WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi · 2022
A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity.We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans.Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns.Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers.The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets.Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4× larger MultiNLI.Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets.Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process. Seed MultiNLI example Generated WANLI ExampleLabel & Reasoning P: 5 percent probability that each part will be defect free.H: Each part has a 95 percent chance of having a defect.P: 1 percent of the seats were vacant.H: 99 percent of the seats were occupied. EntailmentSet complements P: The artisans, tradespeople, and providers of entertainment (reputable and not so reputable) lived downtown on the reclaimed marshlands north and east, in the area still known as Shitamachi.H: The only place where artisans, tradespeople and entertainers could live was in the marshlands to the north and east.P: At the time of the Revolution, the old port of Marseille was a great center of shipbuilding and commerce.H: The only place where ships were built was in the old port of Marseille. NeutralHypothesis introduces exclusivity P: To the south, in the Sea of Marmara, lie the woods and beaches of the Princes' Islands.H: In the north is the Sea of Marmara where there are mountains to climb.P: From the park's southern entrance, follow the avenue south to the Hotel de Ville.H: From the park's northern entrance, follow the avenue north to the Hotel de Ville. ContradictionReversing cardinal directions P: Democrats released documents indicating that Republicans sold big political donors meals with the party's leaders in federal buildings in 1995.H: It is illegal for a party to solicit products to donors.P: In the late 1960s, students at a university in Wisconsin tried to organize a union.H: It was illegal for the students to organize a union. NeutralIllegal things can happen P: She ducked and parried the blow.H: She ducked to miss the blow.P: She stepped on the brake and the car came to a