Maximiliana Behnke


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Edinburgh’s Submissions to the 2020 Machine Translation Efficiency Task
Nikolay Bogoychev | Roman Grundkiewicz | Alham Fikri Aji | Maximiliana Behnke | Kenneth Heafield | Sidharth Kashyap | Emmanouil-Ioannis Farsarakis | Mateusz Chudyk
Proceedings of the Fourth Workshop on Neural Generation and Translation

We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multi-core CPU, and GPU. At the model level, we use teacher-student training with a variety of student sizes, tie embeddings and sometimes layers, use the Simpler Simple Recurrent Unit, and introduce head pruning. On GPUs, we used 16-bit floating-point tensor cores. On CPUs, we customized 8-bit quantization and multiple processes with affinity for the multi-core setting. To reduce model size, we experimented with 4-bit log quantization but use floats at runtime. In the shared task, most of our submissions were Pareto optimal with respect the trade-off between time and quality.

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Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation
Maximiliana Behnke | Kenneth Heafield
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The attention mechanism is the crucial component of the transformer architecture. Recent research shows that most attention heads are not confident in their decisions and can be pruned. However, removing them before training a model results in lower quality. In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training. Our experiments on machine translation show that it is possible to remove up to three-quarters of attention heads from transformer-big during early training with an average -0.1 change in BLEU for Turkish→English. The pruned model is 1.5 times as fast at inference, albeit at the cost of longer training. Our method is complementary to other approaches, such as teacher-student, with English→German student model gaining an additional 10% speed-up with 75% encoder attention removed and 0.2 BLEU loss.


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Improving Machine Translation of Educational Content via Crowdsourcing
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
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)