Data-to-text Generation (D2T) aims to generate textual natural language statements that can
fluently and precisely describe the structured data such as graphs, tables, and meaning …

We present BRIDGE, a powerful sequential architecture for modeling dependencies
between natural language questions and relational databases in cross-DB semantic …

In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …

Text generation from a knowledge base aims to translate knowledge triples to natural
language descriptions. Most existing methods ignore the faithfulness between a generated …

Abstract Data-to-Text Generation (DTG) is a subfield of Natural Language Generation
aiming at transcribing structured data in natural language descriptions. The field has been …

The neural boom that has sparked natural language processing (NLP) research throughout
the last decade has similarly led to significant innovations in data-to-text (D2T) generation …

Natural language interfaces to databases (NLIDB) democratize end user access to relational
data. Due to fundamental differences between natural language communication and …

In open domain table-to-text generation, we notice the unfaithful generation usually contains
hallucinated entities which can not be aligned to any input table record. We thus try to …

The ubiquitous and layout-friendly PDF documents have multiple elements for disseminating
knowledge and require visual cues for interpretation. Users with visual impairments depend …