Improve category taxonomy and quality flags

README.md

@@ -32,11 +32,15 @@ This version adds real task-oriented multi-turn dialogue data from MultiWOZ 2.2
 - Total rows: 30000
 - Train rows: 24215
 - Eval rows: 5785
-- High-quality train rows: 15283
-- High-quality eval rows: 3669
+- High-quality train rows: 20653
+- High-quality eval rows: 4899
 - Average input turns: 9.706
 - Average streaming chunks: 9.706
 - Source distribution: {"DailyDialog": 10000, "MultiWOZ": 10000, "Taskmaster": 10000}
+- Domain category distribution: {"customer_service": 204, "education_career": 2287, "finance_business": 180, "food_dining": 1263, "general_daily_life": 2879, "health_wellness": 220, "home_services": 227, "hospitality_lodging": 404, "personal_schedule": 683, "shopping_retail": 530, "social_relationship": 1986, "technology_support": 195, "travel_transportation": 18942}
+- Intent category distribution: {"booking_or_reservation": 15759, "confirmation_clarification": 2710, "customer_support": 1241, "emotional_support": 232, "information_request": 6236, "instruction_following": 26, "negotiation_decision": 139, "planning_coordination": 1461, "problem_solving": 445, "recommendation": 739, "small_talk": 1012}
+- Scenario category distribution: {"attraction_search": 455, "banking_support": 212, "customer_complaint": 218, "family_conversation": 603, "flight_booking": 9926, "food_ordering": 18, "friend_conversation": 459, "general_conversation": 3288, "home_repair": 97, "hotel_booking": 1557, "hotel_search": 184, "insurance_support": 95, "job_interview": 104, "medical_assistance": 290, "movie_ticketing": 218, "music_search": 141, "restaurant_booking": 1890, "restaurant_search": 819, "schedule_planning": 642, "school_life": 854, "shopping_assistance": 395, "taxi_booking": 1106, "technical_support": 460, "train_booking": 4317, "travel_planning": 252, "workplace_conversation": 1400}
+- Unknown/other taxonomy ratio: 0.0
 - Category distribution: {"daily_dialogue": 10000, "task_oriented_dialogue": 20000}
 
 ## Sources
@@ -49,13 +53,25 @@ This version adds real task-oriented multi-turn dialogue data from MultiWOZ 2.2
 
 Rows contain `id`, `source_dataset`, `source_id`, `dialogue_id`, `domain`, `task_type`, `dialogue_history`, `streaming_chunks`, `deep_reasoning`, `answer`, `metadata`, `quality_flags`, `quality_score`, `is_high_quality`, and `split`. The final schema remains unified across sources; source-specific details such as source, category, domain/services, scenario, original split, and raw file are kept in `metadata`.
 
+## Category Taxonomy
+
+Each sample includes a coarse `metadata.category` and three additional taxonomy fields:
+
+- `metadata.domain_category`: broad topic/domain such as travel, dining, lodging, entertainment, health, education, work, shopping, or general daily life.
+- `metadata.intent_category`: interaction intent such as information request, recommendation, booking, planning, customer support, small talk, or emotional support.
+- `metadata.scenario_category`: more specific scenario such as restaurant booking, hotel search, taxi booking, train booking, food ordering, movie ticketing, schedule planning, or workplace conversation.
+
+The taxonomy is deterministic and source-aware. MultiWOZ uses domain/service annotations, Taskmaster uses scenario/file metadata when available, and DailyDialog uses lightweight keyword and dialogue-pattern rules.
+
 ## Reasoning
 
 Streaming reasoning is generated by deterministic rule-based state tracking over turn-level chunks. DailyDialog rows focus on daily intent, tone, and continuity. MultiWOZ and Taskmaster rows use task-oriented templates for goal, known constraints, missing information, scenario/domain, and next-step policy. Deep reasoning is a compact global summary from the final tracked state, dialogue history, and target answer. The answer is not rewritten by default; it comes from the original next assistant turn.
 
-## Quality
+## Quality Filtering
+
+The quality checks are category-aware. Long task-oriented conversations are no longer penalized in the same way as short daily dialogues. Some heuristic checks, including weak final state and premature response detection, are kept as diagnostic warnings rather than hard filters.
 
