diff --git a/test/experimental/test_vocab.py b/test/experimental/test_vocab.py
index d091d7796b..afb140c71e 100644
--- a/test/experimental/test_vocab.py
+++ b/test/experimental/test_vocab.py
@@ -1,9 +1,7 @@
 # -*- coding: utf-8 -*-
 from collections import OrderedDict
 import os
-import platform
 import torch
-import unittest
 from test.common.torchtext_test_case import TorchtextTestCase
 from torchtext.experimental.vocab import (
     vocab,
@@ -20,18 +18,12 @@ def tearDown(self):
     def test_has_unk(self):
         c = OrderedDict()
         v = vocab(c)
-
-        # check if unk is mapped to the first index
-        self.assertEqual(v['not_in_it'], 0)
         self.assertEqual(v['<unk>'], 0)
 
     def test_new_unk(self):
         c = OrderedDict()
         v = vocab(c, unk_token="<new_unk>")
-
-        # check if new_unk is mapped to the first index
         self.assertEqual(v['<new_unk>'], 0)
-        self.assertEqual(v['not_in_it'], 0)
 
     def test_vocab_membership(self):
         token_to_freq = {'<unk>': 2, 'a': 2, 'b': 2}
@@ -54,6 +46,50 @@ def test_vocab_get_item(self):
         self.assertEqual(v['a'], 1)
         self.assertEqual(v['b'], 2)
 
+    def test_reassign_token(self):
+        token_to_freq = {'<unk>': 1, 'a': 2, 'b': 2}
+        sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
+        c = OrderedDict(sorted_by_freq_tuples)
+        v = vocab(c, min_freq=1)
+
+        self.assertEqual(v['<unk>'], 2)
+        self.assertEqual(v['a'], 0)
+        self.assertEqual(v['b'], 1)
+        v.reassign_token('<unk>', 0)
+        self.assertEqual(v['<unk>'], 0)
+        self.assertEqual(v['a'], 1)
+        self.assertEqual(v['b'], 2)
+
+        self.assertEqual(v.get_itos(), ['<unk>', 'a', 'b'])
+
+        with self.assertRaises(RuntimeError):
+            v.reassign_token('not in vocab', 0)
+
+        with self.assertRaises(RuntimeError):
+            v.reassign_token('<unk>', 3)
+
+    def test_default_index(self):
+        token_to_freq = {'<unk>': 2, 'a': 2, 'b': 2}
+        sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
+        c = OrderedDict(sorted_by_freq_tuples)
+        v = vocab(c, min_freq=2)
+
+        self.assertTrue(v.get_default_index() is None)
+        with self.assertRaises(RuntimeError):
+            v['not in vocab']
+
+        v.set_default_index(0)
+        self.assertEqual(v['not in vocab'], 0)
+
+    def test_default_index_jit(self):
+        token_to_freq = {'<unk>': 2, 'a': 2, 'b': 2}
+        sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
+        c = OrderedDict(sorted_by_freq_tuples)
+        v = vocab(c, min_freq=2)
+        v.set_default_index(0)
+        v_jit = torch.jit.script(v)
+        self.assertEqual(v_jit['not in vocab'], 0)
+
     def test_vocab_insert_token(self):
         c = OrderedDict({'<unk>': 2, 'a': 2})
 
@@ -88,6 +124,10 @@ def test_vocab_append_token(self):
         self.assertEqual(v.get_itos(), expected_itos)
         self.assertEqual(dict(v.get_stoi()), expected_stoi)
 
+        # token must not exist to be appended
+        with self.assertRaises(RuntimeError):
+            v.append_token('b')
+
     def test_vocab_len(self):
         token_to_freq = {'a': 2, 'b': 2, 'c': 2}
         sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
@@ -149,6 +189,8 @@ def test_vocab_lookup_token(self):
         v = vocab(c)
 
         self.assertEqual(v.lookup_token(1), 'a')
+        with self.assertRaises(RuntimeError):
+            v.lookup_token(100)
 
     def test_vocab_lookup_tokens(self):
         token_to_freq = {'a': 2, 'b': 2, 'c': 2}
@@ -172,24 +214,6 @@ def test_vocab_lookup_indices(self):
 
         self.assertEqual(v.lookup_indices(tokens), expected_indices)
 
