Doina Precup


2024

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On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
Jordi Armengol - Estape | Vincent Michalski | Ramnath Kumar | Pierre - Luc St-Charles | Doina Precup | Samira Ebrahimi Kahou
Proceedings of the Fifth Workshop on Insights from Negative Results in NLP

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representations for few-shot classification. More specifically, language is a rich modality that can be used to guide visual learning. In this work, we experiment with a multi-modal architecture for few-shot learning that consists of three components: a classifier, an auxiliary network, and a bridge network. While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization. The bridge should encourage a form of lightweight semantic alignment between language and vision which could be useful for the classifier. However, after evaluating the proposed approach on two popular few-shot classification benchmarks we find that a) the improvements do not reproduce across benchmarks, and b) when they do, the improvements are due to the additional compute and parameters introduced by the bridge network. We contribute insights and recommendations for future work in multi-modal meta-learning, especially when using language representations.

2018

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Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
Kian Kenyon-Dean | Jackie Chi Kit Cheung | Doina Precup
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.

2017

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World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions
Teng Long | Emmanuel Bengio | Ryan Lowe | Jackie Chi Kit Cheung | Doina Precup
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

2016

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Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms
Kian Kenyon-Dean | Jackie Chi Kit Cheung | Doina Precup
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data
Teng Long | Ryan Lowe | Jackie Chi Kit Cheung | Doina Precup
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)