Update README.md

README.md

@@ -41,3 +41,128 @@ configs:
   - split: train
     path: data/train-*
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+
+## 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)