-    # we separate out these errors because Windows runs into seg faults when propagating
-    # exceptions from C++ using pybind11
-    @unittest.skipIf(platform.system() == "Windows", "Test is known to fail on Windows.")
-    def test_errors_vocab_cpp(self):
-        token_to_freq = {'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}
-        sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
-        c = OrderedDict(sorted_by_freq_tuples)
-
-        with self.assertRaises(RuntimeError):
-            # Test proper error raised when setting a token out of bounds
-            v = vocab(c, min_freq=3)
-            v.insert_token('new_token', 100)
-
-        with self.assertRaises(RuntimeError):
-            # Test proper error raised when looking up a token out of bounds
-            v = vocab(c)
-            v.lookup_token(100)
-
     def test_errors_vocab_python(self):
         token_to_freq = {'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}
         sorted_by_freq_tuples = sorted(token_to_freq.items(), key=lambda x: x[1], reverse=True)
@@ -205,6 +229,7 @@ def test_vocab_load_and_save(self):
 
         c = OrderedDict(sorted_by_freq_tuples)
         v = vocab(c, min_freq=3)
+        v.set_default_index(0)
 
         expected_itos = ['<unk>', 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']
         expected_stoi = {x: index for index, x in enumerate(expected_itos)}
@@ -218,6 +243,7 @@ def test_vocab_load_and_save(self):
             loaded_v = torch.load(vocab_path)
             self.assertEqual(v.get_itos(), expected_itos)
             self.assertEqual(dict(loaded_v.get_stoi()), expected_stoi)
+            self.assertEqual(v['not in vocab'], 0)
 
         with self.subTest('torchscript'):
             vocab_path = os.path.join(self.test_dir, 'vocab_torchscript.pt')
@@ -227,6 +253,7 @@ def test_vocab_load_and_save(self):
             loaded_v = torch.load(vocab_path)
             self.assertEqual(v.get_itos(), expected_itos)
             self.assertEqual(dict(loaded_v.get_stoi()), expected_stoi)
+            self.assertEqual(v['not in vocab'], 0)
 
     def test_build_vocab_iterator(self):
         iterator = [['hello', 'hello', 'hello', 'freq_low', 'hello', 'world', 'world', 'world', 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T',
diff --git a/torchtext/csrc/register_bindings.cpp b/torchtext/csrc/register_bindings.cpp
index 0b325bcda6..cf6656d12a 100644
--- a/torchtext/csrc/register_bindings.cpp
+++ b/torchtext/csrc/register_bindings.cpp
@@ -15,12 +15,10 @@ namespace py = pybind11;
 
 namespace {
 Vocab build_vocab_from_text_file(const std::string &file_path,
-                                 const std::string &unk_token,
                                  const int64_t min_freq, const int64_t num_cpus,
                                  py::object fn) {
   torch::jit::script::Module module(*torch::jit::as_module(fn));
-  return _build_vocab_from_text_file(file_path, unk_token, min_freq, num_cpus,
-                                     module);
+  return _build_vocab_from_text_file(file_path, min_freq, num_cpus, module);
 }
 } // namespace
 
@@ -104,23 +102,27 @@ PYBIND11_MODULE(_torchtext, m) {
           }));
 
   py::class_<Vocab, c10::intrusive_ptr<Vocab>>(m, "Vocab")
-      .def(py::init<std::vector<std::string>, std::string>())
+      .def(py::init<StringList, c10::optional<int64_t>>())
       .def_readonly("itos_", &Vocab::itos_)
-      .def_readonly("unk_token_", &Vocab::unk_token_)
-      .def("__contains__",
-           [](c10::intrusive_ptr<Vocab> &self, const py::str &item) -> bool {
-             ssize_t length;
-             const char *buffer = PyUnicode_AsUTF8AndSize(item.ptr(), &length);
-             return self->__contains__(c10::string_view{buffer, (size_t)length});
-           })
+      .def_readonly("default_index_", &Vocab::default_index_)
+      .def(
+          "__contains__",
+          [](c10::intrusive_ptr<Vocab> &self, const py::str &item) -> bool {
+            ssize_t length;
+            const char *buffer = PyUnicode_AsUTF8AndSize(item.ptr(), &length);
+            return self->__contains__(c10::string_view{buffer, (size_t)length});
+          })
       .def("__getitem__",
            [](c10::intrusive_ptr<Vocab> &self, const py::str &item) -> int64_t {
              ssize_t length;
              const char *buffer = PyUnicode_AsUTF8AndSize(item.ptr(), &length);
              return self->__getitem__(c10::string_view{buffer, (size_t)length});
            })
-      .def("__len__", &Vocab::__len__)
+      .def("reassign_token", &Vocab::reassign_token)
       .def("insert_token", &Vocab::insert_token)
+      .def("set_default_index", &Vocab::set_default_index)
+      .def("get_default_index", &Vocab::get_default_index)
+      .def("__len__", &Vocab::__len__)
       .def("append_token", &Vocab::append_token)
       .def("lookup_token", &Vocab::lookup_token)
       .def("lookup_tokens", &Vocab::lookup_tokens)
@@ -234,15 +236,18 @@ TORCH_LIBRARY_FRAGMENT(torchtext, m) {
           });
 
