Papers with Transformer based models
Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks (2022.aacl-main)
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| Challenge: | Experimental results show that transformer-based models can handle Dialect Arabic (DA) classification tasks with a large corpus of labeled examples. |
| Approach: | They extend transformer-based models with unlabeled data in a generative adversarial setting using semi-supervised Generative Adversarial Networks (SS-GAN) they show that the model can produce high-quality embeddings for the Dialect Arabic examples and generalize for the downstream classification task given few labeled examples. |
| Outcome: | The proposed model outperforms models with unlabeled data in a generative adversarial setting with unlabelled examples and faster convergence when only a few labeled examples are available. |
Hierarchical Transformer for Task Oriented Dialog Systems (2021.naacl-main)
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| Challenge: | Existing models for dialog generation are challenging to train using the standard Seq2Seq models. |
| Approach: | They propose a framework for Hierarchical Transformer Encoders that can be morphed into any hierarchical transformer by using specially designed attention masks and positional encodings. |
| Outcome: | The proposed framework can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings. |
Transkimmer: Transformer Learns to Layer-wise Skim (2022.acl-long)
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| Challenge: | Prior work has proposed to augment Transformer model with the capability of skimming tokens to improve its computational efficiency. |
| Approach: | They propose to add a parameterized predictor before each layer that learns to make the skimming decision. |
| Outcome: | The proposed model achieves 10.97x speedup on GLUE benchmark compared with BERT-base baseline with less than 1% accuracy degradation. |
Mamba Knockout for Unraveling Factual Information Flow (2025.acl-long)
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| Challenge: | Recent work has introduced Mamba-based SSM architectures that rival Transformer performance in various settings. |
| Approach: | They propose to use attentional interpretability techniques originally developed for Transformers to trace how information is transmitted and localized across tokens and layers. |
| Outcome: | The proposed model disentangles how distinct features enable token-to-token information exchange or enrich individual tokens, thus offering a unified lens to understand Mamba internal operations. |