-`quality_flags` and `metadata.quality_checks` support filtering by real-source status, multi-turn context, non-empty reasoning, placeholder detection, length checks, and role alternation. Raw external data is not committed to git; processed train/eval files are intended for upload to `skyzhou06/LifeMultiTurnStreamingCoT`.
+`quality_flags` and `metadata.quality_checks` support filtering by real-source status, multi-turn context, non-empty reasoning, placeholder detection, category-aware length checks, malformed-row checks, repetition checks, and role alternation. Raw external data is not committed to git; processed train/eval files are intended for upload to `skyzhou06/LifeMultiTurnStreamingCoT`.
 
 ## Leakage Control
 

dataset_info.json

@@ -6,6 +6,21 @@
     "task_oriented_dialogue": 20000
   },
   "dataset_name": "LifeMultiTurnStreamingCoT",
+  "domain_category_distribution": {
+    "customer_service": 204,
+    "education_career": 2287,
+    "finance_business": 180,
+    "food_dining": 1263,
+    "general_daily_life": 2879,
+    "health_wellness": 220,
+    "home_services": 227,
+    "hospitality_lodging": 404,
+    "personal_schedule": 683,
+    "shopping_retail": 530,
+    "social_relationship": 1986,
+    "technology_support": 195,
+    "travel_transportation": 18942
+  },
   "domain_distribution": {
     "cooking": 1027,
     "customer_service": 192,
@@ -23,19 +38,64 @@
     "travel": 18532
   },
   "eval_rows": 5785,
-  "high_quality_eval_rows": 3669,
-  "high_quality_train_rows": 15283,
+  "high_quality_eval_rows": 4899,
+  "high_quality_percentage": 0.8517,
+  "high_quality_rows": 25552,
+  "high_quality_train_rows": 20653,
+  "intent_category_distribution": {
+    "booking_or_reservation": 15759,
+    "confirmation_clarification": 2710,
+    "customer_support": 1241,
+    "emotional_support": 232,
+    "information_request": 6236,
+    "instruction_following": 26,
+    "negotiation_decision": 139,
+    "planning_coordination": 1461,
+    "problem_solving": 445,
+    "recommendation": 739,
+    "small_talk": 1012
+  },
   "quality_flag_distribution": {
     "answer_not_grounded": 1292,
+    "deep_reasoning_too_long": 1,
+    "excessive_repetition": 442,
     "generic_answer": 333,
     "premature_respond": 4690,
-    "repeated_turns": 4260,
+    "repeated_turns": 442,
     "target_leakage": 176,
-    "too_many_turns": 23590,
-    "too_short_average_turn": 10026,
+    "too_many_turns": 262,
+    "too_short_average_turn": 8,
     "weak_final_state": 8627,
     "weak_target_answer": 2756
   },
+  "scenario_category_distribution": {
+    "attraction_search": 455,
+    "banking_support": 212,
+    "customer_complaint": 218,
+    "family_conversation": 603,
+    "flight_booking": 9926,
+    "food_ordering": 18,
+    "friend_conversation": 459,
+    "general_conversation": 3288,
+    "home_repair": 97,
+    "hotel_booking": 1557,
+    "hotel_search": 184,
+    "insurance_support": 95,
+    "job_interview": 104,
+    "medical_assistance": 290,
+    "movie_ticketing": 218,
+    "music_search": 141,
+    "restaurant_booking": 1890,
+    "restaurant_search": 819,
+    "schedule_planning": 642,
+    "school_life": 854,
+    "shopping_assistance": 395,
+    "taxi_booking": 1106,
+    "technical_support": 460,
+    "train_booking": 4317,
+    "travel_planning": 252,
+    "workplace_conversation": 1400
+  },
   "source_distribution": {
     "DailyDialog": 10000,
     "MultiWOZ": 10000,
@@ -43,5 +103,6 @@
   },
   "total_rows": 30000,
   "train_rows": 24215,
+  "unknown_other_ratio": 0.0,
   "version": "v0.1"
 }

eval.jsonl

@@ -1,3 +1,3 @@
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+size 82708681

train.jsonl

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