| --- |
| 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) |
|
|
