UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic (2024)

Abstract: The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 lowresource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.

heading: 1 Introduction
text: There are a variety of challenges inherent in spoken language translation for low-resource languages. By definition, these languages have very limited data available to use for natural language processing (NLP) tasks. The majority of current work on NLP targets just 20 out of the approximately 7,000 languages used worldwide, leading to a significant gap in research and negative impacts on excluded speech communities (Joshi et al., 2020; Magueresse et al., 2020). While most machine learning tasks are performed using vast amounts of data, models for low-resource languages must be adapted to work with less data or other strategies must be employed to augment the existing data.
In this paper, we present our submission for the 2024 IWSLT low-resource shared task. Concretely, we submit two systems for speech translation to English, from Maltese and North Levantine Arabic. The main motivation for focusing on these two languages is their similarity to one another which we aim to exploit to improve the performance of our pipeline speech translation system.
As a well-known case of diglossia, the Arabic language has a notable distinction between the formal variety used in written communication, political speech, and the educational system – known as Modern Standard Arabic (MSA) – and the informal varieties primarily used in spoken communication – collectively referred to as Dialectal Arabic (DA). These dialects exhibit considerable diversity influenced by geographical and socio-economic factors, diverging significantly from MSA in phonology, morphology, lexicon, and syntax (Zbib et al., 2012). On the other hand, Maltese is a Semitic language derived from Siculo-Arabic (Borg and AzzopardiAlexander, 1997), with a notable mutual intelligibility with Tunisian DA (Čéplö et al., 2016). Its evolution independently from the Arab world – particularly its substantial influence from Italian and English and its use of a modified Latin alphabet – makes it a distinct language and not an Arabic dialect.
We split the task of spoken language translation into two sequential tasks consisting of automatic speech recognition (ASR) and machine translation (MT). This process transforms speech in a lowresource language into text in a high-resource language, namely English for this shared task. At the same time, splitting the task into ASR and MT allows us to exploit existing multilingual models and source larger corpora for each sub-task to improve their performance in the target language. All of our code is made publicly available.1

heading: 2 Related Work
text: In order to examine past approaches to lowresource spoken language translation, this literature review includes an overview of previous IWSLT low-resource track submissions and our ASR and
1https://github.com/saranabhani/ iwslt-2024-um-pipeline
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MT systems as well as our innovative approach to data augmentation through transliteration.

heading: 2.1 Previous IWSLT Low-Resource Track Approaches
text: For the 2023 IWSLT Shared Task (Agarwal et al., 2023), Williams et al. (2023a) submitted five systems for Maltese-English spoken language translation as part of the low-resource track in the unconstrained setting. This marked the first time that Maltese was included in the IWSLT low-resource track campaign, with this submission being the sole entry in its category, making it a unique approach in this context. All of the systems employed a pipeline approach, making use of XLS-R (Conneau et al., 2020) for ASR and mBART-50 (Tang et al., 2020) for MT, fine-tuned using various training data. In their primary approach, their model was exclusively fine-tuned on Maltese data resulting in a BLEU score of 0.6. Contrasting with the Maltese-only model, they explored four alternative approaches by incorporating corpora from Arabic, French, Italian, or a combination of all three in conjunction with the Maltese data. The most successful configuration, with a BLEU score of 0.7, was achieved by fine-tuning the ASR system on a combination of Maltese data with 50 hours each of Arabic, French, and Italian data from the CommonVoice speech corpus (Williams et al., 2023a).
While our submission utilizes a pipeline approach, the alternative is an end-to-end system where a single neural network is trained to jointly perform both ASR and MT (Sethiya and Maurya, 2023). This approach offers several advantages by significantly reducing training time, allowing for quicker development of models, and necessitating lower memory resources compared to other methods, which can be particularly beneficial for environments with constraints on computational processing power. Additionally, by integrating ASR and MT into an end-to-end system, it mitigates the risk of errors propagating from the ASR output to the MT input, which is a common problem in pipeline systems (Sethiya and Maurya, 2023). However, speech translation systems that operate end-to-end require parallel data containing both speech audio signals on the source side and translated transcriptions on the target side. Acquiring such parallel data can pose challenges, even for languages with readily available components for pipeline-based systems. Consequently, the pipeline approach is often deemed more feasible and realis-
tic (Alves et al., 2020). For the 2023 IWSLT Quechua-Spanish speech translation task in the low-resource track, E. Ortega et al. (2023) utilized a variety of systems both constrained and unconstrained, with one of the few pipeline-based methods submitted for this task. The primary constrained system employed a direct speech translation model based on the Fairseq speech-to-text (S2T) framework (Wang et al., 2020). To create audio representations, this system made use of log mel-scale filter banks for features and a transformer for translations. With a BLEU score of 1.25, their primary system surpassed the performance of the pipeline alternatives in the constrained setting. On the other hand, the primary unconstrained system employed a pipeline approach on the additional 60 hours of speech data made available, where speech transcriptions were generated using a pretrained XLS-R based multilingual model augmented by a fine-tuned language model (Park et al., 2019), and translations were generated using the fine-tuned Flores-101 model from Guzmán et al. (2019). The unconstrained pipeline approach performed much better with a BLEU score of 15.36 for the primary model. Their findings reveal that the use of a pretrained language model with fine-tuning is necessary for cascaded spoken language translation (ASR and MT combined in a pipeline) in low-resource scenarios for Quechua to Spanish translation. This work further demonstrates the immense value of access to additional data, which yielded nearly 14 BLEU points improvement for the unconstrained task when applied to both ASR and MT systems compared to the limited data used in the constrained setting (E. Ortega et al., 2023). Accordingly, our approach also utilizes an unconstrained pipeline of an XLS-Rbased ASR model and a fine-tuned pretrained MT model considering it was found to have the best results for this submission.

