diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp
new file mode 100644
index 00000000000000..0616cc5403204a
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp
@@ -0,0 +1,74 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op/com.microsoft/embed_layer_normalization.hpp"
+
+#include "default_opset.hpp"
+#include "onnx_import/core/null_node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+OutputVector embed_layer_normalization(const Node& node) {
+    auto nodes = node.get_ng_inputs();
+    auto num_nodes = nodes.size();
+
+    NGRAPH_CHECK(num_nodes >= 7 && num_nodes <= 8,
+                 "EmbedLayerNormalization takes 7 or 8 inputs. Provided " + std::to_string(num_nodes));
+    NGRAPH_CHECK(nodes[0].get_element_type() == element::i32, "input_ids must have int32 type");
+
+    const auto& input_ids = nodes[0];
+    const auto& segment_ids = nodes[1];
+    const auto& word_embeddings = nodes[2];
+    const auto& position_embeddings = nodes[3];
+    const auto& segment_embeddings = nodes[4];
+    const auto& gamma = nodes[5];
+    const auto& beta = nodes[6];
+
+    auto zero = default_opset::Constant::create(element::i32, Shape{1}, {0});
+    std::shared_ptr<ngraph::Node> input = std::make_shared<default_opset::Gather>(word_embeddings, input_ids, zero, 0);
+    input = std::make_shared<default_opset::Add>(input, position_embeddings);
+
+    // add segment embeddings if available
+    if (!ngraph::op::is_null(segment_ids)) {
+        NGRAPH_CHECK(!ngraph::op::is_null(segment_embeddings),
+                     "segment_ids provided, but segment_embedding input is missing");
+        NGRAPH_CHECK(nodes[1].get_element_type() == element::i32, "segment_ids must have int32 type");
+        auto gathered_segment_embeddings =
+            std::make_shared<default_opset::Gather>(segment_embeddings, segment_ids, zero, 0);
+        input = std::make_shared<default_opset::Add>(input, gathered_segment_embeddings);
+    }
+
+    float eps = node.get_attribute_value<float>("epsilon");
+    // reduce over hidden_size
+    // hidden_size dimension is 2 here, because the shape after Gather(word_embedding, input_ids)
+    // is (batch_size, seq_len, hidden_size)
+    int hidden_size_dim = 2;
+    const auto reduction_axes = default_opset::Constant::create(element::i32, Shape{1}, {hidden_size_dim});
+    std::shared_ptr<ngraph::Node> result =
+        std::make_shared<default_opset::MVN>(input, reduction_axes, true, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT);
+
+    // result = gamma * result + beta
+    result = std::make_shared<default_opset::Multiply>(result, gamma);
+    result = std::make_shared<default_opset::Add>(result, beta);
+
+    // compute mask_index output
+    std::shared_ptr<ngraph::Node> mask_index;
+    if (num_nodes > 7 && !ngraph::op::is_null(nodes[7])) {
+        NGRAPH_CHECK(nodes[7].get_element_type() == element::i32, "mask must have int32 type");
+        auto axis = default_opset::Constant::create(element::i32, Shape{}, {1});
+        mask_index = std::make_shared<default_opset::ReduceSum>(nodes[7], axis, false);
+    } else {
+        auto batch_size = std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(nodes[0]),
+                                                                  zero,   // indices
+                                                                  zero);  // axis
+        mask_index = std::make_shared<default_opset::Broadcast>(zero, batch_size);
+    }
+    return {result, mask_index};
+}
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp
new file mode 100644
index 00000000000000..2d9fcecdcf7932
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp
@@ -0,0 +1,17 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+OutputVector embed_layer_normalization(const Node& node);
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
index b1e3734a2f35f9..539cc7fc0d7ef2 100644
--- a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
+++ b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
@@ -30,6 +30,7 @@
 #include "op/ceil.hpp"
 #include "op/clip.hpp"
 #include "op/com.microsoft/bias_gelu.hpp"
+#include "op/com.microsoft/embed_layer_normalization.hpp"
 #include "op/com.microsoft/skip_layer_normalization.hpp"
 #include "op/compress.hpp"
 #include "op/concat.hpp"
@@ -482,6 +483,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR_WITH_DOMAIN(OPENVINO_ONNX_DOMAIN, "Swish", 1, swish);
 
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "BiasGelu", 1, bias_gelu);
+    REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "EmbedLayerNormalization", 1, embed_layer_normalization);
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "SkipLayerNormalization", 1, skip_layer_normalization);
 }
 
diff --git a/ngraph/test/engines_util/ie_engines.cpp b/ngraph/test/engines_util/ie_engines.cpp
