60 papers with code • 19 benchmarks • 18 datasets
Data-to-text generation is the task of generating text from a data source.
( Image credit: Data-to-Text Generation with Content Selection and Planning )
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Ranked #1 on Language Modelling on enwik8 (using extra training data)
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on Data-to-Text Generation on MULTIWOZ 2.1
We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
Ranked #2 on Data-to-Text Generation on ToTTo
It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.
Ranked #4 on Data-to-Text Generation on MULTIWOZ 2.1
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems.
Ranked #4 on Data-to-Text Generation on E2E NLG Challenge
This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets.
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.