heading: 2.2 Automatic Speech Recognition
text: Due to their extensive multilingual pretraining, Wav2Vec 2.0 models (Baevski et al., 2020) are able to acquire and utilize cross-lingual speech representations to improve accuracy for ASR. The XLS-R model presented by Babu et al. (2022) underwent pretraining of Wav2Vec 2.0 models for as many as 128 distinct languages including Maltese, using 436,000 hours of unannotated speech data from diverse sources including the Mozilla Common Voice (Ardila et al., 2020), BABEL (Gales et al., 2014),
and Multilingual LibriSpeech (Pratap et al., 2020) speech corpora. Specifically, this system incorporates 9,000 hours of unannotated Maltese speech sourced from the Voxpopuli corpus (Wang et al., 2021). Notably, the largest model is pretrained using a cumulative total of 56 thousand hours of speech data (Conneau et al., 2020). This represents an increase in both the amount of data and the languages covered.
A common practice in the field of ASR is to use a language model to reduce errors in the generated transcription. This technique was used in the development of Wav2Vec 2.0 (Baevski et al., 2020) and Deepspeech 2 (Amodei et al., 2016). Leveraging an external language model trained on domain-specific textual data has the potential to increase the accuracy of ASR systems by minimizing errors in content.
Moreover, due to the scarcity of high-quality labelled data in DA, the models based on XLS-R emerge as optimal solutions for leveraging available datasets and adapting ASR to distinct Arabic variants through fine-tuning, as highlighted by Waheed et al. (2023) in their work on VoxArabica. These models not only capitalize on existing resources but also offer the adaptability to accommodate the nuances of various Arabic dialects, thus addressing the challenges associated with the limited availability of labelled data for DA.

heading: 2.3 Machine Translation
text: Past approaches to multilingual neural machine translation treat it as a sequence-to-sequence task, where an encoder is utilized to process an input sequence in the source language and a decoder is used to generate the corresponding output sequence in the target language. With massively multilingual translation, a model undergoes training on multiple translation directions simultaneously. While this approach can facilitate advantageous cross-lingual transfer among related languages, it also carries the risk of amplifying interference between unrelated languages.
In this work we make use of the NLLB model (NLLB Team et al., 2022) for MT. It uses a single SentencePiece model to tokenize the text sequences by training it across all languages using a total of 100M sentences sampled from primary bitext data. For equitable representation of low-resource languages, high-resource ones are downsampled and low-resource ones are upsampled, using a sampling temperature of five. The resulting vocabulary size
of the trained SentencePiece model is 256,000, ensuring comprehensive representation across the diverse range of supported languages. The choice of this model is highly motivated by its inclusion of a large number of languages, notably Maltese and North Levantine Arabic (NLLB Team et al., 2022).

