diff --git a/docs/images/models/DAT.png b/docs/images/models/DAT.png
new file mode 100644
index 000000000..7b3b56538
Binary files /dev/null and b/docs/images/models/DAT.png differ
diff --git a/docs/source/models/dssm_derivatives.md b/docs/source/models/dssm_derivatives.md
index 9271beb82..986bdbcb4 100644
--- a/docs/source/models/dssm_derivatives.md
+++ b/docs/source/models/dssm_derivatives.md
@@ -1,6 +1,6 @@
 # DSSM衍生扩展模型
 
-## DSSM + SENet
+## 1. DSSM + SENet
 
 ### 简介
 
@@ -84,7 +84,7 @@ model_config:{
 
 [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
 
-## 并行DSSM
+## 2. 并行DSSM
 
 在召回中，我们希望尽可能把不同的特征进行交叉融合，以便提取到隐藏的信息。而不同的特征提取器侧重点不尽相同，比如MLP是隐式特征交叉，FM和DCN都属于显式、有限阶特征交叉, CIN可以实现vector-wise显式交叉。因此可以让信息经由不同的通道向塔顶流动，每种通道各有所长，相互取长补短。最终将各通道得到的Embedding聚合成最终的Embedding，与对侧交互，从而提升召回的效果。
 
@@ -93,3 +93,54 @@ model_config:{
 ### 示例Config
 
 [parallel_dssm_on_taobao_backbone.config](https://github.com/alibaba/EasyRec/tree/master/samples/model_config/parallel_dssm_on_taobao_backbone.config)
+
+## 3. 对偶增强双塔 Dual Augmented Two-Tower
+
+双塔模型对用户和物品的特征分开进行建模，在对特征进行了多层神经网络的整合后进行交互。由于网络的整合可能会损失一部分信息，因此过晚的user/item交互不利于模型的学习，这也是DSSM的一个主要的弊端。在对偶增强双塔算法中，作者设计了一个辅助向量，通过学习对user和item进行增强，使得user和item的交互更加有效。
+
+![dat](../../images/models/dat.png)
+
+### 配置说明
+
+作为DSSM的衍生模型，DAT的配置与DSSM类似，在model_config中除了user和item的feature_group外，还需要增加user_id_augment feature_group和item_id_augment feature_group, 作为模型输入的增强向量。
+两塔各自的DNN最后一层输出维度需要和user_id_augment的embedding维度保持一致，以便构造AMM损失（Adaptive-Mimic Mechanism）。
+
+```
+  feature_groups: {
+    group_name: 'user_id_augment'
+    feature_names: 'user_id'
+    wide_deep:DEEP
+  }
+  feature_groups: {
+    group_name: 'item_id_augment'
+    feature_names: 'adgroup_id'
+    wide_deep:DEEP
+  }
+
+  dat {
+    user_tower {
+      id: "user_id"
+      dnn {
+        hidden_units: [ 128,  32]
+        # dropout_ratio : [0.1, 0.1, 0.1, 0.1]
+      }
+    }
+    item_tower {
+      id: "adgroup_id"
+      dnn {
+        hidden_units: [ 128, 32]
+      }
+    }
+    simi_func: COSINE
+    temperature: 0.01
+    l2_regularization: 1e-6
+  }
+```
+
+### 示例Config
+
+[dat_on_taobao.config](https://github.com/alibaba/EasyRec/tree/master/samples/model_config/dat_on_taobao.config)
+
+### 参考论文
+
+[A Dual Augmented Two-tower Model for Online Large-scale Recommendation](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_4.pdf)
diff --git a/easy_rec/python/model/dat.py b/easy_rec/python/model/dat.py
index fceb8cee8..5c312299c 100644
--- a/easy_rec/python/model/dat.py
+++ b/easy_rec/python/model/dat.py
@@ -25,6 +25,15 @@ def __init__(self,
                               is_training)
     assert self._model_config.WhichOneof('model') == 'dat', \
         'invalid model config: %s' % self._model_config.WhichOneof('model')
+
+    feature_group_names = [
+        fg.group_name for fg in self._model_config.feature_groups
+    ]
+    assert 'user' in feature_group_names, 'user feature group not found'
+    assert 'item' in feature_group_names, 'item feature group not found'
+    assert 'user_id_augment' in feature_group_names, 'user_id_augment feature group not found'
+    assert 'item_id_augment' in feature_group_names, 'item_id_augment feature group not found'
+
     self._model_config = self._model_config.dat
     assert isinstance(self._model_config, DATConfig)
 
