diff --git a/src/main/python/khaiii/resource/resource.py b/src/main/python/khaiii/resource/resource.py
index 110ee70..4d1099b 100644
--- a/src/main/python/khaiii/resource/resource.py
+++ b/src/main/python/khaiii/resource/resource.py
@@ -23,13 +23,8 @@
 #############
 # constants #
 #############
-SPECIAL_CHARS = [
-    '<u>',    # unknown character
-    '<w>', '</w>',    # begin/end of word
-    '<s>', '</s>'    # begin/end of sentence
-]
-
-PAD_CHR = '<p>'    # sepcial character for padding
+UNK_CHR = '@@UNKNOWN@@'
+SPECIAL_CHARS = ['<w>', '</w>']    # begin/end of word
 
 
 #########
@@ -45,9 +40,9 @@ def __init__(self, cfg: Namespace):
             cfg:  config
         """
         vocab_in_path = '{}/vocab.in'.format(cfg.rsc_src)
-        self.vocab_in = Vocabulary(vocab_in_path, cfg.cutoff, SPECIAL_CHARS)
+        self.vocab_in = Vocabulary(vocab_in_path, cfg.cutoff, UNK_CHR, SPECIAL_CHARS)
         vocab_out_path = '{}/vocab.out'.format(cfg.rsc_src)
-        self.vocab_out = Vocabulary(vocab_out_path, 0, None)
+        self.vocab_out = Vocabulary(vocab_out_path)    # no unknown, no special
         restore_dic_path = '{}/restore.dic'.format(cfg.rsc_src)
         self.restore_dic = self._load_restore_dic(restore_dic_path)
 
diff --git a/src/main/python/khaiii/resource/vocabulary.py b/src/main/python/khaiii/resource/vocabulary.py
index eb7a67c..aa492b0 100644
--- a/src/main/python/khaiii/resource/vocabulary.py
+++ b/src/main/python/khaiii/resource/vocabulary.py
@@ -11,7 +11,6 @@
 ###########
 # imports #
 ###########
-import copy
 import logging
 import os
 from typing import List
@@ -24,26 +23,25 @@ class Vocabulary:
     """
     vocabulary class
     """
-    def __init__(self, path: str, cutoff: int = 1, special: List[str] = None, padding: str = ''):
+    def __init__(self, path: str, cutoff: int = 1, unk: str = '', special: List[str] = None):
         """
+        padding index is always 0. None and '' get padding index.
+        if `unk` is given (such as input vocab), its index is always 1.
+        if `unk` is not given (such as output vocab), an exception will be thrown for unknown entry
         Args:
             path:  file path
             cutoff:  cutoff frequency
+            unk:  unknown(OOV) entry
             special:  special entries located at the first
-            padding:  add padding special char at the end
         """
         self.dic = {}    # {entry: number} dictionary
-        self.rev = copy.deepcopy(special) if special else []    # reverse dictionary
+        self.unk = unk
+        self.rev = ['', unk] if unk else ['', ]    # reverse dictionary
+        if special:
+            self.rev.extend(special)
         for num, entry in enumerate(self.rev):
             self.dic[entry] = num
         self._load(path, cutoff)
-        self.padding = padding
-        if padding:
-            if padding in self.dic:
-                raise ValueError('padding special character already in vocab: {}'.format(padding))
-            padding_idx = len(self.dic)
-            self.dic[padding] = padding_idx
-            self.rev.append(padding)
         assert len(self.dic) == len(self.rev)
 
     def __getitem__(self, key):
@@ -57,22 +55,14 @@ def __getitem__(self, key):
             return self.rev[key]
         try:
             return self.dic[key]
-        except KeyError:
-            return 0    # unknown word number
+        except KeyError as key_err:
+            if self.unk:
+                return self.dic[self.unk]
+            raise key_err
 
     def __len__(self):
         return len(self.dic)
 
