Simplifying Paragraph-level Question Generation via Transformer Language Models

Lopez, Luis Enrico; Cruz, Diane Kathryn; Cruz, Jan Christian Blaise; Cheng, Charibeth

doi:10.48550/arxiv.2005.01107

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2021

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“…During generation, we limit all outputs to a maximum sequence length of 100, preemptively terminating generation if it begins to exceed this maximum length. We do not use sampling during translation, nor increase the temperature parameter as this induces randomness (Lopez et al, 2020).…”

Section: Translationmentioning

confidence: 99%

Data Processing Matters: SRPH-Konvergen AI's Machine Translation System for WMT'21

Sutawika¹,

Cruz²

2021

Preprint

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In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT'21 Large Scale Multilingual Translation Task -Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest's hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian → Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques.

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Section: Translationmentioning

confidence: 99%