Rnn is capable of digesting variable-sized text and respond accordingly. Classical rnns have a “vanishing memory” problem. Which more advanced versions such as lstm tend to solve. However, a single algorithm is not enough for a successful conversation. A sentence can include multiple intents, or the user may be referencing to an earlier conversation—simple and trivial things. For human conversations. But challenging tasks for ai. We must also consider that written. Conversations can include incorrect words or typos—again. Easy to detect for human eyes but damaging for. Keyword-based algorithms.
To correctly deal with these scenarios
An ai must run filters on the received. Text to detect any typos or incorrect words. And try to guess the correct meaning, then pass the corrected sentence to the next layer. In the next stage, the sentences must be broken down to detect the intent(s) of the user. Besides the sentence Kazakhstan B2B List the ai must receive several other information as well, such as information about the user, a history of the. Previous conversations and information. About the system the bot is working with. Having that additional data at hand makes the ai more responsive, as it allows the. Bot to personalize the conversation and. Remember and understand the context of the conversation.
A smart algorithm must also
Be able to let the user “switch intents,” I.E. When users stop abruptly at the middle of a transaction conversation and instead ask to check their balance, the bot should be able to freeze the conversation, look up the. Balance of the user it has been talking to (I.E. Remembering Fjlists the person from the previous intent) and after showing the results. Return to the initial intents. Besides intent detection, sentiment analysis is also extremely important. Sentiment analysis is a categorization algorithm. Which can determine the mood. Of the users based on their conversation.