-    def padding_idx(self) -> int:
-        """
-        맨 마지막에 추가한 패딩의 인덱스를 리턴한다.
-        Returns:
-            패딩 인덱스
-        """
-        if not self.padding:
-            raise RuntimeError('vocabulary has no padding')
-        return self.dic[self.padding]
-
     def _load(self, path: str, cutoff: int = 1):
         """
         load vocabulary from file
diff --git a/src/main/python/khaiii/train/dataset.py b/src/main/python/khaiii/train/dataset.py
index 7aee785..a1b1612 100644
--- a/src/main/python/khaiii/train/dataset.py
+++ b/src/main/python/khaiii/train/dataset.py
@@ -12,6 +12,7 @@
 # imports #
 ###########
 from argparse import Namespace
+import itertools
 import logging
 import os
 import random
@@ -20,7 +21,7 @@
 from torch import LongTensor, Tensor    # pylint: disable=no-member, no-name-in-module
 from tqdm import tqdm
 
-from khaiii.resource.resource import PAD_CHR, Resource
+from khaiii.resource.resource import Resource
 from khaiii.train.sentence import PosSentence, PosWord
 
 
@@ -44,97 +45,129 @@ def __len__(self):
             return sum([len(w.raw) for w in self.pos_tagged_words]) + len(self.pos_tagged_words) + 1
         return 0
 
