Update README.md

README.md

@@ -25,4 +25,182 @@ configs:
     path: data/train-*
   - split: validation
     path: data/validation-*
+license: mit
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- state-tracking
+- textworld
+- language-models
+- evaluation
+size_categories:
+- 10K<n<100K
 ---
+
+# TextWorld State Tracking Dataset
+
+## Dataset Description
+
+This dataset contains **27,145 examples** for evaluating language models' ability to track object states across narrative contexts. The data is derived from [TextWorld](https://github.com/microsoft/TextWorld), an interactive text-based game environment, and formatted for state tracking evaluation.
+
+### Dataset Summary
+
+Each example presents a narrative context (a sequence of game observations and actions) along with:
+- An **entity** (object) being tracked
+- Its **positive_state** (the true current state)
+- A **negative_state** (a mutually exclusive contrastive state)
+
+The task is to determine which state correctly describes the entity given the context.
+
+**Key Statistics:**
+- **Total examples:** 27,145
+  - Train: 24,076 examples
+  - Dev: 3,069 examples
+- **Tracked properties:** 3 (open, closed, eaten)
+- **Average context length:** ~2,850 words (median: 2,064 words)
+- **Context length range:** 53 - 12,855 words (25th-75th percentile: 950 - 4,180 words)
+- **Unique entities:** 13 distinct objects tracked
+- **Entities tracked:** Various objects from TextWorld environments (doors, containers, food items, etc.)
+
+### Supported Tasks
+
+1. **Binary State Classification:** Given context and entity, predict which of two states is correct
+2. **State Tracking:** Track state changes of entities across narrative sequences
+3. **Reading Comprehension:** Understanding implicit and explicit state information from text
+
+### Languages
+
+The dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance contains:
+
+```json
+{
+  "context": "Text describing the game state and actions taken",
+  "entity": "Name of the object being tracked",
+  "positive_state": "The correct current state (e.g., 'closed', 'eaten', 'open')",
+  "negative_state": "A contrastive incorrect state (e.g., 'open', 'not eaten', 'closed')"
+}
+```
+
+**Example:**
+
+```json
+{
+  "context": "-= Bedroom =-\nYou see a closed chest drawer. You see an antique trunk...\n> open chest drawer\nYou open the chest drawer.",
+  "entity": "chest drawer",
+  "positive_state": "open",
+  "negative_state": "closed"
+}
+```
+
+### Data Fields
+
+- `context` (string): Progressive narrative showing the game state, observations, and actions taken. Includes room descriptions and action-response pairs.
+- `entity` (string): The specific object whose state is being tracked (e.g., "wooden door", "apple", "refrigerator")
+- `positive_state` (string): The ground truth current state of the entity. One of: "open", "closed", or "eaten"
+- `negative_state` (string): A mutually exclusive state that does NOT describe the entity. One of: "closed", "open", or "not eaten"
+
+### Data Splits
+
+|             | Train  | Dev   |
+|-------------|--------|-------|
+| **Examples**| 24,076 | 3,069 |
+
+The splits are based on distinct TextWorld game traces, ensuring no overlap in source trajectories.
+
+## Dataset Creation
+
+### Source Data
+
+The dataset is derived from TextWorld game traces generated using the TextWorld framework. TextWorld creates procedurally generated text-based games with consistent world states.
+
+#### Initial Data Collection
+
+1. **Game Generation:** TextWorld environments were generated with various objects and properties
+2. **Trace Collection:** Agent trajectories were collected, capturing sequences of actions and observations
+3. **State Extraction:** Ground truth entity states were extracted from the game engine's internal state
+
+#### Data Processing
+
+The raw TextWorld traces underwent several processing steps:
+
+1. **State Annotation:** Each game step was annotated with the current states of all entities
+2. **Context Assembly:** Progressive contexts were built by accumulating observations and actions
+3. **Quality Filtering:**
+   - Minimum context length: 50 words (ensures non-trivial examples)
+   - Entity mention verification: Entity must be mentioned in context
+   - Property mention verification: State must be mentioned in context
+   - State balance filtering: Entities must appear with multiple states (prevents trivial learning)
+
+4. **Contrastive Pair Creation:** For each entity-state pair, a mutually exclusive contrastive state was assigned
+
+### Annotations
+
+#### Annotation process
+
+Annotations are automatically extracted from TextWorld's game engine, which maintains perfect ground truth about entity states. No human annotation was required.
+
+#### Who are the annotators?
+
+N/A - Automatically generated from game engine state.
+
+### Personal and Sensitive Information
+
+The dataset contains only synthetic text from procedurally generated games. No personal or sensitive information is present.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This is a synthetic dataset for research purposes, with no direct social impact concerns.
+
+### Discussion of Biases
+
+The dataset reflects the structure and vocabulary of TextWorld environments:
+- Limited to household/indoor settings
+- Stereotypical room layouts and object placements
+- Formulaic language patterns from the text generation templates
+
+### Other Known Limitations
+
+1. **Limited Property Types:** Only tracks 3 property types (open/closed/eaten)
+2. **Limited Entity Diversity:** Only 13 unique entities across all 27K examples
+3. **Synthetic Language:** Generated text has repetitive patterns and is not representative of natural language
+4. **Domain-Specific:** Focused on household objects and simple spatial relations
+5. **State Simplicity:** Real-world states are more complex and nuanced
+6. **Long Contexts:** Average of ~2,850 words may exceed context windows of smaller models
+7. **Context Redundancy:** Progressive contexts include many repeated observations and may allow shortcuts without full state tracking
+
+## Additional Information
+
+### Dataset Curators
+
+Created as part of research on language model state tracking capabilities.
+
+### Licensing Information
+
+MIT License
+
+### Citation Information
+
+```bibtex
+@dataset{textworld_state_tracking_2024,
+  title={TextWorld State Tracking Dataset},
+  author={Anonymous},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/YOUR_USERNAME/textworld-state-tracking}}
+}
+```
+
+### Contributions
+
+Built using the [TextWorld](https://github.com/microsoft/TextWorld) framework by Microsoft Research.