index 3238b32c18ca14..e9b918c2e0ceb4 100644
--- a/ngraph/test/engines_util/ie_engines.cpp
+++ b/ngraph/test/engines_util/ie_engines.cpp
@@ -337,6 +337,9 @@ testing::AssertionResult test::IE_Engine::compare_results_with_tolerance_as_fp(c
             comparison_result = test::compare_with_tolerance(test_results.first, test_results.second, tolerance);
             break;
         }
+        case InferenceEngine::Precision::I32:
+            comparison_result = compare_blobs<int32_t>(computed_output_blob, expected_output_blob, 0);
+            break;
         default:
             comparison_result = testing::AssertionFailure() << "Unsupported data type encountered in "
                                                                "'compare_results_with_tolerance_as_fp' method";
diff --git a/ngraph/test/engines_util/interpreter_engine.cpp b/ngraph/test/engines_util/interpreter_engine.cpp
index 65f614d4c73fa8..6648f7f8fca6aa 100644
--- a/ngraph/test/engines_util/interpreter_engine.cpp
+++ b/ngraph/test/engines_util/interpreter_engine.cpp
@@ -124,6 +124,9 @@ testing::AssertionResult test::INTERPRETER_Engine::compare_results_with_toleranc
         case element::Type_t::f32:
             comparison_result = compare_with_fp_tolerance(expected_result_constant, result_tensor, tolerance);
             break;
+        case element::Type_t::i32:
+            comparison_result = compare_values<int32_t>(expected_result_constant, result_tensor, 0);
+            break;
         default:
             comparison_result = testing::AssertionFailure() << "Unsupported data type encountered in "
                                                                "'compare_results_with_tolerance_as_fp' method";
diff --git a/ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt b/ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt
new file mode 100644
index 00000000000000..577926c6d3114f
--- /dev/null
+++ b/ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt
@@ -0,0 +1,186 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input_ids"
+    input: "segment_ids"
+    input: "word_embeddings"
+    input: "position_embeddings"
+    input: "segment_embeddings"
+    input: "gamma"
+    input: "beta"
+    input: "mask"
+    output: "output"
+    output: "mask_index"
+    name: "EmbedLayerNormalization_1"
+    op_type: "EmbedLayerNormalization"
+    attribute {
+      name: "epsilon"
+      f: 9.999999960041972e-13
+      type: FLOAT
+    }
+    domain: "com.microsoft"
+  }
+  name: "graph"
+  input {
+    name: "input_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "word_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "word_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "position_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "pos_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "segment_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "gamma"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "beta"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "mask"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mask_index"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/embed_layer_normalization.prototxt b/ngraph/test/models/onnx/embed_layer_normalization.prototxt
new file mode 100644
index 00000000000000..1cd1bfcc1b3d70
--- /dev/null
+++ b/ngraph/test/models/onnx/embed_layer_normalization.prototxt
@@ -0,0 +1,187 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input_ids"
+    input: ""
+    input: "word_embeddings"
+    input: "position_embeddings"
+    input: ""
+    input: "gamma"
+    input: "beta"
+    output: "output"
+    name: "EmbedLayerNormalization_1"
+    op_type: "EmbedLayerNormalization"
+    attribute {
+      name: "epsilon"
+      f: 9.999999960041972e-13
+      type: FLOAT
+    }
+    domain: "com.microsoft"
+  }
+  name: "graph"
+  initializer {
+    dims: 10
+    dims: 5
+    data_type: 1
+    name: "word_embeddings"
+    float_data: 0.01326417364180088
+    float_data: -0.017005326226353645
+    float_data: 0.021556973457336426
+    float_data: -0.079218357801437378
+    float_data: -0.019958715885877609
+    float_data: 0.066062852740287781
+    float_data: -0.063465960323810577
+    float_data: -0.036202378571033478
+    float_data: -0.038673330098390579
+    float_data: -0.050637193024158478
+    float_data: 0.0024814880453050137
+    float_data: -0.017267324030399323
+    float_data: -0.0047671985812485218
+    float_data: -0.014202062971889973
+    float_data: 0.10090816766023636
+    float_data: 0.044896259903907776
+    float_data: 0.015443948097527027
+    float_data: -0.0010053194127976894
+    float_data: 0.071923978626728058
+    float_data: 0.01173736434429884
+    float_data: 0.034053854644298553
+    float_data: -0.037060577422380447
+    float_data: 0.01355923805385828
+    float_data: 0.054467327892780304
