This paper provides a comprehensive analysis of the first shared task on End-to-End Natural
Language Generation (NLG) and identifies avenues for future research based on the results …

Existing neural methods for data-to-text generation are still struggling to produce long and
diverse texts: they are insufficient to model input data dynamically during generation, to …

Controlled table-to-text generation seeks to generate natural language descriptions for
highlighted subparts of a table. Previous SOTA systems still employ a sequence-to …

In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent with the
aim of completing a concrete task. Although this technology represents one of the central …

We introduce SciGen, a new challenge dataset consisting of tables from scientific articles
and their corresponding descriptions, for the task of reasoning-aware data-to-text …

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-
linguistic input data, such as tables and attribute–value pairs, but overlook that different …

Due to limited knowledge carried by queries, traditional dialogue systems often face the
dilemma of generating boring responses, leading to poor user experience. To alleviate this …

Table-to-text generation aims at automatically generating natural text to help people
conveniently obtain salient information in tables. Although neural models for table-to-text …

Recent neural models for data-to-text generation rely on massive parallel pairs of data and
text to learn the writing knowledge. They often assume that writing knowledge can be …

Generating fluent, coherent, and informative text from structured data is called table-to-text
generation. Copying words from the table is a common method to solve the “out-of …