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- # retrieval_based_chatbot
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- A retrieval-based chatbot
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Retrieval-based learning chatbot
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+ CSC525 - Module 8 Option 2 - Retrieval-based Learning Chatbot - Joseph Armani
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+
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+ ## TODO
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+ A Python tool to generate high-quality dialog variations.
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+ This package automatically downloads the following models during installation:
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+
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+ - Universal Sentence Encoder v4 (TensorFlow Hub)
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+ - ChatGPT Paraphraser T5-base
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+ - Helsinki-NLP translation models (en-de, de-es, es-en)
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+ - GPT-2 (for perplexity scoring)
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+ - spaCy en_core_web_sm
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+ - nltk wordnet and averaged_perceptron_tagger_eng models
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+
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+ ## Install package
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+ pip install -e .
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+
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+ ## Description
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+ This Python script demonstrates a complete pipeline for dialogue augmentation, including validation, optimization, and data augmentation.
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+ It creates high-quality augmented versions of dialogues by applying various text augmentation techniques and quality control checks.
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+ Two approaches are used for text augmentation: paraphrasing and back-translation. The pipeline also includes quality metrics for evaluating the augmented text.
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+ Special handling is implemented for very short text such as greetings and farewells, which are predefined and filtered for quality.
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+ The pipeline is designed to process a dataset of dialogues and generate multiple high-quality augmented versions of each dialogue.
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+ The pipeline ensures duplicate dialogues are not generated and that the output meets quality thresholds for semantic similarity, grammar, fluency, diversity, and content preservation.
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+
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+ ## References
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+
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+ Accsany, P. (2024). Working with JSON data in Python. Real Python. <https://realpython.com/python-json/>
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+ Explosion AI Team. (n.d.). Spacy · industrial-strength natural language processing in python. <https://spacy.io/>
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+ GeeksforGeeks. (2024). Text augmentation techniques in NLP. GeeksforGeeks. <https://www.geeksforgeeks.org/text-augmentation-techniques-in-nlp/>
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+ Helsinki-NLP. (2024). Opus-MT [Computer software]. GitHub. <https://github.com/Helsinki-NLP/Opus-MT>
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+ Hugging Face. (n.d.). Transformers. Hugging Face. <https://huggingface.co/docs/transformers/en/index>
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+ Humarin. (2023). ChatGPT paraphraser on T5-base [Computer software]. Hugging Face. <https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base>
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+ Keita, Z. (2022). Data augmentation in NLP using back-translation with MarianMT. Towards Data Science. <https://towardsdatascience.com/data-augmentation-in-nlp-using-back-translation-with-marianmt-a8939dfea50a>
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+ Memgraph. (2023). Cosine similarity in Python with scikit-learn. Memgraph. <https://memgraph.com/blog/cosine-similarity-python-scikit-learn>
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+ Morris, J. (n.d.). language-tool-python (Version 2.8.1) [Computer software]. PyPI. <https://pypi.org/project/language-tool-python/>
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+ TensorFlow. (n.d.). Universal sentence encoder. TensorFlow Hub. <https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder>
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+ Waheed, A. (2023). How to calculate ROUGE score in Python. Python Code. <https://thepythoncode.com/article/calculate-rouge-score-in-python>