DART: Open-Domain Structured Data Record to Text Generation

By Dragomir R. Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chia-Hsuan Hsieh, Nazneen Rajani, Xiangru Tang, Aadit Vyas, Neha Verma, P. Krishna and 12 more. 2020

230 citations recorded by the source corpus.

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, neutral): “DART (Nan et al., 2021) 3.”
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization (2022, methodology): “To evaluate these capabilities, we compare OPT-IML models with baseline OPT on three UnifiedSKG tasks formatted as text-to-text: DART (Nan et al., 2020), which is a held-out datato-text task for transforming data triples to text, Spider (Yu et al., 2018), a SQL query generation task given a database”
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021, neutral): “– GEM (en): Wiki-Lingua (Faisal Ladhak & McKeown, 2020), WenNLG (Gardent et al., 2017; Castro Ferreira et al., 2020), CommonGEN (Lin et al., 2020), E2E (Dušek et al., 2019), DART (Radev et al., 2020), ToTTo (Parikh et al., 2020), Wiki-Auto (Jiang et al., 2020), TurkCorpus(Xu et al., 2016) – DialoGL”
UL2: Unifying Language Learning Paradigms (2022, background): “We use several component tasks from UnifiedSKG (Xie et al., 2022), namely WikiTQ (Pasupat & Liang, 2015), CompWQ (Talmor & Berant, 2018), FetaQA (Nan et al., 2021), HybridQA (Chen et al., 2020), WikiSQL (Zhong et al., 2017), TabFat (Chen et al., 2019), Feverous (Aly et al., 2021), SQA (Iyyer et al.,”
HTLM: Hyper-Text Pre-Training and Prompting of Language Models (2021, background): “DART (Nan et al., 2020) is a open-domain structured generation dataset containing Wikipedia tables.”
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models (2022, methodology): “Moreover, on E2E, DART and ONR tasks (shown in Table 3), input-tuning achieves comparable or even better performance than ﬁne-tuning.”
Large Language Models Can Be Strong Differentially Private Learners (2021, methodology): “For table-to-text generation tasks such as E2E (Novikova et al., 2017) and DART (Nan et al., 2020), each example/record in the training data consists of a pair of table entry and corresponding text description to be predicted.”
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, background): “Avoiding catastrophic forgetting while maintaining the topological structure of the graph, the model achieves the SOTA performance on two AMR-to-text benchmarks.”
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing (2020, background): “…data understanding problems, such as table semantic parsing (Zhong et al., 2017; Yu et al., 2018b), question answering (Pasupat & Liang, 2015; Chen et al., 2020), retrieval (Zhang et al., 2019a), fact-checking (Chen et al., 2019) and summarization (Parikh et al., 2020; Radev et al., 2020).”
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021, background): “, 2020) and DART (Radev et al., 2020) to generate text given a table and relevant highlighted cells.”
Large Language Models are few(1)-shot Table Reasoners (2022, background): “Thus, we follow Nan et al. (2021) to do human evaluation over the four aspects: (1) ﬂuency (whether the generated sentence contains the linguistic error), (2) correctness (whether the generated sentence answers the question correctly), (3) faithfulness (whether the generated sentence is grounded on…”