heading: 2.4 Transliteration
text: From a simplified linguistic perspective, Maltese can be regarded as a variant of Arabic with a significant level of code-switching to Italian and a modified Latin alphabet. Past work suggests that transliterating Maltese could serve as a viable strategy for benefiting from cross-lingual similarities with Arabic (Micallef et al., 2023). In the approach taken by Micallef et al. (2023), the transliteration process involves two main steps: mapping and ranking. Initially, Maltese text tokens and characters in Latin script are mapped to one or more corresponding alternatives in Arabic script. Subsequently, a separate component either ranks these alternatives or employs a deterministic hard-coded baseline.
This approach is further developed in Micallef et al. (2024) by taking a mixed pipeline and integrating a combination of transliteration and translation based on the etymology of Maltese words. This is motivated by the results of Micallef et al. (2023), where the advantages of transliterating Arabicorigin words were limited by the corresponding disadvantages of distancing Italian and English-origin words from their etymological source through transliteration. A mixed pipeline gave promising results on downstream tasks, establishing the technique as a competitive approach for Maltese NLP tasks.

heading: 3 Automatic Speech Recognition

heading: 3.1 Data Sources
text: For Maltese, we use the training sets provided by the shared task namely Common Voice 7.0 (Ardila et al., 2020) and MASRI (Hernandez Mena et al., 2020). The speech corpus is made up of around 50 hours of Maltese speech data.
To train our Arabic ASR system, we opted to use a 50-hour subset from the Common Voice project (Ardila et al., 2020), as this would contain roughly the same data that we used for Maltese ASR. While a training set for North Levantine Arabic would have been preferred, there was no data provided for the shared task, nor were we able to find ASR data for North Levantine Arabic. Furthermore, even
though a Tunisian Arabic training set could be used, we did not make use of this to train, since North Levantine Arabic is more closely related to MSA than Tunisian Arabic (Kwaik et al., 2018).

heading: 3.2 Approach
text: For the ASR component of the pipeline, we continue to build off of previous work done for both DA and Maltese ASR. As concluded in Williams et al. (2023b), fine-tuning the Wav2Vec 2.0 XLS-R model (Babu et al., 2022) with around 50 hours of Maltese speech data produces the best Maltese ASR model to date and was used in the IWSLT 2023 submission by Williams et al. (2023a). A similar XLS-R based ASR model is employed for DA by leveraging data for MSA.
In addition, for Maltese, we incorporate language models with the ASR system to get more accurate speech transcriptions. For this we use ngram models built using the KenLM language modelling toolkit (Heafield, 2011), which assign scores to sequences of words. This aids in selecting the best candidates through beam search for improved ASR output. We use KenLM mainly as it has been used for other state-of-the-art ASR publications as well as in previous work on Maltese in particular. We make use of the 6-gram word-level LM produced by Hernandez Mena et al. (2020) as a baseline to compare our own KenLM n-gram models which was trained on Korpus Malti v3.0 (Gatt and Čéplö, 2013). We produce 2 additional word-level n-gram language models for Maltese: a 3-gram and a 4-gram, both trained on the Korpus Malti v4.1 Shuffled train dataset2 (Micallef et al., 2022). We note that Korpus Malti v4 used here is substantially larger than the v3 used for the 6-gram baseline.