   m.class_<Vocab>("Vocab")
-      .def(torch::init<StringList, std::string>())
+      .def(torch::init<StringList, c10::optional<int64_t>>())
       .def("__contains__",
            [](const c10::intrusive_ptr<Vocab> &self, const std::string &item)
                -> bool { return self->__contains__(c10::string_view{item}); })
       .def("__getitem__",
            [](const c10::intrusive_ptr<Vocab> &self, const std::string &item)
                -> int64_t { return self->__getitem__(c10::string_view{item}); })
-      .def("__len__", &Vocab::__len__)
+      .def("reassign_token", &Vocab::reassign_token)
       .def("insert_token", &Vocab::insert_token)
+      .def("__len__", &Vocab::__len__)
+      .def("set_default_index", &Vocab::set_default_index)
+      .def("get_default_index", &Vocab::get_default_index)
       .def("append_token", &Vocab::append_token)
       .def("lookup_token", &Vocab::lookup_token)
       .def("lookup_tokens", &Vocab::lookup_tokens)
diff --git a/torchtext/csrc/vocab.cpp b/torchtext/csrc/vocab.cpp
index 1831d46f39..e659fbdb70 100644
--- a/torchtext/csrc/vocab.cpp
+++ b/torchtext/csrc/vocab.cpp
@@ -7,27 +7,21 @@
 #include <vocab.h>       // @manual
 namespace torchtext {
 
-Vocab::Vocab(const StringList &tokens, const std::string &unk_token)
-    : stoi_(MAX_VOCAB_SIZE, -1), unk_token_(std::move(unk_token)) {
-  for (std::size_t i = 0; i < tokens.size(); i++) {
-    // tokens should not have any duplicates
-    auto token_position =
-        _find(c10::string_view{tokens[i].data(), tokens[i].size()});
-    if (stoi_[token_position] != -1) {
-#ifdef _MSC_VER
-      std::cerr << "[RuntimeError] Duplicate token found in tokens list: "
-                << tokens[i] << std::endl;
-#endif
-      throw std::runtime_error("Duplicate token found in tokens list: " +
-                               tokens[i]);
-    }
+Vocab::Vocab(const StringList &tokens,
+             const c10::optional<int64_t> &default_index)
+    : stoi_(MAX_VOCAB_SIZE, -1), default_index_{default_index} {
+  for (size_t i = 0; i < tokens.size(); i++) {
+    // throw error if duplicate token is found
+    auto id = _find(c10::string_view{tokens[i].data(), tokens[i].size()});
+    TORCH_CHECK(stoi_[id] == -1,
+                "Duplicate token found in tokens list: " + tokens[i]);
+
     _add(tokens[i]);
   }
-
-  unk_index_ =
-      stoi_[_find(c10::string_view{unk_token.data(), unk_token.size()})];
 }
 
+Vocab::Vocab(const StringList &tokens) : Vocab(tokens, {}) {}
+
 int64_t Vocab::__len__() const { return itos_.size(); }
 
 bool Vocab::__contains__(const c10::string_view &token) const {
@@ -38,77 +32,92 @@ bool Vocab::__contains__(const c10::string_view &token) const {
   return false;
 }
 
-
 int64_t Vocab::__getitem__(const c10::string_view &token) const {
   int64_t id = _find(token);
-  if (stoi_[id] != -1) {
+  if (stoi_[id] != -1)
     return stoi_[id];
-  }
-  return unk_index_;
+
+  // throw error if default_index_ is not set
+  TORCH_CHECK(default_index_.has_value(),
+              "Token " + std::string(token) +
+                  " not found and default index is not set");
+
+  // return default index if token is OOV
+  return default_index_.value();
 }
 
