Papers with training model
Client-Customized Adaptation for Parameter-Efficient Federated Learning (2023.findings-acl)
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| Challenge: | Pre-trained language models have a large memory footprint and are difficult to use in federated learning (FL) |
| Approach: | They propose a hypernetwork-based FL framework that generates client-specific adapters by conditioning the client information. |
| Outcome: | The proposed framework maximizes the utility of shared model parameters while minimizing divergence caused by client heterogeneity. |
MatchTime: Towards Automatic Soccer Game Commentary Generation (2024.emnlp-main)
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| Challenge: | Existing data on soccer commentary are often unsatisfactory, and the quality of existing data is often poor. |
| Approach: | They propose to manually annotate timestamps for 49 soccer matches and then use them to create a model to correct and filter existing data. |
| Outcome: | The proposed model improves the viewing experience of soccer and can be trained on the curated dataset. |
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)
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| Challenge: | Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks. |
| Approach: | They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics. |
Concept Pointer Network for Abstractive Summarization (D19-1)
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| Challenge: | Abstractive summarization (ABS) has gained overwhelming success owing to a tremendous development of sequence-to-sequence models and its variants. |
| Approach: | They propose a concept pointer network that leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts and then points to the most appropriate choice using both the concept set and original source text. |
| Outcome: | The proposed model improves on the DUC-2004 and Gigaword datasets and human evaluation of its abstractive abilities supports the quality of the summaries produced. |
Consistency Training with Virtual Adversarial Discrete Perturbation (2022.naacl-main)
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| Challenge: | Existing methods for regularizing a model are agnostic to the training model and may not be effective for perturbed inputs. |
| Approach: | They propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions by replacing tokens while keeping original semantics. |
| Outcome: | The proposed method outperforms baselines on semi-supervised text classification tasks and a robustness benchmark. |