Lexicon

NLP / ML terms

AI stands for Artificial Intelligence. Artificial intelligence is the name given to any computer system taught to mimic intelligent human behaviours.

NLP stands for Natural Language Processing

ML stands for Machine learning. Machine learning enables AI systems to come up with their own solutions, rather than being pre-programmed with a set of answers.

An intent is the user’s intention. In all chatbot platforms, intent more specifically refers to the top-level intent category. For example, if a user types “show me yesterday’s financial news”, the user’s intent is to retrieve a list of financial headlines. Intents are given a name, often a verb and a noun, such as “showNews”.

An entity is also a common term when it comes to conversational agents. Entities are part of an utterance (what the user says). For example, if a user types “show me yesterday’s financial news”, the entities are “yesterday” and “financial”.

Example: “profession” (actual value can be “Developer”, “Tester”, “Sales”) or “location” (actual value might be “Berlin”, “Los Angeles”, “Tahiti”)

A question is a phrase entered by the candidate (end user) to the chatbot. The chatbot tries to find the matching answer (or action) and react accordingly. Each question belongs to a category.

A group of questions around the same topic.

Each category has a generic answer which is supposed to be an adequate answer to all the questions belonging to the given category.

A question can have context-specific answer. Example: the answer to the “What’s the salary?” question can be job position-dependent. Hence, specific contexts can be created, for example for job offers.

A group of questions that besides a generic answer has a set of context dependent answers.

The summary of all the questions, categories, contexts and answers.

A typically continuous process of adding new questions to a dataset, assigning questions to categories and adding answers.

Non category specific generic topics, like saying hello or goodbye.

Review of the current FAQ dataset of the customer based on developed for that purpose jobpal’s tools

Dashboard related terms

Our web based interface to train datasets, manage companies and related bots, and monitor various metrics, used by customers and admins.

A section of our dashboard where dashboard users can monitor metrics about customer / chatbot interactions. Like questions asked and answered, distribution among categories, etc.

Stands for Multi Factor Authentication or Two Factor Authentication. A security protocol that involves additional authentication protocol(s) besides the username/password method. Our dashboard implements 2FA, using Authenticator app as the secondary channel.

Chatbot related terms

Aka “agent”. The artificial conversation partner of our end users (candidates).

The place where the actual conversation takes place between an end user (candidate) and a chatbot. Example: Facebook, Whatsapp, WeChat, WebMessenger, SMS.

A “roadmap” for a chatbot, containing multiple steps, describing a sequence of actions performed by a chatbot.

A sequence of static information displayed by the bot. The end user (candidate) typically has some level of control on the display order (like being able what to be shown next or being able to go “back”). This functionality is used for the “About us” section in a conversation where you can present generic information about your company.

A series of questions in the conversation where the candidate can narrow down the job offers to his/her specific interest. The sequence of questions for a specific chatbot can be decided upon during implementation. Example: the bot shows the available countries first and requests the candidate to select. Then based on the selection, shows the available cities, then the company divisions, job types, etc. As a result, the available jobs are shown in a form of a job carousel.

A set of jobs displayed for the end user (candidate) in a nice visual form. The end user can pick a job and ask for more information or apply for it.

A series of “pre-screening” questions asked by the chatbot after the end user decided to apply for a specific job, it includes generic information like name and email, but job offers can have job offer specific questions as well. The answer to these questions are attached to the candidate’s application.

At the end of the conversation the chatbot might ask (configuration dependent) for feedback regarding the end user’s conversation experience.

A set of predefined answers the bot displays when asking a question. Typically used during the job discovery or job application flow. There are platform-specific limitations regarding the number of quick replies can be used on a question.

Similarly to quick replies buttons are pre-set options an end user can use instead of typing in a free text answers. Typically used in the content discovery flow and in the job carousel. There’s a platform limitation of 3 buttons per question.

Generic terms

Stands for Applicant Tracking System. Typically used by our customers, and connected to our system. We (typically) get a job feed from the customer’s ATS and send the candidates' applications back.

A list of available job positions, fetched from the customer’s ATS.

A person from customer side that is actively training and managing the dataset: deleting, approving and adding questions to the dataset.