-void Vocab::append_token(const std::string &token) { _add(token); }
+void Vocab::set_default_index(int64_t index) { default_index_ = index; }
+
+c10::optional<int64_t> Vocab::get_default_index() const {
+  return default_index_;
+}
+
+void Vocab::append_token(const std::string &token) {
+  // throw error if token already exist in vocab
+  auto id = _find(c10::string_view{token.data(), token.size()});
+  TORCH_CHECK(stoi_[id] == -1, "Token " + token +
+                                   " already exists in the Vocab with index: " +
+                                   std::to_string(stoi_[id]));
+
+  _add(token);
+}
+
+void Vocab::reassign_token(const std::string &token, const int64_t &index) {
+  // throw error if index is not valid
+  TORCH_CHECK(index >= 0 && index < __len__(),
+              "Specified index " + std::to_string(index) +
+                  " is out of bounds for vocab of size " +
+                  std::to_string(__len__()));
+
+  // throw error if token not found
+  TORCH_CHECK(__contains__(token), "Token " + token + " not found in Vocab");
+
+  _remove(token);
+  insert_token(token, index);
+}
 
 void Vocab::insert_token(const std::string &token, const int64_t &index) {
-  if (index < 0 || index > itos_.size()) {
-#ifdef _MSC_VER
-    std::cerr << "[RuntimeError] Specified index " << index
-              << " is out of bounds of the size of stoi dictionary: "
-              << stoi_.size() << std::endl;
-#endif
-    throw std::runtime_error(
-        "Specified index " + std::to_string(index) +
-        " is out of bounds of the size of stoi dictionary: " +
-        std::to_string(stoi_.size()) + ".");
-  }
+  // throw error if index is not valid
+  TORCH_CHECK(index >= 0 && index <= __len__(),
+              "Specified index " + std::to_string(index) +
+                  " is out of bounds for vocab of size " +
+                  std::to_string(__len__()));
 
-  // if item already in stoi we throw an error
-  auto token_position = _find(c10::string_view{token.data(), token.size()});
-  if (stoi_[token_position] != -1) {
-#ifdef _MSC_VER
-    std::cerr << "[RuntimeError] Token " << token
-              << " already exists in the Vocab with index: "
-              << stoi_[token_position] << std::endl;
-#endif
-    throw std::runtime_error("Token " + token +
-                             " already exists in the Vocab with index: " +
-                             std::to_string(stoi_[token_position]) + ".");
-  }
+  // throw error if token already present
+  TORCH_CHECK(!__contains__(token), "Token " + token + " not found in Vocab");
 
   // need to offset all tokens greater than or equal index by 1
-  for (size_t i = index; i < itos_.size(); i++) {
+  for (size_t i = index; i < __len__(); i++) {
     stoi_[_find(c10::string_view{itos_[i].data(), itos_[i].size()})] = i + 1;
   }
 
   itos_.insert(itos_.begin() + index, token);
   stoi_[_find(c10::string_view{token.data(), token.size()})] = index;
-
-  // need to update unk_index in case token equals unk_token or token
-  // inserted before unk_token
-  unk_index_ =
-      stoi_[_find(c10::string_view{unk_token_.data(), unk_token_.size()})];
 }
 
 std::string Vocab::lookup_token(const int64_t &index) {
-  if (index < 0 || index > static_cast<int64_t>(itos_.size())) {
-#ifdef _MSC_VER
-    std::cerr << "[RuntimeError] Specified index " << index
-              << " is out of bounds of the size of itos dictionary: "
-              << itos_.size() << std::endl;
-#endif
-    throw std::runtime_error(
-        "Specified index " + std::to_string(index) +
-        " is out of bounds of the size of itos dictionary: " +
-        std::to_string(itos_.size()) + ".");
-  }
+  // throw error if index is not valid
+  TORCH_CHECK(index >= 0 && index < __len__(),
+              "Specified index " + std::to_string(index) +
+                  " is out of bounds for vocab of size " +
+                  std::to_string(__len__()));
 