heading: 3.3 Results and Discussion
text: Table 1 shows the WER score for all languages considered on the shared task development set. For both Maltese and North Levantine Arabic, a single model is trained, but for Maltese we show the models’ performance without adding a language model as well as incorporating each language model.
For Maltese, we see that all models perform comparably, but models using a language model give better results. In addition, when using the 3-gram and 4-gram models, these give better results than the 6-gram model, which we attribute to the larger data used to train the former models.
2https://huggingface.co/datasets/MLRS/korpus_ malti/tree/4.1.0/data/shuffled
The overall performance of our Arabic approach was limited by the lack of North Levantine Arabic speech data, which severely impacted the accuracy of the ASR system when tested on Levantine data. We provide a brief qualitative error analysis of the Arabic ASR outputs to highlight this.
We looked at a sample of the ASR output generated from North Levantine Arabic audio data using our model trained on MSA. The analysis of specific examples reveals various errors that significantly impact the usability of the ASR system for Levantine speech recognition. Table 2 shows a few examples of the output, highlighting various inconsistencies with the reference text.
Phonetic errors were a common issue across the examples. For instance, in Sample 2c, the system outputted “ IÓX A ” for “ IÓY ® ̄” (I applied) likely
due to the similar pronunciation of “ ” and “ ¬” at the start of word, and the dialect-specific pronunciation of “ ” (qaf) as a glottal stop [P] in Levantine Arabic, which is similar to “Z” (hamza). Additionally, in Sample 2a, segmentation errors featured prominently, as seen where “ àA¾J J ë” should have been segmented into “ àA¿ ù ë” (she was). In Sample 2d, “PñÓ
BAK ñ ÉJ.ÓA JË AJ. K P A K” exemplifies improper
segmentation, where “ø ñ ” (a little) and “PñÓB@”
(matters) were incorrectly merged as “PñÓ BAK ñ ".
Lexical errors were evident, particularly in Sample 2b, where “ á J ºJ Ë @” (the Czechs) was incorrectly outputted as “Õæ º
", missing both the prefix “È@” (the) and misinterpreting the main noun due to a phonetic mix-up of “Ð” and “ à”, which are both nasal consonants. Phonetic confusion also occurred
in Sample 2d, where “ú ̄ [fi:]” (in) was replaced
with “ú G. [bi:]”. In the case of “ AK Pñ ” (Syria), the ASR output was “ éK Pñ ", only differing by the final character. These two characters have the same pronunciation in word-final position, so the difference is just orthographic.
The analysis revealed that the Character Error Rate (CER) was consistently better than the Word Error Rate (WER), highlighting that while individual characters are often recognized correctly, the system struggles to assemble these into correct word forms. This indicates foundational competence at the character level but significant challenges in managing the complexity of word formation, especially considering the morphological and contextual nuances of North Levantine Arabic.
Whilst some character-level errors seem to be due to similar phonetic characteristics of different characters, it is clear that multiple errors can be attributed to Levantine-specific dialectal differences, most prominently the “ ” (qaf) and “Z” (hamza) distinction. These character-level errors impact wordlevel recognition and subsequent performance on the downstream machine translation task.
The prevalence of errors due to dialectal differences underscores the need to integrate Levantinespecific training data and develop a dedicated language model to handle the nuances brought by dialectal variations.

heading: 4 Machine Translation

heading: 4.1 Data Sources
text: To train our translation models we make use of a variety of sources for parallel data including those provided for the shared task as well as others which we could find. The datasets used are summarized in Table 3.
To train our Maltese translation model, we used a combined dataset of Common Voice (CV) (Ardila et al., 2020) and MASRI project (Hernandez Mena et al., 2020) (henceforth referred to as CV+MASRI), both of which were the datasets provided officially for the shared task. In addition, we also used OPUS-100, which is a comprehensive English-focused dataset (Zhang et al., 2020; Tiedemann, 2012). The dataset consists of 100 languages and English is common in every 99 translated language pairs. We chose this dataset because of its vastness, especially considering it offered 1M parallel sentences for the English and Maltese pair. We preprocessed the data to drop any data points that were empty as well as duplicate instances.
For our Arabic translation systems, we utilized a range of datasets. Specifically, we used the North Levantine (APC)-MSA-English textual data provided for the task (Sellat et al., 2023), along with the IWSLT 2022 Tunisian Arabic (AEB) speech translation data (Anastasopoulos et al., 2022). Additionally, the MSA data, which was included with both the Tunisian and Levantine datasets, was also used. However, since the size of this data was miniscule, we also incorporated the Arab-Acquis MSA-English parallel data (Habash et al., 2017). To further augment the Arabic data, we also incorporated the CV+MASRI Maltese dataset, which was transliterated to match the script of our primary data as detailed in Section 4.2 (referred to as MLTARA). We merged datasets from the same dialect or language obtained from multiple sources and shuffled them to ensure diversity and randomness in our training process.
Since the speech transcriptions do not produce casing and punctuation information, and the evaluation for the shared task also ignores these features, we preprocess all translation data as such. For both
languages, preprocessing included text normalization such as converting to lowercase and removing punctuation, while retaining hyphens and apostrophes for Maltese datasets, as these characters hold linguistic significance in Maltese. In addition, for Arabic we also remove diacritics.

