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Natural Language Processing

Azimuth: Systematic Error Analysis for Text Classification
Azimuth: Systematic Error Analysis for Text Classification
We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2022.
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In-Context Learning for Text Classification with Many Labels
In-Context Learning for Text Classification with Many Labels
In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, …
Workshop at the Conference on Empirical Methods in Natural Language Processing (EMNLP),  2022.
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On the Compositional Generalization Gap of In-Context Learning
On the Compositional Generalization Gap of In-Context Learning
Pretrained large generative language models have shown great performance on many tasks, but exhibit low compositional generalization …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2022.
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UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2022.
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Breadth-First Pipeline Parallelism
Breadth-First Pipeline Parallelism
We introduce Breadth-First Pipeline Parallelism, a novel training schedule which optimizes the combination of pipeline and data …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Countering Language Drift with KL Regularization
End-to-end interactive learning of dialogue systems has been all-but-abandoned in favour of other approaches using more labelled data, …
Workshop on Interactive Learning for Natural Language Processing (NeurIPS Workshop),  2022.
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The Stack: 3 TB of permissively licensed source code
The Stack: 3 TB of permissively licensed source code
Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)–not only for natural …
Transactions on Machine Learning Research (TMLR),  2022.
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A Planning based Neural-Symbolic Approach for Embodied Instruction Following
The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. …
Montreal AI Symposium (MAIS),  2022.
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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based …
Montreal AI Symposium (MAIS),  2022.
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Explainable, Sensible and Virtuous Workplace Chatbots
We outline three research directions towards the practical implementation of explainable, sensible and virtuous chatbots for the …
Montreal AI Symposium (MAIS),  2022.
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