Papers by Tuka Alhanai
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations (2021.findings-emnlp)
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| Challenge: | SupCL-Seq extends contrastive learning from computer vision to sequence classification tasks. |
| Approach: | They propose a supervised alternative to Masked Language Modeling (MLM) that extends contrastive learning to sequence optimization in NLP by altering the dropout mask probability in standard Transformer architectures. |
| Outcome: | The proposed method leads to large gains on the GLUE benchmark, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. |
Speak: A Toolkit Using Amazon Mechanical Turk to Collect and Validate Speech Audio Recordings (2022.lrec-1)
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| Challenge: | Speak is a toolkit that allows researchers to crowdsource speech recordings using Amazon Mechanical Turk (MTurk). |
| Approach: | They propose to use Amazon Mechanical Turk to crowdsource speech recordings . they use various measures to ensure that the recordings are of adequate quality . |
| Outcome: | Speak is an open-source toolkit that allows researchers to crowdsource speech recordings using Amazon Mechanical Turk (MTurk). |
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)
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| Challenge: | Adverse Events (AEs) are harmful events resulting from the use of medical products. |
| Approach: | They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness. |
| Outcome: | The proposed approach achieves strong performance over baselines on English benchmarks. |