A pp T ek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task (2022)

Abstract: To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding. We further increased translation length compliance by sentence-level selection of length-compliant hypotheses from different system variants, as well as rescoring of N-best candidates from a single system. Length-compliant back-translated and forward-translated synthetic data, as well as other parallel data variants derived from the original MuST-C training corpus were important for a good quality/desired length trade-off. Our experimental results show that length compliance levels above 90% can be reached while minimizing losses in MT quality as measured in BERT and BLEU scores.

## introduction in this paper, we describe apptek's submission to the iwslt 2022 isometric spoken language translation evaluation @xcite . our goal was to create a system that produces translations which are within 10% of the source sentence length, but have similar levels of quality as a baseline system translations without length control. apptek participated in the constrained condition with an english-to-german neural machine translation (nmt) system that we describe in section 2. the system was extended with 5 different length control methods, which we explain in detail in section 3. we also created synthetic data with back-translation, forwardtranslation, as well as a novel data augmentation method of synonym replacement. all three methods are described in section 4. our experimental results on the must-c tst-common test set and the official evaluation test set are presented in section 5, including ablation studies that prove the effectiveness of synthetic data and noisy length encoding for a better trade-off between length compliance and mt quality. we summarize our findings in section 6. ## length control methods in this work we perform an extensive evaluation of different ways to control the length of the translations generated by the nmt model, all applied to the same baseline transformer big model. ## synthetic data we expand the original must-c data with synthetic data of different types, all derived from the given must-c corpus. first, we include a copy of the data 1 in which two consecutive sentences from the same ted talk are concatenated into one. since many segments in the original data are short, this helps to learn more in-context translations. then, we also include a copy of the data where the english side is preprocessed by lowercasing, removing punctuation marks and replacing digits, monetary amounts and other entities with their spoken forms. this helps to adjust to the spoken style of ted talks and imperfections in the (manual) transcriptions of the training and evaluation data. we also use 82k bilingual phrase pairs extracted from word-aligned must-c data, as described below, as training instances. ## experimental results ## conclusion in this paper, we described apptek's neural mt system with length control that we submitted to the iwslt 2022 isometric spoken translation evaluation. we showed that by using length-compliant synthetic data, as well as encoding the desired translation length in various ways, we can significantly increase the length compliance score, while at the same time limiting the loss of information as reflected in only slightly lower bert scores. as one of the best methods for real-time production settings not involving system combination, n-best list rescoring or 2-pass search, the modified positional encoding that counts the desired length in characters achieves the best quality/length compliance trade-off in our experiments. we attribute this to more fine-grained length control capabilities of this system as compared to systems that use source-side or target-side length pseudo-tokens.