Update README.md

README.md

@@ -12,7 +12,7 @@ language:
 - `mmrag_train.json`: Training set for model training.
 - `mmrag_dev.json`: Validation set for hyperparameter tuning and development.
 - `mmrag_test.json`: Test set for evaluation.
-- `processed_documents.json`: The document pool used for document retrieval.
+- `processed_documents.json`: The chunks used for retrieval. 
 
 ---
 
@@ -22,12 +22,12 @@ The three files are all lists of dictionaries. Each dictionary contains the foll
 
 ### 🔑 `id`
 
-- **Description**: Unique query identifier, structured as `dataset_queryID`.
-- **Example**: `ott_144`.
+- **Description**: Unique query identifier, structured as `SourceDataset_queryIDinDataset`.
+- **Example**: `ott_144`, means this query is picked from OTT-QA dataset
 
 ### ❓ `query`
 
-- **Description**: Text of the question.
+- **Description**: Text of the query.
 - **Example**: `"What is the capital of France?"`
 
 ### ✅ `answer`
@@ -37,29 +37,30 @@ The three files are all lists of dictionaries. Each dictionary contains the foll
 
 ### 📑 `relevant_chunks`
 
-- **Description**: Dictionary of relevant document IDs and their corresponding relevance scores. The document IDs are structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex`
-- **Example**: `{"ott_23573_2": 1}`
+- **Description**: Dictionary of annotated chunk IDs and their corresponding relevance scores. The context of chunks can be get from processed_documents.json. relevance score is in range of {0(irrelevant), 1(Partially relevant), 2(gold)}
+- **Example**: ```json{"ott_23573_2": 1, "ott_114_0": 2, "m.12345_0": 0}```
 
 ### 📖 `ori_context`
 
-- **Description**: A list of the original document IDs related to the query.
-- **Example**: `["ott_6104"]`
+- **Description**: A list of the original document IDs related to the query. This field can help to get the relevant document provided by source dataset.
+- **Example**: `["ott_144"]`, means all chunk IDs start with "ott_114" is from the original document.
 
 ### 📜 `dataset_score`
 
-- **Description**: The relevance score of all datasets regarding this query.
-- **Example**: `{"tat": 0, "triviaqa": 2, "ott": 4, "kg": 1, "nq": 0}`
+- **Description**: The datset-level relevance labels. With the routing score of all datasets regarding this query.
+- **Example**: `{"tat": 0, "triviaqa": 2, "ott": 4, "kg": 1, "nq": 0}`, where 0 means there is no relevant chunks in the dataset. The higher the score is, the more relevant chunks the dataset have.
 
 ---
 
 ## 📚 Knowledge Base: `processed_documents.json`
 
-This file is a list of documents used for document retrieval, which contains the following fields:
+This file is a list of chunks used for document retrieval, which contains the following fields:
 
 ### 🔑 `id`
 
 - **Description**: Unique document identifier, structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex`
-- **example**: `ott_8075_0`
+- **example1**: `ott_8075_0`  (chunks from NQ, TriviaQA, OTT, TAT)
+- **example2**: `m.0cpy1b_5`  (chunks from documents of knowledge graph(Freebase))
 
 ### 📄 `text`