HW - TSC ’s Participation in the IWSLT 2022 Isometric Spoken Language Translation (2022)

Abstract: This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task. We participate in all three language pairs (English-German, English-French, English-Spanish) under the constrained setting, and submit an English-German result under the unconstrained setting. We use the standard Transformer model as the baseline and obtain the best performance via one of its variants that shares the decoder input and output embedding. We perform detailed pre-processing and filtering on the provided bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, R-Drop, Average Checkpoint, and Ensemble. We investigate three methods for biasing the output length: i) conditioning the output to a given target-source length-ratio class; ii) enriching the transformer positional embedding with length information and iii) length control decoding for non-autoregressive translation etc. Our submissions achieve 30.7, 41.6 and 36.7 BLEU respectively on the tst-COMMON test sets for English-German, English-French, English-Spanish tasks and 100% comply with the length requirements.

## introduction this paper introduces our submissions to the iwslt 2022 isometric spoken language translation task. to train our models, we perform multiple data filtering strategies to enhance data quality. in addition, we leverage multilingual model @xcite , forward @xcite and back translation @xcite , and r-drop @xcite strategies to further enhance training effects. we also adopt length token @xcite , length encoding @xcite and non-autoregressive translation (nat) to further enhance system performances. we compare and contrast different strategies in light of our experiment results and conduct analysis accordingly. the overall training process is illustrated in figure 1 . section 2 focuses on our training techniques, including model architecture, data processing and training strategies. section 3 describes our experiment settings and training process. section 4 presents the experiment results while section 5 analyzes the effects of different model enhancement and length control strategies on the quality and length of translation outputs. ## experiment result ## conclusion this paper presents hw-tsc's submission to iwslt 2022 isometric spoken language translation task. in general, we explore data and model augmentation methods, and achieve huge increases in bleu scores when comparing with baseline models. in terms of length compliance, we use strategies such as length token, length encoding, nat, length-aware beam and rerank. our systems obtain 30.7, 41.6 and 36.7 bleu respectively on the tst-common test sets for english-german, english-french, english-spanish tasks and 100% comply with the length requirements.