If you are new to virtual assistants and AI, it’s important to learn the most popular virtual assistant jargons and terminilogies to get familiar. Here is the complete list of bot glossary.
Jargons are present in every industry to help industry players to communicate with one other efficiently. However, they tend to confuse the outsiders and lead to poor communication. Here is a list of jargons used in the “Virtual Assistant World” and we hope this will help you navigate in the land of virtual assistant/technology like a pro.
| AI | Artificial Intelligence (AI) – is intelligence exhibited by machine. In computer science, an ideal “intelligence” machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. |
| API | Application Programming Interface (API)– the messenger that takes requests and tells a system what you want to do and then returns the response to you |
| Bot amnesia | A bot’s inability to maintain context in a conversation. |
| Bot invocation | UI consideration for how to wake up a bot and initiate a conversation. |
Business Logic | The pre-defined premise to run the business. Your virtual assistant will have to ascertain key facts from customers before providing the right solutions. |
| Context | Virtual Assistant’s understanding of the situation explained by the user. |
Contextual Questions | Questions which refer to something earlier in the conversation and are ambiguous on their own. |
| Conversational Management | Managing complex, multi-intent conversations. |
| Corpus | A collection of written or spoken materials stored about a topic. |
| Dialogue Management | Manage the memory and context in a single conversation and across conversations to conduct natural, human-like back and forth conversation. |
| Entity | Entities are key variables. It is a specific object type that exists separately from other things and has a clear identity. Entity modifies the intent as it provides more specifics. |
| Entity Mapping | Mapping variables we want to collect from the user to create context. |
| Entity Recognition/ Entity Extraction | Identifying entities which exists in the utterances to facilitate more complex commands and analysis |
| Escalation | Distinguish what is important and processing them with higher priority. In some virtual assistants, the questions can also be routed to live agents for complex or tricky situations. |
Explaining Possibilities | AI assistants are always limited to helping users with a specific set of tasks and should be able to tell a user what they can do. That includes coherently responding to requests that are out of scope. |
Explicit Confirmation | The virtual assistant asks the user to clarify how it should help. |
| Fallback | The backup plan executed when the user’s intent does not match existing intents and flows. |
| Flow | The overall dialogue flow following the trigger. |
Handoff | Break out of a conversation and allow a human agent to answer the query. Handoffs are useful when users are getting frustrated at not getting the answers they are looking for or when the virtual assistant is not trained for a complicated task. |
| Humanize | Giving a personality and human touch to the virtual assistant. |
Implicit Confirmation | Implicit confirmation involves repeating details back to the user to reassure them that they were understood correctly. This also gives the user a chance to intervene if your assistant misunderstood. |
| Intent | The user’s intention. What the user actually wants out of the conversation? |
| Intent Mapping | Matching user’s statements (utterances) to the correct intents. |
| Intent Recognition | Identify what the user’s intent, even if phrased unexpectedly. |
| Memory | The virtual assistant’s understanding of historical utterances which aids intent recognition and directs the user to the right dialogue flow. |
| Menu cards | A listing of what the virtual assistant is trained for in the form of buttons and words. This helps to direct users into the right dialogue flow. |
| ML | Machine Learning (ML) – Training a program using data and information available (answers). In virtual assistant terms, virtual assistants learn how to respond to the user by analysing human agent responses. Necessary for qualitative intent recognition |
| NLP | Natural Language Processing (NLP) – The translation of human language to one which computer system can comprehend and vice versa. |
| NLU | Natural Language Understanding (NLU) – Understanding intents and extracting key variables (entities) from a user’s inputs. |
| Onboarding | Greetings and introduction by the Virtual Assistant. |
| Persistent menus | As the user may get lost in the conversation, cancel a conversation, or context-switch to another taste, you will need to think about giving your users a solid understanding of how to navigate the bot conversation. |
| Prediction | The ability to predict the right answer to a question, or an action to take at a particular time in the conversation. |
| POC | Proof-of-concept (POC) – A beta stage of virtual assistant development, where the virtual assistant is functional when its inputs are artificially constrained. |
| Q&A Pairs/ Scripts/ Conversation Scripting | Facts, details or solutions to queries or requests. |
| Reinforcement Learning | Virtual Assistant learns from user “corrections” overtime to improve the suitability of responses. |
| Rich controls | Use of contextual-relevant elements such as buttons, images, emojis |
| Sentiment Analysis | Understand the mood of the conversation. Is the user happy? Upset? |
| Small Talks | Refers to the general category of intents usually used for greetings, acknowledgements, chitchats and insults. A natural conversation begins and ends with greetings like “Hello”, and users make statements which require acknowledgements such as “that’s awesome!”. The virtual assistants will often receive other forms of small talks which are out-of-scope. Queries like “will you marry me?” are not uncommon, and we classify them as chit chat. Angered users may also lash out at the bot with insults. |
Simple Questions | Questions that have fixed questions and requires the same answer regardless of context. |
| Supervised learning | Machine trained using data which are well “labelled” or classified. |
| Triggers | Keyword or status change that starts a series of actions. |
| Turing test | A test for an intelligent computer system. If the end-user is unable to differentiate if a human-agent or a computer program held the conversation, we say the computer system passes the “turing test”. |
| UAT | User Acceptance Test (UAT) – Testing conducted on the beta stage virtual assistantt. Includes real world requirement to uncover areas of improvements. |
| UI | User Interface (UI) – How an end-user and the program interact. |
User feedback | Ask for feedback to understand if the bot was efficient. |
| Utterance | Every statement made by an end-user. |
Unhappy Paths | Users who refuse to provide the required information or wish to correct something they said earlier or interrupts conversational flows with chitchats. |
| Unsupervised learning | Machine learning with data which are not “labelled”. |
| UX | User Experience (UX) – The overall experience of the usage of a program. In particular, how intuitive or easy, or pleasing it is to use. |
