Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation

Low-resource machine translation requires methods that differ from those used for high-resource languages. This paper proposes a novel in-context learning approach to support low-resource machine translation of the Coptic language to English, with syntactic augmentation from Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs , specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and targeted instructions of difficult constructions identified in sub-trees and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art translation results for Coptic.

The Birds Need Attention Too: Analysing usage of Self Attention in identifying bird calls in soundscapes

Birds are vital parts of ecosystems across the world and are an excellent measure of the quality of life on earth. Many bird species are endangered while others are already extinct. Ecological efforts in understanding and monitoring bird populations are important to conserve their habitat and species, but this mostly relies on manual methods in rough terrains. Recent advances in Machine Learning and Deep Learning have made automatic bird recognition in diverse environments possible. Birdcall recognition till now has been performed using convolutional neural networks. In this work, we try and understand how self-attention can aid in this endeavor. With that we build an pre-trained Attention-based Spectrogram Transformer baseline for BirdCLEF 2022 and compare the results against the pre-trained Convolution-based baseline. Our results show that the transformer models outperformed the convolutional model and we further validate our results by building baselines and analyzing the results for the previous year BirdCLEF 2021 challenge. Source code available at https://github.com/ck090/BirdCLEF-22