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. |
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| 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. |
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 . |
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (N18-2)
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| Challenge: | Several strategies have been proposed to overcome the projectivity constraint by introducing transition-based dependency parsers that can build non-projective dependencies. |
| Approach: | They propose a modification of dynamic oracles to allow use of non-projective data . their method consistently outperforms traditional projectivization and pseudo-projectivisation approaches . |
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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 . |
Synthetic Data Made to Order: The Case of Parsing (D18-1)
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| Challenge: | supervised dependency parsing is a core task in natural language processing, but unsupervised parsers can hardly produce useful parses. |
| Approach: | They propose to permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. |
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Recursive Top-Down Production for Sentence Generation with Latent Trees (2020.findings-emnlp)
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| Challenge: | Various studies have shown that incorporating syntactic structures into recursive encoders can be beneficial for various natural language tasks. |
| Approach: | They propose a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves to train a recursive neural function. |
| Outcome: | The proposed model outperforms previous models on the LENGTH split and English question formation tasks on the Multi30k dataset. |
Variational Inference and Deep Generative Models (P18-5)
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| Challenge: | Unsupervised and semi-supervised learning has been addressed scarcely in NLP . this tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Approach: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
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Compound Probabilistic Context-Free Grammars for Grammar Induction (P19-1)
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| Challenge: | Existing approaches to grammar induction have resorted to manually-engineered features and auxiliary objectives to induce the desired structures. |
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A Stochastic Decoder for Neural Machine Translation (P18-1)
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| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
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Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
| Approach: | They propose to combine word dependency graphs and latent graphs to create latent models . they propose to model the interaction between the aspect and its surrounding contexts . |
| Outcome: | The proposed model can complement syntactic features with latent semantic dependencies. |