NAIST ’s Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task (2020)

Abstract: This paper describes NAIST’s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.

## introduction neural machine translation (nmt) has significantly improved the quality of machine translation (mt) @xcite @xcite . however, domain-specific translation is still difficult in low-resource scenarios, although high performance can be achieved in resource-rich scenarios @xcite . another major problem is the difficulty in translating noisy input sentences including filler, hesitation, etc. @xcite suggests the difficulty in learning to translate noisy sentences compared to clean ones. the translation of noisy sentences is very important for spoken language translation. in the iwslt 2020 conversational speech translation task, we are going to tackle these two problems. the task includes speech-to-text and textto-text translation from disfluent spanish speeches/transcripts to fluent english text. we chose the text-to-text subtask for our challenge task participation. the data for this task consists of about 130k bilingual pairs, would not be enough to learn a highly accurate nmt @xcite . in such a low-resource scenario, one promising way is domain adaptation using out-of-domain parallel corpora and in-domain monolingual corpora @xcite . in domain adaptation, the "similarity" between in-domain and out-of-domain data affects the translation accuracy significantly @xcite . a domain can be defined by any property of the training data such as topic and style. we expect that the domain similarity comes from these properties. let us return to the task description. in the task, the inputs are conversational speech transcripts by automatic speech recognition (asr). they can include asr errors as well as disfluent and nongrammatical utterances in spontaneous speech. in contrast, the outputs are fluent sequences. in other words, the purpose of this task is to translate disfluent transcripts into fluent sentences. as mentioned before, domain adaptation is a common practice in a low-resource scenario. however, it is difficult to prepare external parallel data in a disfluent source language and a fluent target language. although fluent written parallel data are widely available, the effects of training with them are limited because the style of the input sentences differs from the indomain data. we need a new strategy for training that can effectively use out-of-domain data with low similarity to in-domain data. in this paper, we propose a novel domain adaptation method through style transfer of out-of-domain data using unsupervised machine translation. we increase the similarity between out-of-domain and in-domain data by transferring out-of-domain fluent input sentences into disfluent styles. this enables effective domain adaptive training and provides a robust nmt system for noisy input sentences. out-ofdomain monolingual out-ofdomain parallel style transfer model fluent-to-disfluent nmt model spanish-to-english pseudo in-domain parallel in-domain parallel (2) train in-domain monolingual (1) train in-domain source in-domain target 2 system details our method consists of two components: (1) style transfer model from fluent to disfluent spanish. (2) translation model from disfluent spanish to fluent english, as illustrated in figure 1 . first, we transferred fluent spanish in out-of-domain data into disfluent spanish (section 2.1). then we trained the nmt model leveraging both out-of-domain parallel data as well as in-domain parallel data (section 2.2). ## discussion effect of style transfer in domain adaptation training, the accuracy was slightly improved by transferring the style of out-of-domain data to be like in-domain data. this shows that there is some significance in increasing the similarity between domains through style transfer. however, when we did not perform domain adaptation and only trained with out-of-domain data, the accuracy for in-domain data was reduced by style transfer. the following is an example of style transferred sentence: nueva york 1 a 12 de junio de 2015 (original) nueva york oh a mi eh de de de de (generated) as shown above, some generated sentences lost the meaning of the sentence due to missing phrases. as a result, the quality of the parallel data decreased and the final translation performance was also degraded. one of the causes of this problem is style transfer constraints are too strong. thus, it may be mitigated by a model that could control the tradeoff between style transfer and content preservation @xcite @xcite . further improvement can be expected by preventing changes in the meaning of sentences and converting only the style. ## conclusion in this paper, we presented naist's submission to the iwslt2020 conversational speech translation task. we experimentally show that domain adaptation can improve the translation accuracy of disfluent sentences. moreover, the translation accuracy was improved by increasing the similarity between domains through style transfer, but the effect was limited due to the parallel data quality degradation. furthermore, the loss of accuracy caused by not using clean reference sentences of in-domain data could not be resolved by domain adaptation either. in future work, we will pursue a style transfer system that does not reduce the quality of the parallel data and use it to improve the translation accuracy of nmt. high-quality style transfer may allow us to acquire robustness to the disfluency of input sentences and to learn fluent outputs by removing the disfluency of output sentences.