diff --git a/samples/model_config/dat_on_taobao.config b/samples/model_config/dat_on_taobao.config
new file mode 100644
index 000000000..e972149d7
--- /dev/null
+++ b/samples/model_config/dat_on_taobao.config
@@ -0,0 +1,322 @@
+train_input_path: "data/test/tb_data/taobao_train_data"
+eval_input_path: "data/test/tb_data/taobao_test_data"
+model_dir: "experiments/dat_taobao_ckpt"
+
+train_config {
+  log_step_count_steps: 200
+  optimizer_config: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.001
+          decay_steps: 1000
+          decay_factor: 0.5
+          min_learning_rate: 0.00001
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  save_checkpoints_steps: 4000
+  sync_replicas: false
+  num_steps: 100000
+}
+
+eval_config {
+  metrics_set: {
+    recall_at_topk {
+      topk: 50
+    }
+  }
+  metrics_set: {
+    recall_at_topk {
+      topk: 10
+    }
+  }
+  metrics_set: {
+    recall_at_topk {
+      topk: 5
+    }
+  }
+  metrics_set: {
+    recall_at_topk {
+      topk: 1
+    }
+  }
+}
+
+data_config {
+  input_fields {
+    input_name:'clk'
+    input_type: INT32
+  }
+  input_fields {
+    input_name:'buy'
+    input_type: INT32
+  }
+  input_fields {
+    input_name: 'pid'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'adgroup_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cate_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'campaign_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'customer'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'brand'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'user_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cms_segid'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cms_group_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'final_gender_code'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'age_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'pvalue_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'shopping_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'occupation'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'new_user_class_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'tag_category_list'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'tag_brand_list'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'price'
+    input_type: INT32
+  }
+
+  label_fields: 'clk'
+  batch_size: 512
+  num_epochs: 10000
+  prefetch_size: 32
+  input_type: CSVInput
+
+  negative_sampler {
+    input_path: 'data/test/tb_data/taobao_ad_feature_gl'
+    num_sample: 2048
+    num_eval_sample: 2048
+    attr_fields: 'adgroup_id'
+    attr_fields: 'cate_id'
+    attr_fields: 'campaign_id'
+    attr_fields: 'customer'
+    attr_fields: 'brand'
+    item_id_field: 'adgroup_id'
+  }
+}
+
+feature_config: {
+  features: {
+    input_names: 'pid'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'adgroup_id'
+    feature_type: IdFeature
+    embedding_dim: 32
+    hash_bucket_size: 100000
+  }
+  features: {
+    input_names: 'cate_id'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10000
+  }
+  features: {
+    input_names: 'campaign_id'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features: {
+    input_names: 'customer'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features: {
+    input_names: 'brand'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features: {
+    input_names: 'user_id'
+    feature_type: IdFeature
+    embedding_dim: 32
+    hash_bucket_size: 100000
+  }
+  features: {
+    input_names: 'cms_segid'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100
+  }
+  features: {
+    input_names: 'cms_group_id'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100
+  }
+  features: {
+    input_names: 'final_gender_code'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'age_level'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'pvalue_level'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'shopping_level'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'occupation'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+    input_names: 'new_user_class_level'
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features: {
+     input_names: 'tag_category_list'
+     feature_type: TagFeature
+     separator: '|'
+     hash_bucket_size: 100000
+     embedding_dim: 16
+  }
+  features: {
+     input_names: 'tag_brand_list'
+     feature_type: TagFeature
+     separator: '|'
+     hash_bucket_size: 100000
+     embedding_dim: 16
+  }
+  features: {
+    input_names: 'price'
+    feature_type: IdFeature
+    embedding_dim: 16
+    num_buckets: 50
+  }
+}
+model_config:{
+  model_class: "DAT"
+  feature_groups: {
+    group_name: 'user'
+    feature_names: 'user_id'
+    feature_names: 'cms_segid'
+    feature_names: 'cms_group_id'
+    feature_names: 'age_level'
+    feature_names: 'pvalue_level'
+    feature_names: 'shopping_level'
+    feature_names: 'occupation'
+    feature_names: 'new_user_class_level'
+    feature_names: 'tag_category_list'
+    feature_names: 'tag_brand_list'
+    wide_deep:DEEP
+  }
+  feature_groups: {
+    group_name: "item"
+    feature_names: 'adgroup_id'
+    feature_names: 'cate_id'
+    feature_names: 'campaign_id'
+    feature_names: 'customer'
+    feature_names: 'brand'
+    #feature_names: 'price'
+    #feature_names: 'pid'
+    wide_deep:DEEP
+  }
+  feature_groups: {
+    group_name: 'user_id_augment'
+    feature_names: 'user_id'
+    wide_deep:DEEP
+  }
+  feature_groups: {
+    group_name: 'item_id_augment'
+    feature_names: 'adgroup_id'
+    wide_deep:DEEP
+  }
+
+  dat {
+    user_tower {
+      id: "user_id"
+      dnn {
+        hidden_units: [ 128,  32]
+        # dropout_ratio : [0.1, 0.1, 0.1, 0.1]
+      }
+    }
+    item_tower {
+      id: "adgroup_id"
+      dnn {
+        hidden_units: [ 128, 32]
+      }
+    }
+    simi_func: COSINE
+    temperature: 0.01
+    l2_regularization: 1e-6
+  }
+  embedding_regularization: 5e-5
+  loss_type: SOFTMAX_CROSS_ENTROPY
+}
+
+export_config {
+}