Papers by Maximiliana Behnke
Improving Machine Translation of Educational Content via Crowdsourcing (L18-1)
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Maximiliana Behnke, Antonio Valerio Miceli Barone, Rico Sennrich, Vilelmini Sosoni, Thanasis Naskos, Eirini Takoulidou, Maria Stasimioti, Menno van Zaanen, Sheila Castilho, Federico Gaspari, Panayota Georgakopoulou, Valia Kordoni, Markus Egg, Katia Lida Kermanidis
| Challenge: | Using crowdsourcing to train neural machine translation models is expensive and expensive . professional outsourcing of bilingual data is expensive if the translations are of a lower quality . |
| Approach: | They analyze the impact of crowdsourcing on the quality of in-domain training data . they use translations of MOOCs from English to eleven languages to fine-tune machine translation models . |
| Outcome: | The proposed method improves on general-domain training data and with pre-existing in-domain corpora. |
Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Recent research shows that attention heads are not confident in their decisions and can be pruned. |
| Approach: | They apply the lottery ticket hypothesis to prune heads in early training . they find that the pruned model is 1.5 times faster at inference . |
| Outcome: | The proposed method is 1.5 times faster at inference, but at the cost of longer training. |