Natural Language Generation
Module Description
Course | Module Abbreviation | Credit Points |
---|---|---|
BA-2010[100%|75%] | CS-CL | 6 LP |
BA-2010[50%] | BS-CL | 6 LP |
BA-2010[25%] | BS-AC | 4 LP |
BA-2010 | AS-CL | 8 LP |
Master | SS-CL, SS-TAC | 8 LP |
Lecturer | Anette Frank |
Module Type | |
Language | English |
First Session | 20.04.2021 |
Time and Place | Tuesday, 16:15-17:45, Online |
Commitment Period | tbd. |
Prerequisite for Participation
Assessment
Inhalt
Natural Language Generation (NLG) is a key functionality in many NLP applications. Depending on the type of input for generation, we distinguish data-driven from text-driven language generation. Data-driven NLG aims to verbalize content as captured in knowledge bases or structured linguistic representations, e.g. to communicate search results in database-driven Question Answering, or producing text for structured database records (advertisings, weather or financial reports, etc.). Text-driven NLG is found in text-to-text transduction tasks such as text simplification, summarization or end-to-end dialogue systems.
Further types of input for NLG involve vision, e.g. when generating descriptions of images or answering questions about them, or when modeling situated language as e.g. in robotics, where intelligent systems need to interact with their environment and with humans.
With the advent of Neural Network methods, methods in NLG have been revolutionized through the use of powerful autoregressive networks and pretrained language models (PTLMs). An important research question is, however, how to reconcile the power of autoregressive PTLMs with control of faithfulness to the input for text generation when using them. Novel directions also include non-autoregressive models that take advantage of the inherent parallelism of transformer-based architectures. Finally, in many applications it is important to make such systems interpretable and, ideally, self-explanatory, where again, the NLG capabilities of a system come into play.
In the seminar we will review the fundamentals of NLG and study aspects of NLG that are particularly challenging, i.a.
Module Overview
Agenda
Date | Session | Materials |
Literature
More literature will be provided by the beginning of the term.