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- W4381786080 abstract "The conversational chat model here is based on the RASA framework. This framework is used as the base to design the chatbot. The topical Chat dataset used here contains approximately 210,000 utterances, which is more than 4,100,000 words. The chatbot analyzed this chat dataset and revealed how individuals interact with one another while discussing various topics. The TED (Transformer Embedding Dialogue) policy is used by Rasa to ensure that conversations are meaningful and are able to stick to the original intent of placing an order. The three inputs and all the features were fed into the transformer. Then, we built a model so that we can train it with the data and then can be used for the prediction and identify improvement opportunities. We simulated ASR hypotheses for each turn in a conversation using four different settings. System A had a Word Error Rate (WER) of 0.01–0.39 for the dialogues in the test freq audio and test-uncommon audio, whereas system B had an error rate of about 0.08. We trained our Rasa-based model with topical chat data. Topical chat data includes various conversations on a specific topic in the json files. After training the bot we can ask various questions about around 250 entities and the bot will share fun facts about those entities. The report, confusion matrix, and confidence histogram for your intent classification model were then created by the Rasa test script. Any samples that were inaccurately predicted are recorded and preserved in an errors file." @default.
- W4381786080 created "2023-06-24" @default.
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- W4381786080 date "2023-01-01" @default.
- W4381786080 modified "2023-10-18" @default.
- W4381786080 title "Conversational AI: A Study on Capabilities and Limitations of Dialogue Based System" @default.
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- W4381786080 doi "https://doi.org/10.1007/978-3-031-36402-0_47" @default.
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