mlsanityData to 14 Oct 2026

A Bi-Encoder LSTM Model For Learning Unstructured Dialogs

arXiv 2104.12269 ↗First posted 25 Apr 2021cs.CL cs.AI cs.IRcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 52 references that are in the map. First posted on arXiv 25 Apr 2021.

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7 more
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