   return itos_[index];
 }
 
 StringList Vocab::lookup_tokens(const std::vector<int64_t> &indices) {
+  // throw error if indices are not valid
+  for (size_t i = 0; i < indices.size(); i++) {
+    TORCH_CHECK(indices[i] >= 0 && indices[i] < __len__(),
+                "Specified index " + std::to_string(indices[i]) +
+                    " at position " + std::to_string(i) +
+                    " is out of bounds for vocab of size " +
+                    std::to_string(__len__()));
+  }
+
   std::vector<std::string> tokens(indices.size());
-  for (int64_t i = 0; i < static_cast<int64_t>(indices.size()); i++) {
-    tokens[i] = lookup_token(indices[i]);
+  for (size_t i = 0; i < indices.size(); i++) {
+    tokens[i] = itos_[indices[i]];
   }
   return tokens;
 }
@@ -116,7 +125,7 @@ StringList Vocab::lookup_tokens(const std::vector<int64_t> &indices) {
 std::vector<int64_t>
 Vocab::lookup_indices(const std::vector<c10::string_view> &tokens) {
   std::vector<int64_t> indices(tokens.size());
-  for (int64_t i = 0; i < static_cast<int64_t>(tokens.size()); i++) {
+  for (size_t i = 0; i < tokens.size(); i++) {
     indices[i] = __getitem__(tokens[i]);
   }
   return indices;
@@ -148,9 +157,7 @@ void parse_vocab_file_chunk(const std::string &file_path, size_t offset,
                             const int64_t start_line, const int64_t end_line,
                             std::shared_ptr<IndexDict> counter) {
   std::ifstream fin(file_path, std::ios::in);
-  if (!fin.is_open()) {
-    throw std::runtime_error("Cannot open input file " + file_path + "\n");
-  }
+  TORCH_CHECK(fin.is_open(), "Cannot open input file " + file_path);
 
   fin.seekg(offset);
 
@@ -172,9 +179,7 @@ void parse_raw_text_file_chunk(const std::string &file_path, size_t offset,
                                std::shared_ptr<IndexDict> counter,
                                torch::jit::script::Module &module) {
   std::ifstream fin(file_path, std::ios::in);
-  if (!fin.is_open()) {
-    throw std::runtime_error("Cannot open input file " + file_path + "\n");
-  }
+  TORCH_CHECK(fin.is_open(), "Cannot open input file " + file_path);
 
   fin.seekg(offset);
 
@@ -210,8 +215,9 @@ struct CompareTokens {
 
 StringList
 _concat_tokens(std::vector<std::shared_ptr<IndexDict>> chunk_counters,
-               const std::string &unk_token, const int64_t min_freq,
-               const int64_t num_lines, const bool sort_tokens) {
+               const int64_t min_freq, const int64_t num_lines,
+               const bool sort_tokens) {
+
   TORCH_CHECK(chunk_counters.size() > 0,
               "There must be at least 1 chunk to concatenate!");
 
@@ -257,24 +263,12 @@ _concat_tokens(std::vector<std::shared_ptr<IndexDict>> chunk_counters,
     unique_tokens.push_back(token_freq_pair.first);
   }
 
-  // insert unk_token if not present
-  if (tokens_freq.find(unk_token) == tokens_freq.end()) {
-    std::cerr << "The `unk_token` " << unk_token
-              << " wasn't found in the `ordered_dict`. Adding the `unk_token` "
-                 "to the beginning of the Vocab."
-              << std::endl;
-
-    unique_tokens.insert(unique_tokens.begin(), unk_token);
-  }
-
   return unique_tokens;
 }
 
 constexpr int64_t GRAIN_SIZE = 13107;
 Vocab _load_vocab_from_file(const std::string &file_path,
-                            const std::string &unk_token,
                             const int64_t min_freq, const int64_t num_cpus) {
-  std::cerr << "[INFO] Reading file " << file_path << std::endl;
 
   int64_t num_lines = _infer_lines(file_path);
   int64_t chunk_size = impl::divup(num_lines, num_cpus);
@@ -313,17 +307,15 @@ Vocab _load_vocab_from_file(const std::string &file_path,
   cv.wait(lock, [&thread_count] { return thread_count == 0; });
 
   StringList tokens =
-      _concat_tokens(chunk_counters, unk_token, min_freq, num_lines, false);
+      _concat_tokens(chunk_counters, min_freq, num_lines, false);
 
-  return Vocab(std::move(tokens), unk_token);
+  return Vocab(std::move(tokens));
 }
 
 Vocab _build_vocab_from_text_file(const std::string &file_path,
-                                  const std::string &unk_token,
                                   const int64_t min_freq,
                                   const int64_t num_cpus,
                                   torch::jit::script::Module tokenizer) {
-  std::cerr << "[INFO] Reading file " << file_path << std::endl;
   int64_t num_lines = _infer_lines(file_path);
   int64_t chunk_size = impl::divup(num_lines, num_cpus);
   // Launching a thread on less lines than this likely has too much overhead.
@@ -359,18 +351,20 @@ Vocab _build_vocab_from_text_file(const std::string &file_path,
   std::unique_lock<std::mutex> lock(m);
   cv.wait(lock, [&thread_count] { return thread_count == 0; });
 