heading: 4.2 Transliteration
text: Following Micallef et al. (2023, 2024), we explored integrating transliteration of Maltese into Arabic script, due to the close relationship between Maltese and Arabic as Semitic languages. We took inspiration from this approach to supplement the data used for training the Arabic Machine Translation system. Since Micallef et al. (2024) saw more promising results when using a mixed pipeline of transliteration, that involved transliterating Maltese words of Arabic origin and translating the other words, we continue with this mixed approach. We utilize the etymology model and mapping systems from Micallef et al. (2024). Specifically, we follow the Xara/Tara pipeline, which transliterates tokens of Arabic-origin and symbols, translating everything else to Arabic.
However, we make certain modifications to this to better suit our approach. Firstly, we modify the translation component by swapping out the precomputed word translations from Google Translate with a pretrained NLLB model (NLLB Team et al., 2022), as extracting translations using Google Translate was too expensive, especially considering the different outputs produced by the ASR while experimenting. Translation is performed into Tunisian Arabic (AEB) instead of MSA, using English as a pivot language. The reason for doing this is that translating through English generally yields better results rather than going directly to Arabic, due to the larger availability of parallel data, and this is also observed empirically in Micallef et al. (2024).
Secondly, we merge tokens to more closely reflect the way in which Arabic is written, reducing
the signals from Maltese tokenization. For example, “u il-kelma” (English ‘and the word’), are written together in Arabic script as one word, “ éÒÊ¾Ë@ð”, where “ð” is the conjunction corresponding to “u” (and), “È@” is the definite article corresponding to “il-” (the), and the rest of the word corresponds to “kelma” (word). The annotation for such token mappings from Micallef et al. (2023), includes special markers indicating that such words would be merged in Arabic, so given the 3 tokens u, il-, and kelma, the system would initially output +ð, +È@, and éÒÊ¿, which we merge into a single word. While Micallef et al. (2023, 2024) ignore this signal as they mostly deal with token tagging tasks, we use this signal to merge words. Note that using this method, punctuation symbols are still space separated, but since the data is preprocessed to remove such symbols, this is not an issue in our case.
We applied the transliteration pipeline to the Maltese datasets provided for the shared task (CV+MASRI). The training dataset provided additional data for training the Arabic MT model. In addition to the data augmentation benefit of integrating transliterated Maltese (henceforth referred to as MLTARA) for Arabic-English MT, this also increases the cross-lingual capacities of our Arabic MT model, allowing for the evaluation of MLTARA ASR outputs using the Arabic MT model.

heading: 4.3 Approach
text: We explored various machine translation (MT) systems for translating North Levantine Arabic and Maltese into English. Initially, we established a baseline by fine-tuning the NLLB 1.3B model3 (NLLB Team et al., 2022) on the shared task data, specifically on the CV+MASRI dataset for Maltese and the AEB dataset for Arabic.
Subsequently, we experimented with different fine-tuning strategies. We first attempted a two-
3https://huggingface.co/facebook/nllb-200-1. 3B
stage fine-tuning process where we fine-tune with a large dataset from a different domain or dialect. For the first stage, we considered the OPUS-100 data for the Maltese model and the MSA data for the Arabic model, while the second stage included the same data used for the baseline for both languages. Additionally, for the Arabic we tested finetuning with a mix of Levantine (APC) and Tunisian (AEB) data, as well as a combination of Levantine (APC), Tunisian (AEB), and transliterated Maltese (MLTARA) data. The training on MLTARA, allows us to evaluate this system on both the Maltese and North Levantine development sets.
The same hyperparameters were applied across both MT systems: a learning rate of 2e-5, and a weight decay of 0.01. The training was conducted over three epochs.

