ServiceNow Research

Natural Language Processing

DuoRAT: Towards Simpler Text-to-SQL Models
DuoRAT: Towards Simpler Text-to-SQL Models
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. …
North American Chapter of the Association for Computational Linguistics (NAACL),  2021.
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Understanding by Understanding Not: Modeling Negation in Language Models
Understanding by Understanding Not: Modeling Negation in Language Models
Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language …
North American Chapter of the Association for Computational Linguistics (NAACL),  2021.
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Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. …
International Conference on Learning Representations (ICLR),  2021.
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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2020.
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On the impressive performance of randomly weighted encoders in summarization tasks
On the impressive performance of randomly weighted encoders in summarization tasks
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence …
Annual Meeting of the Association for Computational Linguistics (ACL),  2019.
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Investigating Trust Factors in Human-Robot Shared Control: Implicit Gender Bias Around Robot Voice
This paper explores the impact of warnings, audio feedback, and gender on human-robot trust in the context of autonomous driving and …
Conference on Computer and Robotic Vision (CRV),  2019.
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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and …
International Conference on Learning Representations (ICLR),  2019.
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Towards Deep Conversational Recommendations
Towards Deep Conversational Recommendations
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational …
Conference on Neural Information Processing Systems (NeurIPS),  2018.
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Towards Text Generation with Adversarially Learned Neural Outlines
Towards Text Generation with Adversarially Learned Neural Outlines
Recent progress in deep generative models has been fueled by two paradigms – au- toregressive and adversarial models. We propose a …
Conference on Neural Information Processing Systems (NeurIPS),  2018.
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Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE …
ArXiv,  2018.
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