Papers
See AI2's Award Winning Papers
Learn more about AI2's Lasting Impact Award
Viewing 161-170 of 292 papers
Competency Problems: On Finding and Removing Artifacts in Language Data
Matt Gardner, William Cooper Merrill, Jesse Dodge, Matthew E. Peters, Alexis Ross, Sameer Singh, Noah A. SmithEMNLP • 2021 Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this…Expected Validation Performance and Estimation of a Random Variable's Maximum
Jesse Dodge, Suchin Gururangan, D. Card, Roy Schwartz, Noah A. SmithFindings of EMNLP • 2021 Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a…Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah A. SmithICLR • 2021 State-of-the-art neural machine translation models generate outputs autoregressively, where every step conditions on the previously generated tokens. This sequential nature causes inherent decoding latency. Non-autoregressive translation techniques, on the…Random Feature Attention
Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A. Smith, Lingpeng KongICLR • 2021 Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep. While attention is powerful, it does not scale efficiently to…All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, Noah A. SmithACL • 2021 Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between…Effective Attention Sheds Light On Interpretability
Kaiser Sun and Ana MarasovićFindings of ACL • 2021 An attention matrix of a transformer selfattention sublayer can provably be decomposed into two components and only one of them (effective attention) contributes to the model output. This leads us to ask whether visualizing effective attention gives different…Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Alexis Ross, Ana Marasović, Matthew E. PetersFindings of ACL • 2021 Humans give contrastive explanations that explain why an observed event happened rather than some other counterfactual event (the contrast case). Despite the important role that contrastivity plays in how people generate and evaluate explanations, this…Explaining Relationships Between Scientific Documents
Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, Noah A. SmitACL • 2021 We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing that…PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
Rowan Zellers, Ari Holtzman, Matthew E. Peters, R. Mottaghi, Aniruddha Kembhavi, A. Farhadi, Yejin ChoiACL • 2021 We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just…Promoting Graph Awareness in Linearized Graph-to-Text Generation
Alexander M. Hoyle, Ana Marasović, Noah A. SmithFindings of ACL • 2021 Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graphencoding neural networks. However, recent applications of pretrained transformers to linearizations of graph inputs have…