check_path(output_path)