+    float_data: 0.088897556066513062
+    float_data: 0.019563071429729462
+    float_data: 0.025579970329999924
+    float_data: -0.032200627028942108
+    float_data: -0.0083356937393546104
+    float_data: -0.10528338700532913
+    float_data: 0.04967513307929039
+    float_data: -0.093638911843299866
+    float_data: 0.0018587876111268997
+    float_data: 0.01037109550088644
+    float_data: -0.011854520998895168
+    float_data: 0.035907052457332611
+    float_data: -0.061639595776796341
+    float_data: -0.070428818464279175
+    float_data: 0.080737568438053131
+    float_data: -0.014098187908530235
+    float_data: -0.066207133233547211
+    float_data: 0.078362509608268738
+    float_data: -0.021088391542434692
+    float_data: -0.022340660914778709
+    float_data: -0.065533898770809174
+    float_data: -0.022695079445838928
+    float_data: 0.01550679374486208
+    float_data: -0.022843297570943832
+    float_data: 0.044251278042793274
+    float_data: -0.0071350894868373871
+  }
+  initializer {
+    dims: 8
+    dims: 5
+    data_type: 1
+    name: "position_embeddings"
+    float_data: 0.11355137079954147
+    float_data: 0.048468157649040222
+    float_data: 0.053486518561840057
+    float_data: 0.01513370219618082
+    float_data: 0.14626613259315491
+    float_data: -0.18863441050052643
+    float_data: 0.10133393853902817
+    float_data: 0.098319537937641144
+    float_data: 0.070722959935665131
+    float_data: -0.018062451854348183
+    float_data: -0.018210677430033684
+    float_data: 0.018454158678650856
+    float_data: 0.025413623079657555
+    float_data: -0.017915787175297737
+    float_data: 0.088725067675113678
+    float_data: -0.10261145234107971
+    float_data: -0.16650274395942688
+    float_data: 0.087947741150856018
+    float_data: -0.072966478765010834
+    float_data: -0.072863951325416565
+    float_data: -0.057195741683244705
+    float_data: 0.052380021661520004
+    float_data: 0.150204136967659
+    float_data: 0.036691628396511078
+    float_data: -0.055858571082353592
+    float_data: 0.013746094889938831
+    float_data: -0.041797593235969543
+    float_data: 0.036348219960927963
+    float_data: 0.032991457730531693
+    float_data: -0.031414791941642761
+    float_data: -0.026756083592772484
+    float_data: -0.077081479132175446
+    float_data: 0.039385091513395309
+    float_data: -0.028280897065997124
+    float_data: -0.039638441056013107
+    float_data: 0.1023884043097496
+    float_data: -0.038734495639801025
+    float_data: 0.034112773835659027
+    float_data: -0.024975193664431572
+    float_data: -0.061074573546648026
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "gamma"
+    float_data: 0.037749473005533218
+    float_data: -0.10285304486751556
+    float_data: -0.030169183388352394
+    float_data: -0.02105225995182991
+    float_data: 0.11735564470291138
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "beta"
+    float_data: -0.058927357196807861
+    float_data: -0.019592402502894402
+    float_data: 0.0062640579417347908
+    float_data: -0.010709371417760849
+    float_data: -0.010058049112558365
+  }
+  input {
+    name: "input_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding.prototxt b/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding.prototxt
new file mode 100644
index 00000000000000..36b7a1deaaa870
--- /dev/null
+++ b/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding.prototxt
@@ -0,0 +1,239 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input_ids"
+    input: "segment_ids"
+    input: "word_embeddings"
+    input: "position_embeddings"
+    input: "segment_embeddings"
+    input: "gamma"
+    input: "beta"
+    output: "output"
+    output: "mask_index"
+    name: "EmbedLayerNormalization_1"
+    op_type: "EmbedLayerNormalization"
+    attribute {
+      name: "epsilon"
+      f: 9.999999960041972e-13
+      type: FLOAT
+    }
+    domain: "com.microsoft"
+  }
+  name: "graph"
+  initializer {
+    dims: 10
+    dims: 5
+    data_type: 1
+    name: "word_embeddings"
+    float_data: 0.01326417364180088
+    float_data: -0.017005326226353645
+    float_data: 0.021556973457336426
+    float_data: -0.079218357801437378
+    float_data: -0.019958715885877609
+    float_data: 0.066062852740287781
+    float_data: -0.063465960323810577
+    float_data: -0.036202378571033478
+    float_data: -0.038673330098390579
+    float_data: -0.050637193024158478
+    float_data: 0.0024814880453050137
+    float_data: -0.017267324030399323
+    float_data: -0.0047671985812485218
+    float_data: -0.014202062971889973
+    float_data: 0.10090816766023636
+    float_data: 0.044896259903907776
+    float_data: 0.015443948097527027
+    float_data: -0.0010053194127976894
+    float_data: 0.071923978626728058
+    float_data: 0.01173736434429884
