Paradigm Completion for Derivational Morphology

• URL: http://arxiv.org/abs/1708.09151v2
• Date: Fri, 9 Aug 2024 06:44:35 GMT
• Title: Paradigm Completion for Derivational Morphology
• Authors: Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, David Yarowsky,
• Abstract summary: derivational morphology has been an overlooked problem in NLP. We introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. We show that state-of-the-art neural models are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%.
• Score: 48.405826282007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.

Related papers

• Relative Representations: Topological and Geometric Perspectives [53.88896255693922]
  Relative representations are an established approach to zero-shot model stitching. We introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes.
  arXiv  Detail & Related papers  (2024-09-17T08:09:22Z)
• A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language [15.929767234646631]
  Increase in data, size, or compute can lead to sudden learning of specific capabilities by a neural network. "emergence" is a phenomenon often called "emergence"
  arXiv  Detail & Related papers  (2024-08-22T17:44:22Z)
• Contextual modulation of language comprehension in a dynamic neural model of lexical meaning [0.0]
  We demonstrate the architecture and behavior of the model using as a test case the English lexical item 'have', focusing on its polysemous use. Results support a novel perspective on lexical polysemy: that the many related meanings of a word are metastable neural activation states.
  arXiv  Detail & Related papers  (2024-07-19T23:28:55Z)
• Recent advancements in computational morphology : A comprehensive survey [0.11606731918609076]
  Computational morphology handles the language processing at the word level. Morpheme boundary detection, lemmatization, morphological feature tagging, morphological reinflection etc.
  arXiv  Detail & Related papers  (2024-06-08T10:07:33Z)
• On the Trade-off Between Efficiency and Precision of Neural Abstraction [62.046646433536104]
  Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. We employ formal inductive synthesis procedures to generate neural abstractions that result in dynamical models with these semantics.
  arXiv  Detail & Related papers  (2023-07-28T13:22:32Z)
• Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
  We propose to model latent structures with a learned dynamic potential landscape. Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations. Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T15:53:45Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• On Long-Tailed Phenomena in Neural Machine Translation [50.65273145888896]
  State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens. We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation. We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy.
  arXiv  Detail & Related papers  (2020-10-10T07:00:57Z)
• Generalisation error in learning with random features and the hidden manifold model [23.71637173968353]
  We study generalised linear regression and classification for a synthetically generated dataset. We consider the high-dimensional regime and using the replica method from statistical physics. We show how to obtain the so-called double descent behaviour for logistic regression with a peak at the threshold. We discuss the role played by correlations in the data generated by the hidden manifold model.
  arXiv  Detail & Related papers  (2020-02-21T14:49:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.