ServiceNow AI Research

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Compositional Generalization in Dependency Parsing
Compositional Generalization in Dependency Parsing
Compositionality, or the ability to combine familiar units like words into novel phrases and sentences, has been the focus of intense …
Annual Meeting of the Association for Computational Linguistics (ACL),  2022.
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LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing
LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing
Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent …
Annual Meeting of the Association for Computational Linguistics (ACL),  2022.
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The Power of Prompt Tuning for Low-Resource Semantic Parsing
The Power of Prompt Tuning for Low-Resource Semantic Parsing
Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language tasks. In …
Annual Meeting of the Association for Computational Linguistics (ACL),  2022.
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Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently …
International Conference on Learning Representations (ICLR),  2022.
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Learning to Guide and to Be Guided in the Architect-Builder Problem
Learning to Guide and to Be Guided in the Architect-Builder Problem
We are interested in interactive agents that learn to coordinate, namely, a builder – which performs actions but ignores the goal …
International Conference on Learning Representations (ICLR),  2022.
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Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the …
Causal Learning and Reasoning (CLeaR),  2022.
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Typing assumptions improve identification in causal discovery - theory and algorithms
Typing assumptions improve identification in causal discovery - theory and algorithms
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Causal Learning and Reasoning (CLeaR),  2022.
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VIM: Variational Independent Modules for Video Prediction
VIM: Variational Independent Modules for Video Prediction
We introduce a variational inference model called VIM, for Variational Independent Modules, for sequential data that learns and infers …
Causal Learning and Reasoning (CLeaR),  2022.
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Continual Learning via Local Module Composition
Continual Learning via Local Module Composition
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then …
Conference on Neural Information Processing Systems (NeurIPS),  2021.
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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the …
Conference on Neural Information Processing Systems (NeurIPS),  2021.
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