+    float_data: 0.034053854644298553
+    float_data: -0.037060577422380447
+    float_data: 0.01355923805385828
+    float_data: 0.054467327892780304
+    float_data: 0.088897556066513062
+    float_data: 0.019563071429729462
+    float_data: 0.025579970329999924
+    float_data: -0.032200627028942108
+    float_data: -0.0083356937393546104
+    float_data: -0.10528338700532913
+    float_data: 0.04967513307929039
+    float_data: -0.093638911843299866
+    float_data: 0.0018587876111268997
+    float_data: 0.01037109550088644
+    float_data: -0.011854520998895168
+    float_data: 0.035907052457332611
+    float_data: -0.061639595776796341
+    float_data: -0.070428818464279175
+    float_data: 0.080737568438053131
+    float_data: -0.014098187908530235
+    float_data: -0.066207133233547211
+    float_data: 0.078362509608268738
+    float_data: -0.021088391542434692
+    float_data: -0.022340660914778709
+    float_data: -0.065533898770809174
+    float_data: -0.022695079445838928
+    float_data: 0.01550679374486208
+    float_data: -0.022843297570943832
+    float_data: 0.044251278042793274
+    float_data: -0.0071350894868373871
+  }
+  initializer {
+    dims: 8
+    dims: 5
+    data_type: 1
+    name: "position_embeddings"
+    float_data: 0.11355137079954147
+    float_data: 0.048468157649040222
+    float_data: 0.053486518561840057
+    float_data: 0.01513370219618082
+    float_data: 0.14626613259315491
+    float_data: -0.18863441050052643
+    float_data: 0.10133393853902817
+    float_data: 0.098319537937641144
+    float_data: 0.070722959935665131
+    float_data: -0.018062451854348183
+    float_data: -0.018210677430033684
+    float_data: 0.018454158678650856
+    float_data: 0.025413623079657555
+    float_data: -0.017915787175297737
+    float_data: 0.088725067675113678
+    float_data: -0.10261145234107971
+    float_data: -0.16650274395942688
+    float_data: 0.087947741150856018
+    float_data: -0.072966478765010834
+    float_data: -0.072863951325416565
+    float_data: -0.057195741683244705
+    float_data: 0.052380021661520004
+    float_data: 0.150204136967659
+    float_data: 0.036691628396511078
+    float_data: -0.055858571082353592
+    float_data: 0.013746094889938831
+    float_data: -0.041797593235969543
+    float_data: 0.036348219960927963
+    float_data: 0.032991457730531693
+    float_data: -0.031414791941642761
+    float_data: -0.026756083592772484
+    float_data: -0.077081479132175446
+    float_data: 0.039385091513395309
+    float_data: -0.028280897065997124
+    float_data: -0.039638441056013107
+    float_data: 0.1023884043097496
+    float_data: -0.038734495639801025
+    float_data: 0.034112773835659027
+    float_data: -0.024975193664431572
+    float_data: -0.061074573546648026
+  }
+  initializer {
+    dims: 3
+    dims: 5
+    data_type: 1
+    name: "segment_embeddings"
+    float_data: -0.027431340888142586
+    float_data: -0.01666862890124321
+    float_data: -0.052050836384296417
+    float_data: -0.074926018714904785
+    float_data: 0.0045464779250323772
+    float_data: 0.054949179291725159
+    float_data: 0.046781986951828003
+    float_data: 0.065758734941482544
+    float_data: -0.036851223558187485
+    float_data: -0.041801471263170242
+    float_data: 0.025191636756062508
+    float_data: -0.046526473015546799
+    float_data: 0.027152393013238907
+    float_data: 0.026372035965323448
+    float_data: -0.020972840487957001
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "gamma"
+    float_data: 0.037749473005533218
+    float_data: -0.10285304486751556
+    float_data: -0.030169183388352394
+    float_data: -0.02105225995182991
+    float_data: 0.11735564470291138
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "beta"
+    float_data: -0.058927357196807861
+    float_data: -0.019592402502894402
+    float_data: 0.0062640579417347908
+    float_data: -0.010709371417760849
+    float_data: -0.010058049112558365
+  }
+  input {
+    name: "input_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mask_index"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding_and_mask.prototxt b/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding_and_mask.prototxt
new file mode 100644
index 00000000000000..1181538c82ccaa
--- /dev/null
+++ b/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding_and_mask.prototxt
@@ -0,0 +1,256 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input_ids"
+    input: "segment_ids"
+    input: "word_embeddings"
+    input: "position_embeddings"
+    input: "segment_embeddings"
+    input: "gamma"
+    input: "beta"
+    input: "mask"
+    output: "output"
+    output: "mask_index"
+    name: "EmbedLayerNormalization_1"
+    op_type: "EmbedLayerNormalization"
+    attribute {
+      name: "epsilon"
