File size: 3,000 Bytes
60b78dc 5ae9a3b feb5a19 5ae9a3b feb5a19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
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" |