diff --git a/common/dataset.py b/common/dataset.py
index 318a232..4faf4bc 100644
--- a/common/dataset.py
+++ b/common/dataset.py
@@ -31,12 +31,12 @@ class DatasetFactory(object):
     @staticmethod
     def get_dataset(dataset_name, word_vectors_dir, word_vectors_file, batch_size, device, castor_dir="./", utils_trecqa="utils/trec_eval-9.0.5/trec_eval"):
         if dataset_name == 'sick':
-            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'sick/')
+            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'sick/')
             train_loader, dev_loader, test_loader = SICK.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
             embedding = nn.Embedding.from_pretrained(SICK.TEXT_FIELD.vocab.vectors)
             return SICK, embedding, train_loader, test_loader, dev_loader
         elif dataset_name == 'msrvid':
-            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'msrvid/')
+            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'msrvid/')
             dev_loader = None
             train_loader, test_loader = MSRVID.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
             embedding = nn.Embedding.from_pretrained(MSRVID.TEXT_FIELD.vocab.vectors)
@@ -44,14 +44,14 @@ class DatasetFactory(object):
         elif dataset_name == 'trecqa':
             if not os.path.exists(os.path.join(castor_dir, utils_trecqa)):
                 raise FileNotFoundError('TrecQA requires the trec_eval tool to run. Please run get_trec_eval.sh inside Castor/utils (as working directory) before continuing.')
-            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'TrecQA/')
+            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'TrecQA/')
             train_loader, dev_loader, test_loader = TRECQA.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
             embedding = nn.Embedding.from_pretrained(TRECQA.TEXT_FIELD.vocab.vectors)
             return TRECQA, embedding, train_loader, test_loader, dev_loader
         elif dataset_name == 'wikiqa':
             if not os.path.exists(os.path.join(castor_dir, utils_trecqa)):
                 raise FileNotFoundError('TrecQA requires the trec_eval tool to run. Please run get_trec_eval.sh inside Castor/utils (as working directory) before continuing.')
-            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'WikiQA/')
+            dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'WikiQA/')
             train_loader, dev_loader, test_loader = WikiQA.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
             embedding = nn.Embedding.from_pretrained(WikiQA.TEXT_FIELD.vocab.vectors)
             return WikiQA, embedding, train_loader, test_loader, dev_loader