---
license: cc-by-4.0
language:
- en
pretty_name: MT-Mind2Web
tags:
- web navigation
- conversation
---
# MT-Mind2Web Dataset
MT-Mind2Web is constructed by using the single-turn interactions from [Mind2Web](https://huggingface.co/datasets/osunlp/Mind2Web), an expert-annotated web navigation dataset, as the guidance to construct conversation sessions. 

## Statistics

|                    | Train | Test-Task | Test-Website | Test-Subdomain |
|--------------------|-------|-----------|--------------|----------------|
| # Conversations    |  600  | 34        | 42           | 44             |
| # Turns            | 2,896 | 191       | 218          | 216            |
| Avg. # Turn/Conv.  | 4.83  | 5.62      | 5.19         | 4.91           |
| Avg. # Action/Turn | 2.95  | 3.16      | 3.01         | 3.07           |
| Avg. # Element/Turn| 573.8 | 626.3     | 620.6        | 759.4          |
| Avg. Inst. Length  | 36.3  | 37.4      | 39.8         | 36.2           |
| Avg. HTML Length   | 169K  | 195K      | 138K         | 397K           |


## Dataset Structure
- "task_id" (str): unique id for each task
- "website" (str): website name
- "domain" (str): website domain
- "subdomain" (str): website subdomain
- "turns" (list[dict]): list of subtasks
    - "annotation_id" (str): unique id for each subtask
    - "confirmed_task" (str): subtask description
    - "action_reprs" (list[str]): human readable string representation of the action sequence
    - "actions" (list[dict]): list of actions (steps) to complete the subtask
        - "action_uid" (str): unique id for each action (step)
        - "raw_html" (str): raw html of the page before the action is performed
        - "cleaned_html" (str): cleaned html of the page before the action is performed
        - "operation" (dict): operation to perform
            - "op" (str): operation type, one of CLICK, TYPE, SELECT
            - "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used
            - "value" (str): optional value for the operation, e.g., text to type, option to select
        - "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html".
            - "tag" (str): tag of the element
            - "is_original_target" (bool): whether the element is the original target labeled by the annotator
            - "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.
            - "backend_node_id" (str): unique id for the element
            - "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict
        - "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates"