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 )
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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
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.
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order.
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
We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.