Update README.md

README.md

@@ -1,149 +1,201 @@
----
-license: mit
-task_categories:
-- question-answering
-language:
-- en
-- zh
-pretty_name: MQDialog
-size_categories:
-- 1K<n<10K
----
-
-# Dataset Card for Dataset Name
-
-<!-- Provide a quick summary of the dataset. -->
-
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+# Multi-Questioner Dialogue (MQDialog) Dataset
 
 ## Dataset Details
 
 ### Dataset Description
+The Multi-Questioner Dialogue (MQDialog) dataset is designed to facilitate research in questioner-aware personalization. It contains dialogues with various questioners for each reponder. The dataset is derived from English and Chinese scripts of popular TV shows and real-world conversations. It includes dialogues where selected leading actors act as responders, while other characters or contacts serve as questioners. The dataset contains a diverse set of 12 responders and 173 questioners. The dataset supports research on dialogue generation, response evaluation, and questioner-aware personalization in multi-turn conversations.
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
+### Dataset Sources 
 
-<!-- This section describes suitable use cases for the dataset. -->
+- **English scripts**: The Big Bang Theory, Friends, and Modern Family.
+- **Chinese scripts**: My Own Swordsman and Empresses in the Palace.
+- **Real-world conversations (WeChat)**: Records from a single user, focusing on two-person chats. *(Not public, but you can extract the data using the code we provided)*
 
-[More Information Needed]
+## Direct Use
+The dataset is suitable for:
+- Training and evaluating questioner-aware multi-turn dialogue systems.
+- Studying personality-aligned response generation.
+- Benchmarking the performance of dialogue models with multi-questioner setups.
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
 
 ## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+- **Responders**: 12 leading actors from TV scripts and a single WeChat user.
+- **Questioners**: 173 individuals interacting with the responders, the detailed information is listed in the Table.
+- **Splits**: Randomly divided into training (3761 dialogues per responder on average) and testing (917 dialogues per responder on average).
+
+<table>
+    <tr>
+        <td>Language</td>
+        <td>Data Source</td>
+        <td># Questioners</td>
+        <td>Questioner Examples</td>
+        <td>Responder</td>
+        <td># train</td>
+        <td># test</td>
+    </tr>
+    <tr>
+        <td rowspan="6">English</td>
+        <td rowspan="2">The Big Bang Theory</td>
+        <td>14</td>
+        <td>Priya, Barry, Howard, Leonard, etc.</td>
+        <td>Sheldon</td>
+        <td>4805</td>
+        <td>1101</td>
+    </tr>
+    <tr>
+        <td>12</td>
+        <td>Bernadette, Penny, Raj, Stuart, etc.</td>
+        <td>Leonard</td>
+        <td>4607</td>
+        <td>1014</td>
+    </tr>
+    <tr>
+        <td rowspan="2">Friends</td>
+        <td>12</td>
+        <td>Amy, Chandler, Charlie, Joey, etc.</td>
+        <td>Rachel</td>
+        <td>3768</td>
+        <td>870</td>
+    </tr>
+    <tr>
+        <td>20</td>
+        <td>Ben, Mike, Gary, Paul, etc.</td>
+        <td>Ross</td>
+        <td>3839</td>
+        <td>960</td>
+    </tr>
+    <tr>
+        <td rowspan="2">Modern Family</td>
+        <td>9</td>
+        <td>Alex, Cameron, Dylan, Gloria, etc.</td>
+        <td>Claire</td>
+        <td>1161</td>
+        <td>281</td>
+    </tr>
+    <tr>
+        <td>8</td>
+        <td>Haley, Jay, Luke, Mitchell, etc.</td>
+        <td>Phil</td>
+        <td>881</td>
+        <td>246</td>
+    </tr>
+    <tr>
+        <td rowspan="6">Chinese</td>
+        <td rowspan="3">My Own Swordsman</td>
+        <td>16</td>
+        <td>Bai Sanniang, Guo Furong, Mo Xiaobei, etc.</td>
+        <td>Tong Xiangyu</td>
+        <td>3200</td>
+        <td>831</td>
+    </tr>
+    <tr>
+        <td>16</td>
+        <td>Bao Daren, Ji Wuming, Zhu Wushuang, etc.</td>
+        <td>Bai Zhantang</td>
+        <td>2995</td>
+        <td>857</td>
+    </tr>
+    <tr>
+        <td>8</td>
+        <td>Li Dazui, Xing Butou, Yan Xiaoliu, etc.</td>
+        <td>Lv Xiucai</td>
+        <td>1635</td>
+        <td>409</td>
+    </tr>
+    <tr>
+        <td rowspan="2">Empresses in the Palace</td>
+        <td>17</td>
+        <td>Cao Guiren, Mei Zhuang, Liu Zhu, etc.</td>
+        <td>Zhen Huan</td>
+        <td>1229</td>
+        <td>350</td>
+    </tr>
+    <tr>
+        <td>11</td>
+        <td>Consort Hua, Empress, Huan Bi, etc.</td>
+        <td>Emperor</td>
+        <td>704</td>
+        <td>200</td>
+    </tr>
+    <tr>
+        <td>WeChat Records</td>
+        <td>30</td>
+        <td>Author's contacts</td>
+        <td>Author</td>
+        <td>-</td>
+        <td>-</td>
+    </tr>
+</table>
+*Note: The last response from the responder serves as the ground truth, while preceding dialogues constitute the dialogue history. We provide a compact version of the training set because, during training, the answers within the dialogue history can be used to compute the loss, eliminating the need for the answer in the last sentence.
+
+### Data Files & Code
+
+For each responder, dialogues with different questioners are stored in the corresponding folder, `diags_two_role_{responder_name}`. Intermediate results from data processing are also provided. The final datasets used for questioner-aware personalization are:  
+- `{script_name}_diags_{responder_name}_{questioner_name}_{responder_name}_response_L512_dev.json`  
+- `{script_name}_diags_{responder_name}_{questioner_name}_{responder_name}_response_L512_train.json`  
+
+Additionally, dialogues with different questioners are clustered based on query similarity. The clustering results are stored in the `diags_two_role_{responder_name}_clustered` folder.  
+
+We have provided the preprocessed raw data for each scripts named with `{script_name}_dialgs.json`. To extract dialogues for one responder, please run the python file `extract_two_role_diag_{responder_name}.py` under each subfolder. 
+
+**Related functions**:
+- `get_role_list()`: get whole role name   
+- `extract_diag_between_two_role()`: extract and only reserve diags between two roles  
+- `clean_diag()`: remove duplicates, remove conversations with only one person, and remove empty values  
+- `clean_diag_with_repeated()`: remove conversations with only one person, and remove empty values
+- `split_train_and_dev()`: split training set and validation set
+- `split_diag_with_sliding_window()`: construct diags with limited length through a sliding window
+- `extract_diag_for_target_from_role_conv()`: only reserve diags that the response is from target role
+
+### Data Instances
+Below is an example from the dataset, it contains conversations between the `target_role` (i.e. `responder`) and the `input_role` (i.e. `questioner`). 
+```json
+{
+    "id": "episode_14_chunk_6_index_0_part2_piece_0",
+    "conversations": [
+        {
+            "from": "Bernadette",
+            "value": "Did you hear? Isn’t it terrible?"
+        },
+        {
+            "from": "Leonard",
+            "value": "Have you seen him?"
+        },
+        {
+            "from": "Bernadette",
+            "value": "They wouldn’t let me in. Oh my Howie."
+        },
+        {
+            "from": "Leonard",
+            "value": "It’ll be okay. It’ll be okay."
+        }
+    ],
+    "target_role": "Leonard",
+    "target_role_short": "Leonard",
+    "input_role": "Bernadette",
+    "input_role_short": "Bernadette",
+    "role_pair_id": 8
+}
+```
 
 ## Dataset Creation
 
 ### Curation Rationale
+MQDialog was created to address the need for a multilingual, multi-questioner dataset that reflects questioner-aware personalized response generation in diverse conversational contexts.
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+### Data Collection and Processing
+- **Scripts**: Extracted dialogues between a responder (leading actor) and questioners (other characters), ensuring a clean dataset by removing errors, repeated content, and irrelevant entries.
+- **Real-world records**: Focused on one-on-one conversations, with new dialogue sessions defined by a time gap (e.g., 3 hours).
+- **Filtering**: Questioners with fewer than 20 interactions were excluded to ensure meaningful analysis.
 
 ### Recommendations
+- Use the dataset in conjunction with other corpora to mitigate cultural or linguistic biases.
+- Ensure responsible use of the data, particularly when training models for real-world applications.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
-[More Information Needed]
 
-## More Information [optional]
 
-[More Information Needed]
 
-## Dataset Card Authors [optional]
 
-[More Information Needed]
 
-## Dataset Card Contact
 
-[More Information Needed]