TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	EXTRA DATA	REMOVE
Machine Translation	Business Scene Dialogue EN-JA	Transformer-base	BLEU	13.53	# 1
Machine Translation	Business Scene Dialogue JA-EN	Transformer-base	BLEU	12.88	# 1

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Designing the Business Conversation Corpus

WS 2019 · Matīss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa ·

While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.

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Machine Translation

Translation

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Business Scene Dialogue

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Ranked #1 on Machine Translation on Business Scene Dialogue JA-EN (using extra training data)

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Uses Extra Training Data	Result	Benchmark
Machine Translation	Business Scene Dialogue EN-JA	Transformer-base	BLEU	13.53	# 1			Compare
Machine Translation	Business Scene Dialogue JA-EN	Transformer-base	BLEU	12.88	# 1			Compare

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Absolute Position Encodings • Adam • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • Residual Connection • Scaled Dot-Product Attention • SentencePiece • Softmax • Transformer

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Designing the Business Conversation Corpus

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