Core NLP Tasks: Generation
Summarization, translation, and text generation.
Data-to-Text Generation
Converting structured data (tables, knowledge graphs, database records) into fluent natural language descriptions, bridging the gap between databases and human-readable reports.
Dialogue Systems
Conversational AI systems that interact with users through natural language, ranging from task-oriented slot-filling assistants to open-domain chatbots and modern LLM-based dialogue agents.
Grammatical Error Correction
Detecting and correcting grammatical, spelling, and usage errors in written text, progressing from rule-based checkers through classifier ensembles to neural sequence-to-sequence and LLM-based approaches.
Machine Translation
Automatically converting text from one human language to another, progressing from hand-crafted rules through statistical phrase tables to end-to-end neural models.
Paraphrase Generation
Producing semantically equivalent but syntactically different text, enabling data augmentation, style transfer, and deeper understanding of meaning.
Question Answering
Systems that find or generate answers to natural language questions from given context, retrieved documents, or parametric knowledge.
Text Generation
Producing fluent, coherent text from a language model using decoding strategies that balance quality, diversity, and controllability.
Text Summarization
Condensing documents while preserving key information, using either extractive methods that select important sentences or abstractive methods that generate new condensed text.