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Next-Generation Natural Language Generation

Project description

Neural-symbolic approaches for NLG systems

AI programming can be used to produce text from data. This is called natural language generation (NLG), which transforms complex data into natural-sounding language – as if it were written by a human. The EU-funded NG-NLG project will explore the neural approaches to NLG, which currently remain confined to experimental use. The reason for this is that, despite the very natural outputs of recent neural systems, the behaviour of neural NLG systems is not transparent or reliable. The project will develop innovative approaches that combine neural approaches with explicit symbolic semantic representations, thus allowing greater control over the outputs and explicit logical inferences over the data. The project will test its approaches on data-to-text generation, summarisation and dialogue response generation.

Objective

This project aims to overcome the major hurdles that prevent current state-of-the-art models for natural language generation (NLG) from real-world deployment.

While deep learning and neural networks brought considerable progress in many areas of natural language processing, neural approaches to NLG remain confined to experimental use and production NLG systems are handcrafted. The reason for this is that despite the very natural and fluent outputs of recent neural systems, neural NLG still has major drawbacks: (1) the behavior of the systems is not transparent and hard to control (the internal representation is implicit), which leads to incorrect or even harmful outputs, (2) the models require a lot of training data and processing power do not generalize well, and are mostly English-only. On the other hand, handcrafted models are safe, transparent and fast, but produce less fluent outputs and are expensive to adapt to new languages and domains (topics). As a result, usefulness of NLG models in general is limited. In addition, current methods for automatic evaluation of NLG outputs are unreliable, hampering system development.

The main aims of this project, directly addressing the above drawbacks, are:
1) Develop new approaches for NLG that combine neural approaches with explicit symbolic semantic representations, thus allowing greater control over the outputs and explicit logical inferences over the data.
2) Introduce approaches to model compression and adaptation to make models easily portable across domains and languages.
3) Develop reliable neural-symbolic approaches for evaluation of NLG systems.

We will test our approaches on multiple NLG applications—data-to-text generation (e.g. weather or sports reports), summarization, and dialogue response generation. For example, our approach will make it possible to deploy a new data reporting system for a given domain based on a few dozen example input-output pairs, compared to thousands needed by current methods.

Keywords

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Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-STG

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Host institution

UNIVERZITA KARLOVA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 420 375,00
Address
OVOCNY TRH 560/5
116 36 Praha 1
Czechia

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Region
Česko Praha Hlavní město Praha
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 420 375,00

Beneficiaries (1)

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