+      f: 9.999999960041972e-13
+      type: FLOAT
+    }
+    domain: "com.microsoft"
+  }
+  name: "graph"
+  initializer {
+    dims: 10
+    dims: 5
+    data_type: 1
+    name: "word_embeddings"
+    float_data: 0.01326417364180088
+    float_data: -0.017005326226353645
+    float_data: 0.021556973457336426
+    float_data: -0.079218357801437378
+    float_data: -0.019958715885877609
+    float_data: 0.066062852740287781
+    float_data: -0.063465960323810577
+    float_data: -0.036202378571033478
+    float_data: -0.038673330098390579
+    float_data: -0.050637193024158478
+    float_data: 0.0024814880453050137
+    float_data: -0.017267324030399323
+    float_data: -0.0047671985812485218
+    float_data: -0.014202062971889973
+    float_data: 0.10090816766023636
+    float_data: 0.044896259903907776
+    float_data: 0.015443948097527027
+    float_data: -0.0010053194127976894
+    float_data: 0.071923978626728058
+    float_data: 0.01173736434429884
+    float_data: 0.034053854644298553
+    float_data: -0.037060577422380447
+    float_data: 0.01355923805385828
+    float_data: 0.054467327892780304
+    float_data: 0.088897556066513062
+    float_data: 0.019563071429729462
+    float_data: 0.025579970329999924
+    float_data: -0.032200627028942108
+    float_data: -0.0083356937393546104
+    float_data: -0.10528338700532913
+    float_data: 0.04967513307929039
+    float_data: -0.093638911843299866
+    float_data: 0.0018587876111268997
+    float_data: 0.01037109550088644
+    float_data: -0.011854520998895168
+    float_data: 0.035907052457332611
+    float_data: -0.061639595776796341
+    float_data: -0.070428818464279175
+    float_data: 0.080737568438053131
+    float_data: -0.014098187908530235
+    float_data: -0.066207133233547211
+    float_data: 0.078362509608268738
+    float_data: -0.021088391542434692
+    float_data: -0.022340660914778709
+    float_data: -0.065533898770809174
+    float_data: -0.022695079445838928
+    float_data: 0.01550679374486208
+    float_data: -0.022843297570943832
+    float_data: 0.044251278042793274
+    float_data: -0.0071350894868373871
+  }
+  initializer {
+    dims: 8
+    dims: 5
+    data_type: 1
+    name: "position_embeddings"
+    float_data: 0.11355137079954147
+    float_data: 0.048468157649040222
+    float_data: 0.053486518561840057
+    float_data: 0.01513370219618082
+    float_data: 0.14626613259315491
+    float_data: -0.18863441050052643
+    float_data: 0.10133393853902817
+    float_data: 0.098319537937641144
+    float_data: 0.070722959935665131
+    float_data: -0.018062451854348183
+    float_data: -0.018210677430033684
+    float_data: 0.018454158678650856
+    float_data: 0.025413623079657555
+    float_data: -0.017915787175297737
+    float_data: 0.088725067675113678
+    float_data: -0.10261145234107971
+    float_data: -0.16650274395942688
+    float_data: 0.087947741150856018
+    float_data: -0.072966478765010834
+    float_data: -0.072863951325416565
+    float_data: -0.057195741683244705
+    float_data: 0.052380021661520004
+    float_data: 0.150204136967659
+    float_data: 0.036691628396511078
+    float_data: -0.055858571082353592
+    float_data: 0.013746094889938831
+    float_data: -0.041797593235969543
+    float_data: 0.036348219960927963
+    float_data: 0.032991457730531693
+    float_data: -0.031414791941642761
+    float_data: -0.026756083592772484
+    float_data: -0.077081479132175446
+    float_data: 0.039385091513395309
+    float_data: -0.028280897065997124
+    float_data: -0.039638441056013107
+    float_data: 0.1023884043097496
+    float_data: -0.038734495639801025
+    float_data: 0.034112773835659027
+    float_data: -0.024975193664431572
+    float_data: -0.061074573546648026
+  }
+  initializer {
+    dims: 3
+    dims: 5
+    data_type: 1
+    name: "segment_embeddings"
+    float_data: -0.027431340888142586
+    float_data: -0.01666862890124321
+    float_data: -0.052050836384296417
+    float_data: -0.074926018714904785
+    float_data: 0.0045464779250323772
+    float_data: 0.054949179291725159
+    float_data: 0.046781986951828003
+    float_data: 0.065758734941482544
+    float_data: -0.036851223558187485
+    float_data: -0.041801471263170242
+    float_data: 0.025191636756062508
+    float_data: -0.046526473015546799
+    float_data: 0.027152393013238907
+    float_data: 0.026372035965323448
+    float_data: -0.020972840487957001
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "gamma"
+    float_data: 0.037749473005533218
+    float_data: -0.10285304486751556
+    float_data: -0.030169183388352394
+    float_data: -0.02105225995182991
+    float_data: 0.11735564470291138
+  }
+  initializer {
+    dims: 5
+    data_type: 1
+    name: "beta"
+    float_data: -0.058927357196807861
+    float_data: -0.019592402502894402
