# DialogZoo ## Data construction To replicate data construction, three steps are required: * Download data: ```bash scripts/download.sh``` * Convert origin data into our unified format: ```bash scripts/convert_to_unified.sh``` ``` { # Optional values: `single` or `multi`. Indicates whether it is a single-turn or multi-turn dialogue. "turn": str, # The domains involved in the dialogue (a list because some dialogues involve multiple domains). "domain": [], # The language of the dialogue, based on the original dataset annotations (e.g., en, fr, etc.). "locale": str, # The dialogue, represented as a list where each element is a dictionary for a single turn. "dialog": [ { # The roles involved in each turn. Some datasets may have multiple roles per turn, so it's a list. # For datasets without role annotations: # * Use `ROLE` for single-turn data. # * Use `ROLE1`, `ROLE2`, etc., for multi-turn data. "roles": [str, ...], # The text of the current turn. "utterance": str, # Used for the "answer" in QA tasks. "start": int, "end": int, "dialog_turn": int # Rewritten text corresponding to the current turn. "rewritten": str, # Dialogue state, represented as a list where each element includes: # Domain: Some datasets constrain slot-value pairs within specific domains. # Intent: Some datasets constrain slot-value pairs within specific intents. # Slot-value pairs: A list where each element includes a slot and its corresponding values. # Slot name: A string. # Values: A list where a slot may have multiple values. # Each value includes four parts: the value itself, the normalized value, # the character index in the current turn's text, and more. # Relation: Some slots are equal to a value, while others are greater than a value. # Defaults to "equal" if not specified. # Requested slots: A list of slots that need to be queried but are not filled in the current state. "belief_state": [ { # Intent "intent": str, # Slot-value pairs "informed_slot_value_table": [ { # Slot name "slot": str, # Values "values": [{ # Actual value "value": str, # Normalized value "cononical_value": str }, ...], # Slot-value relation "relation": str, }, ... ], # Requested slots "requested_slots": [], # Domain "domain": str, }, ... ], # Dialogue actions, represented as a list where each element includes: # Domain: Some datasets constrain slot-value pairs within specific domains. # Action: The actions involved in the current turn. # Slot-value pairs: Same as in dialogue state. "dialog_acts": [ { # Action "act": str, # Slot-value pairs "slot_value_table": [ { # Slot name "slot": str, # Slot-value relation "relation": str, # Values "values": [ { # Actual value "value": str, # Normalized value "cononical_value": str, # Start position "start": int, # End position "end": int, },... ] }, ... ], # Domain "domain": str, }, ... ], # Slot filling "slots_to_fill": { "intent": str, "slot_value_table": [ { "slot": str, "values": [ { "value": str, "start": int, "end": int } ], "relation": str, # '=', '<=', and so on } ] }, # Named entity recognition "named_entity_recognition": [ { "type": str, "values": [ { "value": str, "start": int, "end": int }, ... ] }, ... ], "characters": [ { "value": str, "start": int, "end": int } ] # Intent detection "active_intents": [str], # Query "query" { ... }, # Query result "querying_result": { ... }, # Recorded satisfied main items "main_items": [], # Aspect Sentiment Triplet Extraction task, represented as a list where each element includes three parts: # Target entity. # Related sentiment. # Words reflecting the sentiment. "aspects": [ { # Target entity "target": { # Entity value "value": str, # Start position in the current turn's text "start": int, # End position in the current turn's text "end": int }, # Category of the target entity "category": str, # Words reflecting the sentiment "opinion": { # Sentiment word "value": str, # Start position in the current turn's text "start": int, # End position in the current turn's text "end": int }, # Related sentiment "sentiment": str } ], "emotions": [ { "emotion": str, "sentiment": "positive", "negative", or "ambiguous", "evidences": [ { "turn": int, "span": str, "start": int, "end": int } ], "evidence_types": [str] } ], "kg_label": str, # Knowledge that may be required for each turn, used to select knowledge. "knowledge_to_select": str, # SQL "sql": str, # Rewritten text "rewritten": str, "roles_to_select": [str], }, ], # Summary derived from the entire dialogue. "summary": str, # Entity relations determined from the entire dialogue. "instance_relations": [ { "instance1": str, "instance2": str, "relations": [ { "relation": str, "trigger": str }, ... ] }, ... ] # Role relations determined from the entire dialogue. "role_relations": [ { "turn": int, "relation": str } ], # Used in FriendsPersona to determine a character's persona based on the entire dialogue. "role_personas": [ { "name": str, "personas": [ { "persona": str, "sentiment": int }, ... ] } ], # External knowledge required for the dialogue. "knowledge": { # `text`, `persona`, `kg`, or `schema`. "type": str, # For `text`. "value": str, # For `persona`, persona of all roles, used for personachat. "value": [ { # Role name, matching the dialogue turn. "role": str, # Persona description, which may include several sentences. "description": [] }, ... ] # For `kg`. "value": { # `directed` or `undirected`. "direction": str, # Graph. "graph": [ { # Source node. "source": str, # Target node. "target": str, # Relation. "relation": str }, ... ] } # For `schema`. "value": { ... } # For `dialogue`. "value": { "dialog": [], "relations": [] } # For `wiki`. "value": { ... } # For `sql`. "value": [ { "turn": int, "sql": str, "result": ... }, ... ], # For dialogues based on specific article excerpts, this field indicates the article and section titles. "value": { "article title": str, "section title": str }, } } ``` * Linearize: ```bash scripts/convert_to_seq.sh``` The processed data is located at ```DialogZoo.tar```. ## Data statistics |ID| MRC| ER| MCQA| QCR| RRR| CI| SF|DCRG|CC |ABSA |T2S |DST |DT |DS |SP |NLI|Total| |-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-| |34,963 | 368,490 | 135,356 | 196,620 | 33,192 | 5,037 | 36,385 | 104,100 | 390,463 | 262,876 | 17,328 | 30,220 | 298,358 | 60,563 | 27,192 | 31,279 | 169,654 | 2,202,076|