-    def make_labels(self, with_spc: bool) -> List[str]:
+    def make_contexts(self, window: int) -> List[List[str]]:
         """
-        각 음절별로 출력 레이블(태그)를 생성한다.
+        각 음절 별로 좌/우 window 크기 만큼 context를 만든다.
         Args:
-            with_spc:  공백(어절 경계) 포함 여부
+            window:  left/right window size
         Returns:
-            레이블 리스트
+            contexts
         """
-        if not with_spc:
-            # 문장 경계, 어절 경계 등 가상 음절을 제외하고 순수한 음절들의 레이블
-            return [tag for pos_word in self.pos_tagged_words for tag in pos_word.tags]
-        labels = [PAD_CHR, ]    # 문장 시작
-        for pos_word in self.pos_tagged_words:
-            if len(labels) > 1:
-                labels.append(PAD_CHR)    # 어절 경계
-            labels.extend(pos_word.tags)
-        labels.append(PAD_CHR)    # 문장 종료
-        return labels
-
-    def make_contexts(self, window: int, spc_dropout: float) -> List[str]:
+        chars = [c for w in self.words for c in w]
+        chars_len = len(chars)
+        chars_padded = ['', ] * window + chars + ['', ] * window
+        contexts = [chars_padded[idx-window:idx+window+1]
+                    for idx in range(window, chars_len + window)]
+        return contexts
+
+    @classmethod
+    def _flatten(cls, list_of_lists):
+        """
+        flatten one level of nesting
+        Args:
+            list_of_lists:  list of lists
+        Returns:
+            flattened list
+        """
+        return list(itertools.chain.from_iterable(list_of_lists))
+
+    def make_left_spc_masks(self, window: int, left_vocab_id: int, spc_dropout: float) \
+            -> List[List[int]]:
         """
         각 음절 별로 좌/우 window 크기 만큼 context를 만든다.
         Args:
             window:  left/right window size
-            spc_dropout:  space(word delimiter) dropout rate
+            left_vocab_id:  vocabulary ID for '<w>'
+            spc_dropout:  space dropout rate
         Returns:
-            contexts
+            left space masks
         """
-        contexts = []
-        for wrd_idx, word in enumerate(self.words):
-            for chr_idx, char in enumerate(word):
-                left_context = list(reversed(word[:chr_idx]))
-                if random.random() >= spc_dropout:
-                    left_context.append('<w>')
-                for left_word in reversed(self.words[:wrd_idx]):
-                    left_context.extend(reversed(left_word))
-                    if len(left_context) >= window:
-                        break
-                if len(left_context) < window:
-                    left_context.extend(['<s>', ] * (window - len(left_context)))
-                left_context = list(reversed(left_context[:window]))
-                assert len(left_context) == window
-
-                right_context = list(word[chr_idx+1:])
-                if random.random() >= spc_dropout:
-                    right_context.append('</w>')
-                for right_word in self.words[wrd_idx+1:]:
-                    right_context.extend(list(right_word))
-                    if len(right_context) >= window:
-                        break
-                if len(right_context) < window:
-                    right_context.extend(['</s>', ] * (window - len(right_context)))
-                right_context = right_context[:window]
-                assert len(right_context) == window
-                contexts.append(left_context + [char, ] + right_context)
-        return contexts
-
-    def to_tensor(self, cfg: Namespace, rsc: Resource, is_train: bool) -> Tuple[Tensor, Tensor]:
+        def _filter_left_spc_mask(left_spc_mask):
+            """
+            중심 음절로부터 첫번째 왼쪽 공백만 남기고 나머지는 제거한다.
+            Args:
+                left_spc_mask:  왼쪽 공백 마스크
+            """
+            for idx in range(window, -1, -1):
+                if left_spc_mask[idx] == left_vocab_id:
+                    if random.random() < spc_dropout:
+                        left_spc_mask[idx] = 0
+                    for jdx in range(idx-1, -1, -1):
+                        left_spc_mask[jdx] = 0
+                    break
+
+        left_spcs = self._flatten([[left_vocab_id, ] + [0, ] * (len(word)-1)
+                                   for word in self.words])
+        left_padded = [0, ] * window + left_spcs + [0, ] * window
+        left_spc_masks = [left_padded[idx-window:idx+1] + [0, ] * window
+                          for idx in range(window, len(left_spcs) + window)]
+        for left_spc_mask in left_spc_masks:
+            _filter_left_spc_mask(left_spc_mask)
+        return left_spc_masks
+
+    def make_right_spc_masks(self, window: int, right_vocab_id: int, spc_dropout: float) \
+            -> List[List[int]]:
+        """
+        각 음절 별로 좌/우 window 크기 만큼 context를 만든다.
+        Args:
+            window:  left/right window size
+            right_vocab_id:  vocabulary ID for '</w>'
+            spc_dropout:  space dropout rate
+        Returns:
+            right space masks
+        """
+        def _filter_right_spc_mask(right_spc_mask):
+            """
+            중심 음절로부터 첫번째 오른쪽 공백만 남기고 나머지는 제거한다.
+            Args:
+                right_spc_mask:  오른쪽 공백 마스크
+            """
+            for idx in range(window, len(right_spc_mask)):
+                if right_spc_mask[idx] == right_vocab_id:
+                    if random.random() < spc_dropout:
+                        right_spc_mask[idx] = 0
+                    for jdx in range(idx+1, len(right_spc_mask)):
+                        right_spc_mask[jdx] = 0
+                    break
+
+        right_spcs = self._flatten([[0, ] * (len(word)-1) + [right_vocab_id, ]
+                                    for word in self.words])
+        right_padded = [0, ] * window + right_spcs + [0, ] * window
+        right_spc_masks = [[0, ] * window + right_padded[idx:idx+window+1]
+                           for idx in range(window, len(right_spcs) + window)]
+        for right_spc_mask in right_spc_masks:
+            _filter_right_spc_mask(right_spc_mask)
+        return right_spc_masks
+
+    def to_tensor(self, cfg: Namespace, rsc: Resource, do_spc_dropout: bool) \
+            -> Tuple[Tensor, Tensor, Tensor, Tensor]:
         """
         문장 내에 포함된 전체 음절들과 태그를 모델의 forward 메소드에 넣을 수 있는 텐서로 변환한다.
         Args:
             cfg:  config
             rsc:  Resource object
-            is_train:  whether is train or not
+            do_spc_dropout:  whether do space dropout or not
         Returns:
             labels tensor
             contexts tensor
+            left space masks tensor
+            right space masks tensor
         """
         # 차원: [문장내 음절 갯수, ]
-        label_nums = [rsc.vocab_out[l] for l in self.make_labels(False)]
+        label_nums = [rsc.vocab_out[tag] for pos_word in self.pos_tagged_words \
+                                         for tag in pos_word.tags]
         labels_tensor = LongTensor(label_nums)
         # 차원: [문장내 음절 갯수 x context 크기]
-        spc_dropout = cfg.spc_dropout if is_train else 0.0
-        context_nums = [[rsc.vocab_in[c] for c in context] \
-                        for context in self.make_contexts(cfg.window, spc_dropout)]
+        contexts = self.make_contexts(cfg.window)
+        context_nums = [[rsc.vocab_in[c] for c in context] for context in contexts]
         contexts_tensor = LongTensor(context_nums)
-        return labels_tensor, contexts_tensor
+        spc_dropout = cfg.spc_dropout if do_spc_dropout else 0.0
+        left_spc_masks = self.make_left_spc_masks(cfg.window, rsc.vocab_in['<w>'], spc_dropout)
+        left_spc_masks_tensor = LongTensor(left_spc_masks)
+        right_spc_masks = self.make_right_spc_masks(cfg.window, rsc.vocab_in['</w>'], spc_dropout)
+        right_spc_masks_tensor = LongTensor(right_spc_masks)
 