+    float_data: 0.0062640579417347908
+    float_data: -0.010709371417760849
+    float_data: -0.010058049112558365
+  }
+  input {
+    name: "input_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "mask"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mask_index"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp b/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
index 43f74e64b06703..63611843a27dfd 100644
--- a/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
+++ b/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
@@ -73,7 +73,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta
     test_case.add_input<float>(input);
     test_case.add_input<float>(skip);
     test_case.add_expected_output<float>(expected);
-    test_case.run(5);
+    test_case.run_with_tolerance_as_fp();
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta) {
@@ -99,7 +99,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta
     test_case.add_input<float>(input);
     test_case.add_input<float>(skip);
     test_case.add_expected_output<float>(expected);
-    test_case.run(7);
+    test_case.run_with_tolerance_as_fp();
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma) {
@@ -125,7 +125,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma) {
     test_case.add_input<float>(input);
     test_case.add_input<float>(skip);
     test_case.add_expected_output<float>(expected);
-    test_case.run(6);
+    test_case.run_with_tolerance_as_fp();
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_dynamic_shapes) {
@@ -173,5 +173,219 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_dynamic_shapes)
     test_case.add_input<float>(Shape{4}, beta);
     test_case.add_input<float>(Shape{4}, bias);
     test_case.add_expected_output<float>(Shape{3, 2, 4}, expected);
-    test_case.run(7);
+    test_case.run_with_tolerance_as_fp();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization.onnx"));
+
+    std::vector<int> input_ids = {
+        8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
+    };
+    std::vector<float> expected_output = {
+        -0.06615843, -0.18040463, 0.02199928,  0.01868065,  0.05397778,  -0.11761580, -0.09138932, -0.02506775,
+        -0.02368510, -0.10373901, -0.05551499, -0.20972314, 0.01365213,  0.01132561,  -0.08603337, -0.08906764,
+        0.09692993,  -0.04444099, -0.02037602, -0.03453060, -0.10214549, -0.13331436, -0.02665862, -0.01228805,
+        -0.14232540, -0.07032782, 0.05511986,  -0.00120272, -0.04875736, -0.13051267, -0.05709254, 0.17854357,
+        -0.01759873, -0.01819968, 0.07573269,  0.00557164,  0.06232717,  0.00530490,  -0.01565807, -0.14841977,
+        -0.02299280, 0.02038561,  -0.00049481, 0.02575402,  0.10081697,  -0.12517214, -0.09316762, -0.00974943,
+        -0.03093284, -0.06309240, -0.05551499, -0.20972314, 0.01365213,  0.01132561,  -0.08603337, -0.06176658,
+        0.08304203,  -0.05025182, 0.00383657,  -0.02288112, -0.11407227, -0.01386134, -0.04411830, -0.00537948,
+        0.00164397,  -0.03739140, 0.09941526,  0.00333974,  -0.04251949, -0.12992151, -0.09509478, -0.11811313,
+        -0.03307065, -0.00866115, -0.15162414, 0.01106802,  0.06037656,  0.00035292,  -0.00223284, -0.11215645,
+        -0.01390734, 0.07064321,  0.04028325,  -0.00290875, 0.12875907,  -0.12517214, -0.09316762, -0.00974943,
+        -0.03093284, -0.06309240, -0.08723789, 0.03130914,  0.03131931,  -0.01526242, 0.20811458,  -0.05696163,
+        0.16304255,  -0.02407495, -0.02955675, -0.03086288, -0.08130091, -0.05001551, -0.04875683, 0.00143666,
+        -0.12153473, -0.00018507, 0.10957482,  -0.00416618, -0.01612359, -0.11605026, -0.08593204, 0.09055272,
+        -0.03054028, -0.03603891, -0.08479506, -0.00034568, 0.03713699,  0.00163411,  -0.01738501, -0.18267182,
+    };
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int>(input_ids);
+    test_case.add_expected_output<float>(expected_output);
+    test_case.run_with_tolerance_as_fp(1e-7f);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_embedding) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization_with_segment_embedding.onnx"));
+
+    std::vector<int> input_ids = {
+        8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
+    };
+    std::vector<int> segment_ids = {
+        0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 1,
+    };
+    std::vector<float> expected_output = {
+        -0.06044213, -0.14845914, 0.02457689,  0.02091519,  0.09514004,  -0.10280035, -0.02087995, -0.03323204,
+        -0.02967127, -0.13447416, -0.05191760, -0.16518904, 0.02340531,  0.02176395,  0.04972410,  -0.07360736,
+        0.12192874,  -0.04081530, -0.02338044, -0.05671440, -0.09475864, -0.08944942, -0.03362993, -0.01683486,
+        -0.16770349, -0.07382569, 0.06230322,  0.02215859,  -0.05212611, -0.03934773, -0.04748865, 0.18134241,
