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Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, K. McKeown, Doug Downey, Yejin ChoiarXiv • 2022 Generics express generalizations about the world (e.g., “birds can fly"). However, they are not universally true – while sparrows and penguins are both birds, only sparrows can fly and penguins cannot. Commonsense knowledge bases, that are used extensively in…ABC: Attention with Bounded-memory Control
Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, Noah A. SmithACL • 2022 Transformer architectures have achieved state-of-the-art results on a variety of sequence modeling tasks. However, their attention mechanism comes with a quadratic complexity in sequence lengths, making the computational overhead prohibitive, especially for…Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hanna HajishirziACL • 2022 Can we enable NLP models to appropriately respond to instructional prompts and consequently generalize to new tasks? To study this question, we leverage the existing NLP datasets and the instructions that were used to crowdsource them to create…Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language
Royi Lachmy, Valentina Pyatkin, Reut TsarfatyACL • 2022 Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally evoke abstract constructs such as objects, loops, conditions…Extracting Latent Steering Vectors from Pretrained Language Models
Nishant Subramani, Nivedita Suresh, Matthew E. PetersFindings of ACL • 2022 Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We hypothesize that the information needed to steer the model to…Generated Knowledge Prompting for Commonsense Reasoning
Jiachen Liu, Alisa Liu, Ximing Lu, S. Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh HajishirziACL • 2022 Despite their ability to capture large amount of knowledge during pretraining, large-scale language models often benefit from incorporating external knowledge bases, especially on commonsense reasoning tasks. This motivates us to explore how we can best…Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp, Pradeep DasigiACL • 2022 Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on, while not generalising to different task distributions. We…Generating Scientific Definitions with Controllable Complexity
Tal August, Katharina Reinecke, Noah A. SmithACL • 2022 Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms. We introduce a new task and dataset for defining scientific…Generating Scientific Claims for Zero-Shot Scientific Fact Checking
Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Lu WangACL • 2022 Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain expertise. To address this challenge, we propose scientific claim generation, the…Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish SabharwalFindings of ACL • 2022 Humans often solve complex problems by interacting (in natural language) with existing agents, such as AI assistants, that can solve simpler sub-tasks. These agents themselves can be powerful systems built using extensive resources and privately held data. In…