-    def make_chars(self) -> List[str]:
-        """
-        문장 내 포함된 음절들을 만든다. 문장 경계 및 어절 경계를 포함한다.
-        Returns:
-            음절의 리스트
-        """
-        chars = ['<s>', ]    # 문장 시작
-        for word in self.words:
-            if len(chars) > 1:
-                chars.append('<w>')    # 어절 경계
-            chars.extend(word)
-        chars.append('</s>')    # 문장 종료
-        return chars
+        return labels_tensor, contexts_tensor, left_spc_masks_tensor, right_spc_masks_tensor
 
 
 class PosDataset:
diff --git a/src/main/python/khaiii/train/embedder.py b/src/main/python/khaiii/train/embedder.py
index 61acf2d..b58cd7b 100644
--- a/src/main/python/khaiii/train/embedder.py
+++ b/src/main/python/khaiii/train/embedder.py
@@ -33,17 +33,22 @@ def __init__(self, cfg: Namespace, rsc: Resource):
         super().__init__()
         self.cfg = cfg
         self.rsc = rsc
-        self.embedding = nn.Embedding(len(rsc.vocab_in), cfg.embed_dim)
+        self.embedding = nn.Embedding(len(rsc.vocab_in), cfg.embed_dim, 0)
 
     def forward(self, inputs):    # pylint: disable=arguments-differ
         """
         임베딩을 생성하는 메소드
         Args:
-            inputs:  contexts of batch size
+            inputs:  batch size list of (context, left space mask, right space mask)
         Returns:
             embedding
         """
-        embeds = self.embedding(inputs)
+        contexts, left_spc_masks, right_spc_masks = inputs
+        embeds = self.embedding(contexts)
+        embeds += self.embedding(left_spc_masks)
+        embeds += self.embedding(right_spc_masks)
+        # 왼쪽과 오른쪽 패딩에는 zero 벡터인데 아래 positional encoding이 더해짐
+        # 사소하지만 아래도 패딩 영역에 대해 마스킹 후 더해줘야 하지 않을까?
         embeds += positional_encoding(self.cfg.context_len, self.cfg.context_len,
                                       self.cfg.embed_dim, 1)
         return embeds
diff --git a/src/main/python/khaiii/train/evaluator.py b/src/main/python/khaiii/train/evaluator.py
index 33f5fe3..3596039 100644
--- a/src/main/python/khaiii/train/evaluator.py
+++ b/src/main/python/khaiii/train/evaluator.py
@@ -38,9 +38,12 @@ def evaluate(self) -> Tuple[float, float, float]:
         """
         char_acc = self.cnt['match_chars'] / self.cnt['total_chars']
         word_acc = self.cnt['match_words'] / self.cnt['total_words']
-        recall = self.cnt['match_morphs'] / self.cnt['total_gold_morphs']
-        precision = self.cnt['match_morphs'] / self.cnt['total_pred_morphs']
-        f_score = 2.0 * recall * precision / (recall + precision)
+        if self.cnt['match_morphs'] == 0:
+            recall = precision = f_score = 0.0
+        else:
+            recall = self.cnt['match_morphs'] / self.cnt['total_gold_morphs']
+            precision = self.cnt['match_morphs'] / self.cnt['total_pred_morphs']
+            f_score = 2.0 * recall * precision / (recall + precision)
         self.cnt.clear()
         return char_acc, word_acc, f_score
 
