| Challenge: | Prior work on text complexity has focused on simplifying input text in one language, primarily English. |
| Approach: | They propose a method to align news articles written for different levels of target language proficiency. |
| Outcome: | The proposed model outperforms pipeline approaches that translate and simplify text independently. |
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| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
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Controlling Pre-trained Language Models for Grade-Specific Text Simplification (2023.emnlp-main)
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| Challenge: | Existing approaches to text simplification control output complexity at corpus level disregarding complexity of individual inputs and considering only one level of output complexity. |
| Approach: | They propose a method that predicts edit operations required for a specific grade level . they say this approach improves the quality of the simplified outputs over corpus-level heuristics . |
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Multilingual Neural Machine Translation with Task-Specific Attention (C18-1)
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| Challenge: | Multilingual machine translation is a task of building a system capable of translating between multiple source and target languages. |
| Approach: | They propose task-specific attention models to retain parameter sharing generalization . they observe improved translation quality even in low-resource zero-shot directions . |
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Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
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Learning Simplifications for Specific Target Audiences (P18-2)
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| Challenge: | Text simplification is a monolingual text-to-text transformation task . data from TS data can contain multiple simplifications of the same original text . |
| Approach: | They propose to use sequence-to-sequence neural models to build models tailored for specific grade levels. |
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Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)
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| Challenge: | Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating. |
| Approach: | They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages. |
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A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification (L18-1)
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| Challenge: | Xu et al., 2016) show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification. |
| Approach: | They evaluate neural sequence-to-sequence models for text simplification on Wikipedia and Newsela datasets. |
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Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification (2021.acl-long)
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| Challenge: | Text simplification reduces the language complexity of professional content for accessibility purposes. |
| Approach: | They propose that text simplification can be decomposed into a pipeline of tasks . they show that the pipeline can be used to predict whether a text needs to be simplified . |
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Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
| Approach: | They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders. |
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Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
| Approach: | They propose a recipe for training machine translation models on synthetic target data by leveraging a large pre-trained model. |
| Outcome: | The proposed model outperforms training on real-world translation datasets. |