Papers with optimization criteria
Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)
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| Challenge: | Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot. |
| Approach: | They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios. |
| Outcome: | The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models. |
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks (2023.findings-acl)
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| Challenge: | Existing studies show that pre-trained language models can be used to mitigate societal biases and stereotypes. |
| Approach: | They propose a modular bias mitigation approach that integrates debiasing modules into the core model on-demand at inference time. |
| Outcome: | The proposed approach improves on-par with baseline finetuning on gender, race, and age protected attributes on three classification tasks with gender, age, and race as protected attributes. |
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020.emnlp-main)
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| Challenge: | Existing active learning approaches for textual data are limited due to the complexity of language. |
| Approach: | They propose an approach where guided outputs of a language generation model can be enhanced through an active learning process. |
| Outcome: | The proposed approach achieves performance increases of 3% and 5% on TREC-6 and SST-2 datasets compared with NGDG, which does not optimize for a reward function. |