+        -0.01965741, -0.02202452, 0.01973994,  0.01575558,  0.04300199,  0.01436110,  -0.00198062, -0.09065692,
+        -0.02923042, -0.00748686, 0.00717049,  0.02638642,  0.12174864,  -0.12973398, -0.11872391, -0.00549398,
+        -0.02386289, -0.02210563, -0.03590920, -0.13728066, -0.01337939, 0.01538021,  -0.14687485, -0.05033565,
+        0.03818212,  -0.04939338, 0.00961064,  -0.07407621, -0.09624685, 0.05594898,  -0.04948713, -0.01305631,
+        -0.03779668, -0.01469170, 0.12346989,  0.02082030,  -0.03449103, -0.06029151, -0.09300473, -0.16308543,
+        -0.02370042, 0.01066893,  -0.06523034, 0.00497636,  0.01933458,  -0.00900802, 0.00430878,  -0.13999483,
+        -0.02377289, 0.01760014,  0.03896973,  0.00831112,  0.15634246,  -0.11109130, -0.11997811, -0.02304414,
+        -0.01989413, -0.12763791, -0.05698400, 0.17125534,  0.00499324,  -0.02953288, 0.09178342,  -0.05001877,
+        0.16157132,  -0.02312993, -0.02932195, -0.04914058, -0.07994118, -0.07199102, -0.04517454, 0.01249476,
+        -0.07525793, -0.00207180, 0.03993115,  -0.01676321, -0.00214832, -0.16074482, -0.05012497, -0.00552153,
+        -0.04302063, -0.00549224, -0.18399858, -0.00767871, -0.02209404, -0.01383207, -0.00082931, -0.19533031,
+    };
+
+    std::vector<int> expected_mask_index = {
+        0,
+        0,
+        0,
+    };
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int>(input_ids);
+    test_case.add_input<int>(segment_ids);
+    test_case.add_expected_output<float>(expected_output);
+    test_case.add_expected_output<int>(expected_mask_index);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_embedding_and_mask) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization_with_segment_embedding_and_mask.onnx"));
+
+    std::vector<int> input_ids = {
+        8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
+    };
+    std::vector<int> segment_ids = {
+        0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 1,
+    };
+    std::vector<int> mask = {
+        1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1,
+    };
+    std::vector<float> expected_output = {
+        -0.06044213, -0.14845914, 0.02457689,  0.02091519,  0.09514004,  -0.10280035, -0.02087995, -0.03323204,
+        -0.02967127, -0.13447416, -0.05191760, -0.16518904, 0.02340531,  0.02176395,  0.04972410,  -0.07360736,
+        0.12192874,  -0.04081530, -0.02338044, -0.05671440, -0.09475864, -0.08944942, -0.03362993, -0.01683486,
+        -0.16770349, -0.07382569, 0.06230322,  0.02215859,  -0.05212611, -0.03934773, -0.04748865, 0.18134241,
+        -0.01965741, -0.02202452, 0.01973994,  0.01575558,  0.04300199,  0.01436110,  -0.00198062, -0.09065692,
+        -0.02923042, -0.00748686, 0.00717049,  0.02638642,  0.12174864,  -0.12973398, -0.11872391, -0.00549398,
+        -0.02386289, -0.02210563, -0.03590920, -0.13728066, -0.01337939, 0.01538021,  -0.14687485, -0.05033565,
+        0.03818212,  -0.04939338, 0.00961064,  -0.07407621, -0.09624685, 0.05594898,  -0.04948713, -0.01305631,
+        -0.03779668, -0.01469170, 0.12346989,  0.02082030,  -0.03449103, -0.06029151, -0.09300473, -0.16308543,
+        -0.02370042, 0.01066893,  -0.06523034, 0.00497636,  0.01933458,  -0.00900802, 0.00430878,  -0.13999483,
+        -0.02377289, 0.01760014,  0.03896973,  0.00831112,  0.15634246,  -0.11109130, -0.11997811, -0.02304414,
+        -0.01989413, -0.12763791, -0.05698400, 0.17125534,  0.00499324,  -0.02953288, 0.09178342,  -0.05001877,
+        0.16157132,  -0.02312993, -0.02932195, -0.04914058, -0.07994118, -0.07199102, -0.04517454, 0.01249476,
+        -0.07525793, -0.00207180, 0.03993115,  -0.01676321, -0.00214832, -0.16074482, -0.05012497, -0.00552153,
+        -0.04302063, -0.00549224, -0.18399858, -0.00767871, -0.02209404, -0.01383207, -0.00082931, -0.19533031,
+    };
+    std::vector<int> expected_mask_index = {
+        5,
+        3,
+        4,
+    };
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int>(input_ids);
+    test_case.add_input<int>(segment_ids);
+    test_case.add_input<int>(mask);
+    test_case.add_expected_output<float>(expected_output);
+    test_case.add_expected_output<int>(expected_mask_index);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_dynamic_shapes) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.onnx"));
+
+    std::vector<int> input_ids = {
+        8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
+    };
+    std::vector<int> segment_ids = {
+        0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 1,
+    };
+    std::vector<float> word_embeddings = {
+        0.96980906, 0.65314001, 0.17090958, 0.35815218, 0.75068617, 0.60783064, 0.32504722, 0.03842543, 0.63427407,
+        0.95894927, 0.65279031, 0.63505888, 0.99529958, 0.58185035, 0.41436860, 0.47469750, 0.62351012, 0.33800763,