heading: 4.4 Results and Discussion
text: Table 4 reports the BLEU scores of the Maltese and Arabic models on the transcriptions having reference translations from the respective development sets for Maltese and North Levantine Arabic.
The results for Maltese are reported in Table 4a. We see that fine-tuning using OPUS-100 only, is detrimental compared to the baseline system trained only on CV+MASRI. However, including both OPUS-100 and CV+MASRI yields the best performance. Furthermore, when evaluating the Arabic model trained on transliterated Maltese, in addition to other Arabic data, we observe that it is the worst-performing model. However, the performance is quite comparable to that obtained for the model fine-tuned only on OPUS-100.
Table 4b shows the performance of each of the experimented machine translation systems on the APC validation set. Among the experimented methods, the best performance on the North Levantine Arabic development set was achieved using the twostage fine-tuning process that started with MSA data followed by Levantine data.
An important observation that arose from our experimentation was the impact of adding MLTARA data to the training of the Arabic MT system. We can see that the system fine-tuned firstly on MSA data and subsequently on APC, AEB, and MLTARA gave very competitive results for Arabic MT, with a difference in dev performance of just 0.2 BLEU compared to the same system without MLTARA data. By comparison, the same system performed comparably to Arabic on the Maltese dev set (after being transliterated) with a BLEU of 37.0. Whilst the machine translation systems fine-tuned specifically for Maltese still significantly outperformed the Arabic system fine-tuned with MLTARA data, we note that adding MLTARA data in the fine-tuning of Arabic MT systems can vastly improve the crosslingual capacity of the model, with substantial benefits to the performance on Maltese, and very little impact on the MT performance for Arabic.

heading: 5 Speech Translation Pipeline
text: Following our evaluation on individual tasks in Sections 3 and 4, we now combine both systems by first getting the transcription using an ASR system and then passing this transcription through the MT system to get the translation. The best-performing ASR and MT systems on our validation sets were selected for the pipelines.
For Maltese, we only choose the MT system trained with the 2 stage training, OPUS100+CV+MASRI (which we refer to as MLTLAT) and combine it with the ASR systems with the 3-gram, 4-gram, and 6-gram models, to compose our Primary, Contrastive 1, and Contrastive 2 systems, respectively. For North Levantine Arabic, we use the only trained model for ASR, paired with all 3 MT systems which made use of 2 stage training, namely MSA+APC+AEB, MSA+APC+AEB+MLTARA, and MSA+APC, to compose our Primary, Contrastive 1, and Contrastive 2 systems, respectively. Table 5 summarises the results obtained with these pipelines on the development and testing sets, on ASR only and Speech Translation (ASR+MT). For the Arabic test
set, only Speech Translation results were provided. As seen in Table 5a, all Maltese systems perform competitively with each other. Similar to the findings for the ASR system reported in Section 3, the Primary and Contrastive 1 systems get the best results with the 3-gram and 4-gram models, and the Contrastive 2 system is slightly behind with the 6-gram model. The best systems obtain 52.4 BLEU on the test set.
The results on the North Levantine Arabic data are shown in Table 5b. The systems all achieve low overall BLEU scores, due to the poor performance on ASR as outlined in Section 3. With a pipeline, we observe that using APC data only in addition to MSA performs the worst (Contrastive 2), and that adding data from other languages and dialects we achieve better BLEU scores with the Primary and Contrastive 1 systems.

heading: 6 Conclusion
text: Overall, this paper presented our findings for Maltese and North Levantine Arabic spoken language translation into English with a pipeline system in the unconstrained setting for the 2024 IWSLT lowresource track shared task. For our approach we fine-tune a Wav2Vec 2.0 XLS-R model for ASR, and an NLLB model for MT. We enhance the ASR model by correcting the outputs with a language model. Moreover, we augment the MT data from
additional sources and employ a two-stage finetuning process to improve performance. Additionally, we exploit the cross-lingual similarities between Maltese and Arabic by transliterating Maltese to Arabic script, observing interesting performance boosts.
In terms of limitations, the lack of training data for North Levantine Arabic impeded the progress of our ASR system. By using MSA to train our Arabic ASR models, the resulting system struggled with non-standard pronunciation and dialectspecific variation. Furthermore, the absence of testing data for Tunisian Arabic hindered our models considering its close similarity with Maltese.
More general improvements could be undertaken in future work such as hyper-parameter tuning and supplementing currently available data with backtranslation. Rather than relying solely on parallel data, implementing backtranslation with larger monolingual corpora holds promise for improving the MT systems discussed in this paper.

heading: Acknowledgments
text: We acknowledge the assistance of the LT-Bridge Project (GA 952194) and DFKI for the use of their Virtual Laboratory.

heading: Future Work
text: More general improvements could be undertaken in future work such as hyper-parameter tuning and supplementing currently available data with backtranslation. Rather than relying solely on parallel data, implementing backtranslation with larger monolingual corpora holds promise for improving the MT systems discussed in this paper