Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages
Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages.
Code (1)
Tasks
Question AnsweringTransfer LearningSimilar Papers 제목 키워드 기반
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
This paper discusses the construction, fine-tuning, and deployment of BeaverTalk, a cascaded system for speech-to-text translation as part of the IWSLT 2025 simultaneous translation task. The system architecture employs …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Sentencespeech-recognition+4Domain Adaptation for Object Detection using SE Adaptors and Center Loss
Despite growing interest in object detection, very few works address the extremely practical problem of cross-domain robustness especially for automative applications. In order to prevent drops in performance due to doma…
Domain Adaptationobject-detectionObject DetectionUnsupervised Domain AdaptationDomain Adaptor Networks for Hyperspectral Image Recognition
We consider the problem of adapting a network trained on three-channel color images to a hyperspectral domain with a large number of channels. To this end, we propose domain adaptor networks that map the input to be comp…
DiffPrompter: Differentiable Implicit Visual Prompts for Semantic-Segmentation in Adverse Conditions
Semantic segmentation in adverse weather scenarios is a critical task for autonomous driving systems. While foundation models have shown promise, the need for specialized adaptors becomes evident for handling more challe…
Autonomous DrivingSegmentationSemantic SegmentationCollaborative and Efficient Personalization with Mixtures of Adaptors
Non-iid data is prevalent in real-world federated learning problems. Data heterogeneity can come in different types in terms of distribution shifts. In this work, we are interested in the heterogeneity that comes from co…
Federated LearningMulti-Task Learning