+        0.67475230, 0.31720173, 0.77834547, 0.94957107, 0.66252685, 0.01357164, 0.62284607, 0.67365962, 0.97194499,
+        0.87819350, 0.50962436, 0.05571469, 0.45115921, 0.01998767, 0.44171092, 0.97958672, 0.35944447, 0.48089352,
+        0.68866116, 0.88047588, 0.91823548, 0.21682213, 0.56518888, 0.86510259, 0.50896895, 0.91672295, 0.92115760,
+        0.08311249, 0.27771857, 0.00935670, 0.84234208, 0.64717412,
+    };
+    std::vector<float> position_embeddings = {
+        0.84138614, 0.26473016, 0.39782074, 0.55282146, 0.16494046, 0.36980811, 0.14644176, 0.56961840,
+        0.70373726, 0.28847644, 0.43328807, 0.75610667, 0.39609829, 0.89603841, 0.63892108, 0.89155442,
+        0.68005556, 0.44919774, 0.97857094, 0.11620191, 0.76702368, 0.41182014, 0.67543906, 0.24979627,
+        0.31321833, 0.96541619, 0.58846509, 0.65966839, 0.53320622, 0.23053302, 0.39486930, 0.61880857,
+        0.47486752, 0.47013220, 0.71607453, 0.28799102, 0.38346222, 0.74916983, 0.87845218, 0.10286336,
+    };
+    std::vector<float> segment_embeddings = {
+        0.09237389,
+        0.35404667,
+        0.55181628,
+        0.03362509,
+        0.96896178,
+        0.32099724,
+        0.22126268,
+        0.14126390,
+        0.09725992,
+        0.98404223,
+        0.26034093,
+        0.53702253,
+        0.44792616,
+        0.09956909,
+        0.35231167,
+    };
+    std::vector<float> gamma = {
+        0.46924916,
+        0.84114015,
+        0.90464777,
+        0.03755938,
+        0.50831544,
+    };
+    std::vector<float> beta = {
+        0.16684751,
+        0.77905101,
+        0.86493331,
+        0.41139671,
+        0.13997258,
+    };
+    std::vector<int> mask = {
+        1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
+    };
+    std::vector<float> expected_output = {
+        -0.04089922, 0.35108989,  0.30442458,  0.39546335,  1.15422225,  0.10419128,  -0.19301927, 0.01070970,
+        0.43977541,  0.89119899,  -0.51436460, 1.99256825,  1.41077507,  0.38642293,  0.17583044,  0.03320138,
+        1.16508031,  -0.24356931, 0.47440714,  -0.17844005, 0.20463173,  1.90038323,  1.14138567,  0.34504607,
+        0.16403235,  -0.24976699, 0.29362509,  0.34502214,  0.41751838,  1.09390712,  0.12354189,  1.83025289,
+        1.05569196,  0.34413773,  0.35469764,  -0.69760042, 0.76338542,  1.75443077,  0.44126555,  0.18181801,
+        0.73277575,  0.45443264,  0.17068321,  0.36591727,  0.72869974,  -0.56090516, 0.14415455,  1.47314119,
+        0.42908576,  0.73084539,  -0.22373237, 2.26550221,  0.05606699,  0.39417523,  0.35234636,  0.78569502,
+        0.77521765,  -0.65131050, 0.40168875,  0.45527256,  0.38715565,  0.98521245,  2.21446753,  0.36345237,
+        -0.33269632, 0.36558092,  1.36846578,  1.37523413,  0.33698002,  0.28889543,  -0.40639281, 1.01643157,
+        0.59668219,  0.39197800,  1.03101778,  0.02551098,  -0.03612846, -0.01371557, 0.43444607,  0.96746695,
+        0.60583955,  -0.10362893, 0.40574494,  0.38046724,  0.87445319,  -0.00880148, -0.15437943, 0.08118075,
+        0.44650543,  0.85956848,  -0.27865338, 2.10837507,  0.04798460,  0.43948367,  -0.10185169, 0.19978794,
+        1.32323360,  1.20525467,  0.44288942,  -0.84200430, 0.52563053,  0.69949460,  0.73987913,  0.34668452,
+        0.74545687,  0.57696682,  0.22452033,  -0.27099937, 0.39649010,  0.87083614,  -0.18965788, 0.58206403,
+        -0.08108193, 0.42067638,  1.05117214,  -0.34287399, 0.20424896,  0.27994895,  0.46011117,  0.70890665,
+    };
+    std::vector<int> expected_mask_index = {
+        6,
+        5,
+        5,
+    };
+
+    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(function);
+    test_case.add_input<int>(Shape{3, 8}, input_ids);
+    test_case.add_input<int>(Shape{3, 8}, segment_ids);
+    test_case.add_input<float>(Shape{10, 5}, word_embeddings);
+    test_case.add_input<float>(Shape{8, 5}, position_embeddings);
+    test_case.add_input<float>(Shape{3, 5}, segment_embeddings);
+    test_case.add_input<float>(Shape{5}, gamma);
+    test_case.add_input<float>(Shape{5}, beta);
+    test_case.add_input<int>(Shape{3, 8}, mask);
+    test_case.add_expected_output<float>(Shape{3, 8, 5}, expected_output);
+    test_case.add_expected_output<int>(Shape{3}, expected_mask_index);
+    test_case.run_with_tolerance_as_fp(1e-6);
 }
diff --git a/ngraph/test/runtime/ie/unit_test.manifest b/ngraph/test/runtime/ie/unit_test.manifest
index e0d6c22644656d..c66b35eccaa8cb 100644
--- a/ngraph/test/runtime/ie/unit_test.manifest
+++ b/ngraph/test/runtime/ie/unit_test.manifest
@@ -1584,3 +1584,5 @@ IE_CPU.onnx_model_gather_float_2D_neg_indices
 
 # CPU plug-in doesn't support operation with dynamic rank
 onnx_model_skip_layer_normalization_dynamic_shapes
+# Doesn't support op with dynamic shapes
+onnx_model_embed_layer_normalization_dynamic_shapes