Text Generation

Machine Translation

The use of AI to automatically translate text or speech from one natural language to another.

Question Answering

An NLP task where a system automatically answers questions posed in natural language based on given context.

Chatbot

A software application that simulates human conversation through text or voice interactions using NLP.

Conversational AI

AI systems designed to engage in natural, context-aware dialogue with humans across multiple turns.

Dialogue System

A computer system designed to converse with humans, encompassing task-oriented and open-domain conversation.

Top-K Sampling

A text generation strategy that restricts the model to sampling from the K most probable next tokens.

Text-to-SQL

The task of automatically converting natural language questions into executable SQL queries, enabling non-technical users to interrogate databases through conversational interfaces.

Extractive Summarisation

A summarisation technique that identifies and selects the most important sentences from a source document to compose a condensed version without generating new text.

Intent Detection

The classification of user utterances into predefined categories representing the user's goal or purpose, a fundamental component of conversational AI and chatbot systems.

Dialogue Management

The component of conversational systems that tracks conversation state, determines the next system action, and maintains coherent multi-turn interactions with users.

Dense vector representations of text passages that capture semantic meaning for similarity comparison and retrieval.

AI systems that extract structured information from unstructured documents by combining optical character recognition, layout analysis, and natural language comprehension.

The artificial production of human speech from text, also known as text-to-speech.

A neural network model that learns distributed word representations by predicting surrounding context words.

The application of models trained in one language to perform tasks in another language, leveraging shared multilingual representations learned during pre-training.

Identifying semantic relationships between entities mentioned in text.

Connecting language model outputs to real-world knowledge, facts, or data sources to improve factual accuracy.

Global Vectors for Word Representation — an unsupervised learning algorithm for obtaining word vector representations from aggregated word co-occurrence statistics.