A Bi-Encoder LSTM Model For Learning Unstructured Dialogs

• URL: http://arxiv.org/abs/2104.12269v1
• Date: Sun, 25 Apr 2021 21:37:35 GMT
• Title: A Bi-Encoder LSTM Model For Learning Unstructured Dialogs
• Abstract summary: This paper presents a Long Short Term Memory (LSTM) based architecture that learns unstructured multi-turn dialogs. We show that our model achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and Recall@5 respectively than the benchmark model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Creating a data-driven model that is trained on a large dataset of unstructured dialogs is a crucial step in developing Retrieval-based Chatbot systems. This paper presents a Long Short Term Memory (LSTM) based architecture that learns unstructured multi-turn dialogs and provides results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog Corpus Version 2 was used as the corpus for training. We show that our model achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and Recall@5 respectively than the benchmark model. We also show results on experiments performed by using several similarity functions, model hyper-parameters and word embeddings on the proposed architecture

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