A Comparison of Head Transducers and Transfer for a Limited Domain Translation Application
By Hiyan Alshawi, A. Buchsbaum. 1997
We compare the effectiveness of two related machine translation models applied to the same limited-domain task. One is a transfer model with monolingual head automata for analysis and generation; the other is a direct transduction model based on bilingual head transducers. We conclude that the head transducer model is more effective according to measures of accuracy, computational requirements, model size, and development effort.
Subfields: Natural Language Processing
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In Natural Language Processing
- Jimmy J. Lin
- Rodrigo Nogueira
- Andrew Yates
- Zeynep Akkalyoncu Yilmaz
- Wei Yang
- Sheng-Chieh Lin
- Jheng-Hong Yang
- Haotian Zhang
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “The earliest example we could find of using this trick to generate synthetic training data is Alshawi et al. [1997]. Bannard and Callison-Burch [2005] is often cited for using “pivot languages” (the other language we translate into and back) as anchors from automatically extracting paraphrases from ”