Papers with word generation

3 papers
A Distributional and Orthographic Aggregation Model for English Derivational Morphology (P18-1)

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Challenge: Existing approaches to derived word generation model derivational morphology to generate words with particular semantics are not effective.
Approach: They propose a novel aggregation model that learns derivational transformations as orthographic functions and as functions in distributional word embedding space.
Outcome: The proposed model learns to choose between the hypothesis of each system and the hypothesis from the model.
Finite State Machine Pattern-Root Arabic Morphological Generator, Analyzer and Diacritizer (2020.lrec-1)

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Challenge: Using a finite-state morphologizer, we generate and analyze undiacritized Modern Standard Arabic (MSA) words.
Approach: They propose to use a finite-state Arabic Morphologizer to generate and analyze undiacritized Arabic words and diacritize them.
Outcome: The proposed model generates and analyzes undiacritized Modern Standard Arabic (MSA) words and diacritizes them.
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)

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Challenge: Existing methods for autoregressive text generation have low controllability and accumulating errors.
Approach: They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy.
Outcome: Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance.

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