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TASK_INPUTS.md

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Allowed Inputs

  • The guideline below shows the input fields that are allowed (default) and disallowed (marked as 'X') at inference time, for each subtask.
  • Participants are free to use any of the fields below during training though as additional supervision signals, and e.g. at the inference time use the reconstructed / predicted values instead.
Key Subtask #1
(API Prediction)
Subtask #2
(Response Generation)
Subtask #3
(MM-DST)
JSON File (Turn Level Input Fields)
belief_state
(prediction target)
domain
state_graph_0
state_graph_1
state_graph_2
system_transcript
(current turn)

(prediction target)
system_transcript
(previous turns)
system_transcript_annotated
system_turn_label
transcript
transcript_annotated
turn_idx
turn_label
visual_objects
raw_assistant_keystrokes
JSON File (Dialog Level Input Fields)
dialogue_coref_map
dialogue_idx
domains
API Call File
action
(current turn)

(prediction target)
action
(previous turns)
action_supervision
(current turn)
action_supervision
(previous turns)
focus_images (Fashion)
carousel_state (Furniture)
action_output_state(Furniture)
Metadata Files
fashion_metadata.json
furniture_metadata.csv

Notes

transcript_annotated provides the detailed structural intents, slots and values for each USER turn, including the text spans. system_transcript_annotated provides the similar information for ASSISTANT turns.

turn_label expands transcript_annotated with the coreference labels annotated as well. objects field in turn_label includes a list of objects referred to in each turn - each marked with a local index throughout the dialog (obj_idx) and obj_type. system_turn_label provides the similar information for ASSISTANT turns.

belief_state provides the intents, slots, and values, where their slots and values are cumulative throughout the dialog whenever applicable. Each slot name is prepended with its domain name, e.g. {domain}-{slot_name}. Specifically, we include an object slot called {domain}-O whose values are OBJECT_{local_idx}. For instance, a belief_state with act: DA:REQUEST:ADD_TO_CART:CLOTHING with a slot [[‘fashion-O’, ‘OBJECT_2’], [‘fashion-O’, ‘OBJECT_3’]] would annotate a user belief state with the intention of adding objects 2 and 3 to the cart.

The entire catalog information is stored in either fashion_metadata.json or furniture_metadata.csv. The API calls provide the state of the carousel (furniture) or focus item (fashion) after the ground truth API / actions have been called. By using these two, one should be able to retrieve the entire information about the catalog items that are potentially described in the system response.

For more details, please refer to the full description in the data README document.