@@ -104,13 +107,17 @@ def morphs_to_set(cls, morphs: List[PosMorph]) -> set:
     def report(self, fout: TextIO):
         """
         report recall/precision to file
-        :param  fout:  output file
+        Args:
+            fout:  output file
         """
         print('word accuracy: %d / %d = %.4f' % (self.cnt['match_words'], self.cnt['total_words'],
                                                  self.cnt['match_words'] / self.cnt['total_words']),
               file=fout)
-        recall = self.cnt['match_morphs'] / self.cnt['total_gold_morphs']
-        precision = self.cnt['match_morphs'] / self.cnt['total_pred_morphs']
-        f_score = 2.0 * recall * precision / (recall + precision)
+        if self.cnt['match_morphs'] == 0:
+            recall = precision = f_score = 0.0
+        else:
+            recall = self.cnt['match_morphs'] / self.cnt['total_gold_morphs']
+            precision = self.cnt['match_morphs'] / self.cnt['total_pred_morphs']
+            f_score = 2.0 * recall * precision / (recall + precision)
         print('f-score / (recall, precision): %.4f / (%.4f, %.4f)' % (f_score, recall, precision),
               file=fout)
diff --git a/src/main/python/khaiii/train/models.py b/src/main/python/khaiii/train/models.py
index c1b3222..b4a9695 100644
--- a/src/main/python/khaiii/train/models.py
+++ b/src/main/python/khaiii/train/models.py
@@ -88,15 +88,15 @@ def __init__(self, cfg: Namespace, rsc: Resource):
         # hidden => tag
         self.hidden2tag = nn.Linear(cfg.hidden_dim, len(rsc.vocab_out))
 
-    def forward(self, contexts):    # pylint: disable=arguments-differ
+    def forward(self, inputs):    # pylint: disable=arguments-differ
         """
         forward path
         Args:
-            contexts:  batch size list of character and context
+            inputs:  batch size list of (context, left space mask, right space mask)
         Returns:
             output score
         """
-        embeds = self.embedder(contexts)
+        embeds = self.embedder(inputs)
         embeds_t = embeds.transpose(1, 2)
 
         pool_outs = []
@@ -105,7 +105,7 @@ def forward(self, contexts):    # pylint: disable=arguments-differ
             pool_outs.append(F.max_pool1d(conv_out, conv_out.size(2)))
 
         # conv => hidden
-        features = torch.cat([p.view(contexts.size(0), -1) for p in pool_outs], dim=1)    # pylint: disable=no-member
+        features = torch.cat([p.view(embeds.size(0), -1) for p in pool_outs], dim=1)    # pylint: disable=no-member
         features_drop = F.dropout(features)
         hidden_out = F.relu(self.conv2hidden(features_drop))
 
