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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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pretty_name: MT-Mind2Web |
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tags: |
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- web navigation |
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- conversation |
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--- |
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# MT-Mind2Web Dataset |
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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. |
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## Statistics |
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| | Train | Test-Task | Test-Website | Test-Subdomain | |
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|--------------------|-------|-----------|--------------|----------------| |
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| # Conversations | 600 | 34 | 42 | 44 | |
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| # Turns | 2,896 | 191 | 218 | 216 | |
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| Avg. # Turn/Conv. | 4.83 | 5.62 | 5.19 | 4.91 | |
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| Avg. # Action/Turn | 2.95 | 3.16 | 3.01 | 3.07 | |
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| Avg. # Element/Turn| 573.8 | 626.3 | 620.6 | 759.4 | |
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| Avg. Inst. Length | 36.3 | 37.4 | 39.8 | 36.2 | |
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| Avg. HTML Length | 169K | 195K | 138K | 397K | |
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## Dataset Structure |
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- "task_id" (str): unique id for each task |
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- "website" (str): website name |
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- "domain" (str): website domain |
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- "subdomain" (str): website subdomain |
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- "turns" (list[dict]): list of subtasks |
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- "annotation_id" (str): unique id for each subtask |
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- "confirmed_task" (str): subtask description |
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- "action_reprs" (list[str]): human readable string representation of the action sequence |
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- "actions" (list[dict]): list of actions (steps) to complete the subtask |
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- "action_uid" (str): unique id for each action (step) |
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- "raw_html" (str): raw html of the page before the action is performed |
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- "cleaned_html" (str): cleaned html of the page before the action is performed |
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- "operation" (dict): operation to perform |
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- "op" (str): operation type, one of CLICK, TYPE, SELECT |
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- "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used |
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- "value" (str): optional value for the operation, e.g., text to type, option to select |
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- "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". |
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- "tag" (str): tag of the element |
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- "is_original_target" (bool): whether the element is the original target labeled by the annotator |
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- "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details. |
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- "backend_node_id" (str): unique id for the element |
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- "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict |
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- "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates" |
