| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
| Approach: | They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor. |
| Outcome: | The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability. |
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Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |
Deep Latent Variable Models of Natural Language (D18-3)
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| Challenge: | In this tutorial, we will discuss the challenges of applying neural variational inference to NLP problems. |
| Approach: | The tutorial will cover deep latent variable models in the case where exact inference over the latent variables is tractable. |
| Outcome: | The proposed tutorial will cover deep latent variable models in the case where inference cannot be performed tractably and when it is not . |
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming (P19-1)
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| Challenge: | Discrete structures are ubiquitous in the study of natural languages, for example in morphology, syntax and discourse analysis. |
| Approach: | They treat projective dependency trees as latent variables in their probabilistic model and induce them in such a way as to be beneficial for a downstream task. |
| Outcome: | The proposed model is effective on sentiment analysis and natural language inference tasks and is fully differentiable. |
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)
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| Challenge: | Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation . |
| Approach: | They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser. |
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Efficient and Robust Knowledge Graph Construction (2022.aacl-tutorials)
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| Challenge: | Knowledge graph construction has appealed to the NLP community but has encountered similar issues such as efficiency and robustness. |
| Approach: | They propose to introduce efficient and robust knowledge graph construction techniques and discuss their results. |
| Outcome: | This tutorial will provide an overview of the latest and ongoing techniques for efficient and robust knowledge graph construction. |
Scaling up Discovery of Latent Concepts in Deep NLP Models (2024.eacl-long)
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| Challenge: | Existing studies on pre-trained language models focus on clustering latent spaces within pre-training models . authors propose metrics for assessing the quality of discovered latent concepts . |
| Approach: | They propose metrics to assess the quality of discovered latent concepts . they propose to scale latent concept discovery to larger datasets and models . |
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StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
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| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
| Approach: | They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances. |
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Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
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RNN Architecture Learning with Sparse Regularization (D19-1)
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| Challenge: | State-of-the-art NLP models require hundreds of millions and even billions of parameters to perform, which can lead to memory usage and increased runtime. |
| Approach: | They propose a structure learning method that uses group lasso to learn sparse, parameter-efficient NLP models by pruning more than 90% of the weights of rational RNNs. |
| Outcome: | The proposed method learns sparse, parameter-efficient models without sacrificing performance relative to parameter-rich baselines. |