-  StringList tokens =
-      _concat_tokens(chunk_counters, unk_token, min_freq, num_lines, true);
+  StringList tokens = _concat_tokens(chunk_counters, min_freq, num_lines, true);
 
-  return Vocab(std::move(tokens), unk_token);
+  return Vocab(std::move(tokens));
 }
 
 VocabStates _serialize_vocab(const c10::intrusive_ptr<Vocab> &self) {
   std::vector<int64_t> integers;
   StringList strings = self->itos_;
-  strings.push_back(self->unk_token_);
   std::vector<torch::Tensor> tensors;
 
+  if (self->default_index_.has_value()) {
+    integers.push_back(self->default_index_.value());
+  }
+
   VocabStates states = std::make_tuple(self->version_str_, std::move(integers),
                                        std::move(strings), std::move(tensors));
   return states;
@@ -378,45 +372,27 @@ VocabStates _serialize_vocab(const c10::intrusive_ptr<Vocab> &self) {
 
 c10::intrusive_ptr<Vocab> _deserialize_vocab(VocabStates states) {
   auto state_size = std::tuple_size<decltype(states)>::value;
-  if (state_size != 4) {
-#ifdef _MSC_VER
-    std::cerr << "[RuntimeError] Expected deserialized Vocab to have 4 states "
-                 "but found "
-              << state_size << " states." << std::endl;
-#endif
-    throw std::runtime_error(
-        "Expected deserialized Vocab to have 4 states but found " +
-        std::to_string(state_size) + " states.");
-  }
+  TORCH_CHECK(state_size == 4,
+              "Expected deserialized Vocab to have 4 states but found " +
+                  std::to_string(state_size) + " states");
 
   auto &version_str = std::get<0>(states);
   auto &integers = std::get<1>(states);
   auto &strings = std::get<2>(states);
   auto &tensors = std::get<3>(states);
 
-  // check integers and tensors are empty
-  if (integers.size() != 0 || tensors.size() != 0) {
-#ifdef _MSC_VER
-    std::cerr << "[RuntimeError] Expected `integers` and `tensors` states to "
-                 "be empty."
-              << std::endl;
-#endif
-    throw std::runtime_error(
-        "Expected `integers` and `tensors` states to be empty.");
-  }
+  // check tensors are empty
+  TORCH_CHECK(tensors.size() == 0, "Expected `tensors` states to be empty");
 
-  if (version_str.compare("0.0.1") >= 0) {
-    std::string unk_token = strings.back();
-    strings.pop_back(); // remove last element which is unk_token
+  // throw error if version is not compatible
+  TORCH_CHECK(version_str.compare("0.0.2") >= 0,
+              "Found unexpected version for serialized Vocab: " + version_str);
 
-    return c10::make_intrusive<Vocab>(std::move(strings), std::move(unk_token));
+  c10::optional<int64_t> default_index = {};
+  if (integers.size() > 0) {
+    default_index = integers[0];
   }
-#ifdef _MSC_VER
-  std::cerr << "[RuntimeError] Found unexpected version for serialized Vocab: "
-            << version_str << std::endl;
-#endif
-  throw std::runtime_error(
-      "Found unexpected version for serialized Vocab: " + version_str + ".");
+  return c10::make_intrusive<Vocab>(std::move(strings), default_index);
 }
 
 } // namespace torchtext
diff --git a/torchtext/csrc/vocab.h b/torchtext/csrc/vocab.h
index d915c7de27..06f98865d3 100644
--- a/torchtext/csrc/vocab.h
+++ b/torchtext/csrc/vocab.h
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <c10/util/string_view.h>
 #include <torch/script.h>
 namespace torchtext {
@@ -13,17 +14,24 @@ struct Vocab : torch::CustomClassHolder {
   static const int32_t MAX_VOCAB_SIZE = 30000000;
   int64_t unk_index_;
   std::vector<int32_t> stoi_;
-  const std::string version_str_ = "0.0.1";
+  const std::string version_str_ = "0.0.2";
   StringList itos_;
-  std::string unk_token_;
+  c10::optional<int64_t> default_index_ = {};
 