diff --git a/src/main/python/khaiii/train/tagger.py b/src/main/python/khaiii/train/tagger.py
index c0997d8..6e45a1c 100644
--- a/src/main/python/khaiii/train/tagger.py
+++ b/src/main/python/khaiii/train/tagger.py
@@ -54,10 +54,12 @@ def tag_raw(self, raw_sent: str, enable_restore: bool = True) -> PosSentTensor:
             PosSentTensor object
         """
         pos_sent = PosSentTensor(raw_sent)
-        _, contexts = pos_sent.to_tensor(self.cfg, self.rsc, False)
+        _, contexts, left_spc_masks, right_spc_masks = pos_sent.to_tensor(self.cfg, self.rsc, False)
         if torch.cuda.is_available():
             contexts = contexts.cuda()
-        outputs = self.model(contexts)
+            left_spc_masks = left_spc_masks.cuda()
+            right_spc_masks = right_spc_masks.cuda()
+        outputs = self.model((contexts, left_spc_masks, right_spc_masks))
         _, predicts = F.softmax(outputs, dim=1).max(1)
         tags = [self.rsc.vocab_out[t.item()] for t in predicts]
         pos_sent.set_pos_result(tags, self.rsc.restore_dic if enable_restore else None)
diff --git a/src/main/python/khaiii/train/trainer.py b/src/main/python/khaiii/train/trainer.py
index 6bc2f37..d95d0d5 100644
--- a/src/main/python/khaiii/train/trainer.py
+++ b/src/main/python/khaiii/train/trainer.py
@@ -152,7 +152,7 @@ def _restore_prev_train(self):
             if not line:
                 continue
             (epoch, loss_train, loss_dev, acc_char, acc_word, f_score, learning_rate) = \
-                    line.split('\t')
+                line.split('\t')
             self.cfg.epoch = int(epoch) + 1
             self.cfg.best_epoch = self.cfg.epoch
             self.loss_trains.append(float(loss_train))
@@ -184,7 +184,7 @@ def train(self):
         self.log_file = open('{}/log.tsv'.format(self.cfg.out_dir), 'at')
         self.sum_wrt = SummaryWriter(self.cfg.out_dir)
         patience = self.cfg.patience
-        for _ in range(1000000):
+        for _ in range(100000):
             is_best = self._train_epoch()
             if is_best:
                 patience = self.cfg.patience
@@ -226,13 +226,16 @@ def _train_epoch(self) -> bool:
         loss_trains = []
         for train_sent in tqdm(self.dataset_train, 'EPOCH[{}]'.format(self.cfg.epoch),
                                len(self.dataset_train), mininterval=1, ncols=100):
-            train_labels, train_contexts = train_sent.to_tensor(self.cfg, self.rsc, True)
+            train_labels, train_contexts, left_spc_masks, right_spc_masks = \
+                    train_sent.to_tensor(self.cfg, self.rsc, True)
             if torch.cuda.is_available():
                 train_labels = train_labels.cuda()
                 train_contexts = train_contexts.cuda()
+                left_spc_masks = left_spc_masks.cuda()
+                right_spc_masks = right_spc_masks.cuda()
 
             self.model.train()
-            train_outputs = self.model(train_contexts)
+            train_outputs = self.model((train_contexts, left_spc_masks, right_spc_masks))
             batches.append((train_labels, train_outputs))
             if sum([batch[0].size(0) for batch in batches]) < self.cfg.batch_size:
                 continue
@@ -347,11 +350,14 @@ def evaluate(self, is_dev: bool) -> Tuple[float, float, float, float]:
         losses = []
         for sent in dataset:
             # 만약 spc_dropout이 1.0 이상이면 공백을 전혀 쓰지 않는 것이므로 평가 시에도 적용한다.
-            labels, contexts = sent.to_tensor(self.cfg, self.rsc, self.cfg.spc_dropout >= 1.0)
+            labels, contexts, left_spc_masks, right_spc_masks = \
+                    sent.to_tensor(self.cfg, self.rsc, self.cfg.spc_dropout >= 1.0)
             if torch.cuda.is_available():
                 labels = labels.cuda()
                 contexts = contexts.cuda()
-            outputs = self.model(contexts)
+                left_spc_masks = left_spc_masks.cuda()
+                right_spc_masks = right_spc_masks.cuda()
+            outputs = self.model((contexts, left_spc_masks, right_spc_masks))
             loss = self.criterion(outputs, labels)
             losses.append(loss.item())
             _, predicts = F.softmax(outputs, dim=1).max(1)
@@ -360,4 +366,5 @@ def evaluate(self, is_dev: bool) -> Tuple[float, float, float, float]:
             pred_sent.set_pos_result(pred_tags, self.rsc.restore_dic)
             self.evaler.count(sent, pred_sent)
         avg_loss = sum(losses) / len(losses)
-        return (avg_loss, ) + self.evaler.evaluate()
+        char_acc, word_acc, f_score = self.evaler.evaluate()
+        return avg_loss, char_acc, word_acc, f_score