diff --git a/doc/ImportONNXDefs.md b/doc/ImportONNXDefs.md
index 1413341..0172919 100644
--- a/doc/ImportONNXDefs.md
+++ b/doc/ImportONNXDefs.md
@@ -2,25 +2,31 @@
 The specifications of ONNX are defined under onnx/defs directory in ONNX projects. 
 There is a python script onnx/defs/gen_doc.py that automatically generate documents about operations in ONNX (docs/Operations.md). 
 ONNX MLIR modified this script to import ONNX specifications into ONNX MLIR. There are two files generated for ONNX MLIR with the modified gen_doc.py:
-1. src/dialect/onnx/onnxop.inc: Operation defintion for MLIR tablegen. Will be included in src/dialect/onnx/onnx.td
-2. src/builder/op_build_table.inc: c code for ONNX MLIR frontend to import operation nodes from ONNX model. Will be included in src/builder/frontend_dialect_transformer.cpp
+1. src/Dialect/ONNX/ONNXOps.td.inc: Operation defintion for MLIR tablegen. Will be included in src/Dialect/ONNX/ONNXOps.td
+2. src/Builder/OpBuildTable.inc: c code for ONNX MLIR frontend to import operation nodes from ONNX model. Will be included in src/Builder/FrontendDialectTransformer.cpp
 
 ## How to use the script
 1. Get ONNX. You can use onnx-mlir/third_party/onnx
-2. In your ONNX directory, copy the script docs/gen_doc.py in your ONNX MLIR to onnx/defs in ONNX 
-3. Run the script:  python onnx/defs/gen_doc.py
-4. Two files, onnxop.inc and op_buid_table.inc should be generated in current directory
-5. copy the two file into your ONNX MLIR: cp onnxop.inc your_onnx-mlir/src/dialect/onnx/onnxop.inc; cp op_build_table.inc your_onnx-mlir/src/builder
-6. go to your ONNX MLIR and build
+2. Perform the following steps (assume that we use onnx-mlir/third_party/onnx):
+```bash
+$ cd onnx-mlir
+$ cp doc/gen_doc.py third_party/onnx/onnx/defs/
+$ cd third_party/onnx
+$ python onnx/defs/gen_doc.py
+$ cd ../..
+$ cp third_party/onnx/onnx/defs/ONNXOps.td.inc src/Dialect/ONNX/
+$ cp third_party/onnx/onnx/defs/OpBuildTable.inc src/Builder/
+```
 
 ## Consistency
-The Operators.md generated by gen_doc.py is copied into doc. Please refer to this specification, not the one in onnx github, to make sure operators are consistent in version with onnxop.inc.
+The Operators.md generated by gen_doc.py is copied into doc. Please refer to this specification, not the one in onnx github, to make sure operators are consistent in version with ONNXOps.td.inc.
 
 ## Customization
 In addition to following the ONNX specification, the modified gen_doc.py provides some mechanism for you to customize the output. 
 Several tables are defined at the beginning of the script:
-1. special_attr_defaults: gives attribute special default value. 
+1. special_attr_defaults: gives attribute special default value.
 2. special_op_handler: creates special import function in frontend_dialect_transformer.cpp. Currently special handler is used for operations with oprational arguments
-3. ShapeInferenceList: list of operations which has shape inference defined
-4. CanonicalList : list of operations which has canonical form
-5. manual_code_in_op_def: provides a way to specify any code for an operation in its tablegen
+3. OpsWithShapeInference: list of operations which have shape inference defined
+4. OpsWithCanonicalizer: list of operations which have canonical form
+5. OpsWithPromotableConstOperands: list of operations which have operands that, if produced by constant operations, should be promoted to become an attribute (via attribute promotion)
+6. custom_builder_ops_list: list of operations which need custom build methods to deduce result types