-  explicit Vocab(const std::vector<std::string> &tokens,
-                 const std::string &unk_token);
+  // TODO: [can we remove this?] we need to keep this constructor, otherwise
+  // torch binding gets compilation error: no matching constructor for
+  // initialization of 'torchtext::Vocab'
+  explicit Vocab(const StringList &tokens);
+  explicit Vocab(const StringList &tokens,
+                 const c10::optional<int64_t> &default_index);
   int64_t __len__() const;
   int64_t __getitem__(const c10::string_view &token) const;
   bool __contains__(const c10::string_view &token) const;
-  void append_token(const std::string &token);
+  void set_default_index(int64_t index);
+  c10::optional<int64_t> get_default_index() const;
+  void reassign_token(const std::string &token, const int64_t &index);
   void insert_token(const std::string &token, const int64_t &index);
+  void append_token(const std::string &token);
   std::string lookup_token(const int64_t &index);
   std::vector<std::string> lookup_tokens(const std::vector<int64_t> &indices);
   std::vector<int64_t>
@@ -44,7 +52,7 @@ struct Vocab : torch::CustomClassHolder {
   uint32_t _find(const c10::string_view &w) const {
     uint32_t stoi_size = stoi_.size();
     uint32_t id = _hash(w) % stoi_size;
-    while (stoi_[id] != -1 && itos_[stoi_[id]]!= w) {
+    while (stoi_[id] != -1 && itos_[stoi_[id]] != w) {
       id = (id + 1) % stoi_size;
     }
     return id;
@@ -57,16 +65,22 @@ struct Vocab : torch::CustomClassHolder {
       stoi_[h] = itos_.size() - 1;
     }
   }
+
+  void _remove(const std::string &w) {
+    uint32_t h = _find(c10::string_view{w.data(), w.size()});
+    if (stoi_[h] != -1) {
+      stoi_[h] = -1;
+      itos_.erase(std::find(itos_.begin(), itos_.end(), w));
+    }
+  }
 };
 
 VocabStates _serialize_vocab(const c10::intrusive_ptr<Vocab> &self);
 c10::intrusive_ptr<Vocab> _deserialize_vocab(VocabStates states);
 
 Vocab _load_vocab_from_file(const std::string &file_path,
-                            const std::string &unk_token,
                             const int64_t min_freq, const int64_t num_cpus);
 Vocab _build_vocab_from_text_file(const std::string &file_path,
-                                  const std::string &unk_token,
                                   const int64_t min_freq,
                                   const int64_t num_cpus,
                                   torch::jit::script::Module tokenizer);
diff --git a/torchtext/experimental/vocab.py b/torchtext/experimental/vocab.py
index 26f393ce36..a1aa2290d9 100644
--- a/torchtext/experimental/vocab.py
+++ b/torchtext/experimental/vocab.py
@@ -1,6 +1,5 @@
 import logging
-from typing import Dict, List
-import warnings
+from typing import Dict, List, Optional
 from collections import Counter, OrderedDict
 import torch
 import torch.nn as nn
@@ -30,7 +29,7 @@ def build_vocab_from_text_file(file_path, jited_tokenizer, min_freq=1, unk_token
         jited_tokenizer (ScriptModule): a tokenizer that has been JITed using `torch.jit.script`
         min_freq: The minimum frequency needed to include a token in the vocabulary.
             Values less than 1 will be set to 1. Default: 1.
-        unk_token: The default unknown token to use. Default: '<unk>'.
+        unk_token: The default unknown token to use. Default: '<unk>'. If not found in text file, it will be inserted to index 0.
         num_cpus (int): the number of cpus to use when loading the vectors from file. Default: 4.
 
     Returns:
@@ -44,7 +43,9 @@ def build_vocab_from_text_file(file_path, jited_tokenizer, min_freq=1, unk_token
         >>> jit_tokenizer = torch.jit.script(tokenizer)
         >>> v = build_vocab_from_text_file('vocab.txt', jit_tokenizer)
     """
-    vocab_obj = _build_vocab_from_text_file(file_path, unk_token, min_freq, num_cpus, jited_tokenizer)
+    vocab_obj = _build_vocab_from_text_file(file_path, min_freq, num_cpus, jited_tokenizer)
+    if unk_token not in vocab_obj:
+        vocab_obj.insert_token(unk_token, 0)
     return Vocab(vocab_obj)
 
 
@@ -62,7 +63,7 @@ def load_vocab_from_file(file_path, min_freq=1, unk_token='<unk>', num_cpus=4):
         file_object (FileObject): a file like object to read data from.
         min_freq: The minimum frequency needed to include a token in the vocabulary.
             Values less than 1 will be set to 1. Default: 1.
-        unk_token: The default unknown token to use. Default: '<unk>'.
+        unk_token: The default unknown token to use. Default: '<unk>'. If not found in vocab file, it will be inserted to index 0.
         num_cpus (int): the number of cpus to use when loading the vectors from file. Default: 4.
 
