| | --- |
| | dataset_info: |
| | features: |
| | - name: dialog_id |
| | dtype: string |
| | - name: turns |
| | list: |
| | - name: bigram_overlap_prev |
| | dtype: float64 |
| | - name: context_embedding |
| | list: float64 |
| | - name: intent_label |
| | dtype: string |
| | - name: is_user |
| | dtype: int64 |
| | - name: length_bucket |
| | dtype: string |
| | - name: nb_response_candidates |
| | list: string |
| | - name: readability |
| | dtype: float64 |
| | - name: readability_score |
| | dtype: float64 |
| | - name: role_embedding |
| | list: int64 |
| | - name: sentiment_polarity |
| | dtype: float64 |
| | - name: speaker |
| | dtype: string |
| | - name: text |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 515339977 |
| | num_examples: 13215 |
| | download_size: 458215847 |
| | dataset_size: 515339977 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | --- |
| | |
| | ## Taskmaster-1 Enriched Dialog Dataset (Combined) |
| | ## Overview |
| |
|
| | This dataset is a combined, enriched version of the self_dialog and woz_dialog splits from the Taskmaster-1 dataset. It consists of multi-turn, human-human and human-simulated conversations with systematic enhancements for machine learning workflows—especially dialog modeling, generation, and fine-grained evaluation. |
| |
|
| | All conversations are structured in a JSON format with consistent schema and include added semantic, linguistic, and behavioral annotations. |
| |
|
| | ## Enrichments Included |
| | 1. Role Embedding |
| | |
| | Each turn includes a binary role embedding: |
| |
|
| | [1, 0] for USER |
| |
|
| | [0, 1] for ASSISTANT |
| |
|
| | This makes it easier for sequence models to learn speaker turns without relying on string labels. |
| |
|
| | Use case: Improves model performance in transformer-based dialog agents by allowing role-aware generation and classification. |
| |
|
| |
|
| | 2. Response Candidates |
| | |
| | Each user turn is enriched with nb_response_candidates — 2 to 4 plausible assistant responses sampled from the dataset. These are not ground truth but plausible continuations. |
| |
|
| | Use case: Ideal for retrieval-based dialog training or negative sampling in response ranking tasks. |
| |
|
| | 3. Readability Score |
| | |
| | Computed using Flesch-Kincaid metrics and other NLP readability formulas. Stored as readability (0–100 scale, higher = easier). |
| |
|
| | Use case: Enables analysis of language complexity and training adaptive LLMs for education, accessibility, or voice interfaces. |
| |
|
| | 4. Readability Grade Score |
| | |
| | Stored as readability_score on a U.S. grade level (lower = easier to read). Especially relevant for UX tuning. |
| | |
| | Use case: Allows controlling reading level in generation tasks or selecting user-appropriate training samples. |
| | |
| | 5. Context Embedding |
| | |
| | Each turn is augmented with a context_embedding vector (384-dim, Sentence-BERT). Represents the semantic context of the turn. |
| |
|
| | Use case: Enables plug-and-play use with FAISS-based semantic search, response reranking, and memory-augmented generation. |
| |
|
| | 6. Speaker Role Flags |
| | |
| | An is_user flag is included for each turn (1 = user, 0 = assistant). |
| | |
| | Use case: Simplifies filtering, evaluation, or role-specific metric computation. |
| | |
| | 7. Utterance Length Bucketing |
| | |
| | Each turn is labeled as: |
| | |
| | short (<= 5 tokens) |
| | |
| | medium (6–15 tokens) |
| | |
| | long (> 15 tokens) |
| | |
| | Use case: Enables sampling, curriculum learning, or model analysis across turn complexity. |
| | |
| | 8. Bigram Overlap with Previous Turn |
| | |
| | Computed as bigram_overlap_prev (float between 0 and 1). Measures lexical repetition with the preceding utterance. |
| | |
| | Use case: Useful for: |
| | |
| | Dialogue coherence metrics |
| | |
| | Detecting stagnation or repetition in generated responses |
| | |
| | Analyzing repair-based utterances |
| | |
| | 9. Sentiment Polarity |
| | |
| | Computed using a sentiment analyzer. Stored as sentiment_polarity: |
| |
|
| | Ranges from –1 (strongly negative) to +1 (strongly positive) |
| |
|
| | Use case: Enables emotion-aware generation, tone control, or training sentiment-conditioned agents. |
| |
|
| | 10. Format Summary |
| | |
| | Each conversation has: |
| | |
| | dialog_id: Unique identifier |
| | |
| | turns: List of enriched utterances |
| | |
| | Each turn includes: |
| | |
| | { "speaker": "USER", "text": "I’d like to book a table for 2", "role_embedding": [1, 0], "intent_label": "request", "nb_response_candidates": [...], "readability_score": 4.5, "context_embedding": [...], "readability": 85.6, "is_user": 1, "length_bucket": "medium", "bigram_overlap_prev": 0.2, "sentiment_polarity": 0.1 } |
| |
|
| | ## Suggested Use Cases |
| |
|
| | Fine-tuning LLMs for goal-oriented dialog |
| |
|
| | Training dialog state trackers and response rankers |
| |
|
| | Evaluating model outputs with context-aware metrics |
| |
|
| | Curriculum learning based on length or readability |
| |
|
| | Emotion- and intent-conditioned dialog modeling |
| |
|
| | Semantic retrieval and reranking systems |
| |
|
| | ## Citation |
| |
|
| | @inproceedings{48484, |
| | title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, |
| | author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, |
| | year = {2019} |
| | } |
| |
|
| | ## Taskmaster-1: Towards a Realistic Goal-Oriented Dialogue Dataset (Google-Research-Datasets) |
| |
|
| | ## Original base dataset: @patil-suraj (Original contributor) |
| |
|
| | ## Enrichments and combined version by: GenAIDevTOProd (Adithya) |
| |
|
| | ## License: Same as Taskmaster-1 (if public domain or open license) |
| |
|
