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• W2895815747 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" @default.
• W2895815747 created "2018-10-26" @default.
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• W2895815747 creator A5041948053 @default.
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• W2895815747 date "2018-01-01" @default.
• W2895815747 modified "2023-09-27" @default.
• W2895815747 title "A Bi-Encoder LSTM Model for Learning Unstructured Dialogs" @default.
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• W2895815747 hasPublicationYear "2018" @default.
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