     Returns:
@@ -73,7 +74,9 @@ def load_vocab_from_file(file_path, min_freq=1, unk_token='<unk>', num_cpus=4):
         >>> v = load_vocab_from_file('vocab.txt')
     """
 
-    vocab_obj = _load_vocab_from_file(file_path, unk_token, min_freq, num_cpus)
+    vocab_obj = _load_vocab_from_file(file_path, min_freq, num_cpus)
+    if unk_token not in vocab_obj:
+        vocab_obj.insert_token(unk_token, 0)
     return Vocab(vocab_obj)
 
 
@@ -108,7 +111,7 @@ def vocab(ordered_dict, min_freq=1, unk_token='<unk>'):
         ordered_dict (collections.OrderedDict): object holding the frequencies of each token found in the data.
         min_freq: The minimum frequency needed to include a token in the vocabulary.
             Values less than 1 will be set to 1. Default: 1.
-        unk_token: The default unknown token to use. Default: '<unk>'.
+        unk_token: The default unknown token to use. Default: '<unk>'. If not found in ordered_dict, it will be inserted at index 0.
 
     Raises:
         ValueError: if a default `unk_token` isn't provided.
@@ -134,9 +137,7 @@ def vocab(ordered_dict, min_freq=1, unk_token='<unk>'):
 
     if unk_token not in tokens:
         tokens.insert(0, unk_token)
-        warnings.warn("The `unk_token` '{}' wasn't found in the `ordered_dict`. Adding the `unk_token` "
-                      "to the beginning of the Vocab.".format(unk_token), RuntimeWarning)
-    return Vocab(VocabPybind(tokens, unk_token))
+    return Vocab(VocabPybind(tokens, None))
 
 
 class Vocab(nn.Module):
@@ -198,12 +199,38 @@ def __getitem__(self, token: str) -> int:
         return self.vocab[token]
 
     @torch.jit.export
-    def insert_token(self, token: str, index: int) -> None:
+    def set_default_index(self, index: int) -> None:
+        r"""
+        Args:
+            index: Value of default index. This index will be returned when OOV token is queried
+        """
+        self.vocab.set_default_index(index)
+
+    @torch.jit.export
+    def get_default_index(self) -> Optional[int]:
+        r"""
+        Returns:
+            index (optional[int]): Value of default index if it is set.
+        """
+        return self.vocab.get_default_index()
+
+    @torch.jit.export
+    def reassign_token(self, token: str, index: int) -> None:
         r"""
         Args:
             token (str): the token used to lookup the corresponding index.
             index (int): the index corresponding to the associated token.
+        Raises:
+            RuntimeError: If `index` is not range [0,Vocab.size()) or if token is not present in Vocab
+        """
+        self.vocab.reassign_token(token, index)
 
+    @torch.jit.export
+    def insert_token(self, token: str, index: int) -> None:
+        r"""
+        Args:
+            token (str): the token used to lookup the corresponding index.
+            index (int): the index corresponding to the associated token.
         Raises:
             RuntimeError: if `index` not between [0, Vocab.size()] or if token already exists in the vocab.
         """
@@ -214,6 +241,9 @@ def append_token(self, token: str) -> None:
         r"""
         Args:
             token (str): the token used to lookup the corresponding index.
+
+        Raises:
+            RuntimeError: if token already exists in the vocab
         """
         self.vocab.append_token(token)
 
@@ -275,5 +305,7 @@ def get_itos(self) -> List[str]:
     def __prepare_scriptable__(self):
         r"""Return a JITable Vocab.
         """
-        cpp_vocab = torch.classes.torchtext.Vocab(self.vocab.itos_, self.vocab.unk_token_)
-        return Vocab(cpp_vocab)
+        if not self.is_jitable:
+            cpp_vocab = torch.classes.torchtext.Vocab(self.vocab.itos_, self.vocab.default_index_)
+            return Vocab(cpp_vocab)
+        return self