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 |