Extended Parallel Corpus for Amharic-English Machine Translation

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be useful for machine translation of an under-resourced language, Amharic. The corpus is larger than previously compiled corpora; it is released for research purposes. We trained neural machine translation and phrase-based statistical machine translation models using the corpus. In the automatic evaluation, neural machine translation models outperform phrase-based statistical machine translation models.Comment: Accepted to 2nd AfricanNLP workshop at EACL 202

A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content

International audienceThis work compares the performances achieved by Phrase-Based Statistical Ma- chine Translation systems (PBSMT) and attention-based Neural Machine Transla- tion systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be ex- pected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models

Impact of Tokenization on Language Models: An Analysis for Turkish

Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for morphologically rich languages, such as Turkic languages, where many words can be generated by adding prefixes and suffixes. We compare five tokenizers at different granularity levels, i.e. their outputs vary from smallest pieces of characters to the surface form of words, including a Morphological-level tokenizer. We train these tokenizers and pretrain medium-sized language models using RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. We then fine-tune our models on six downstream tasks. Our experiments, supported by statistical tests, reveal that Morphological-level tokenizer has challenging performance with de facto tokenizers. Furthermore, we find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers. The ratio of the number of vocabulary parameters to the total number of model parameters can be empirically chosen as 20% for de facto tokenizers and 40% for other tokenizers to obtain a reasonable trade-off between model size and performance.Comment: submitted to ACM TALLI

Comparison results of Google Translate and Microsoft Translator on the novel Mughamarah Zahrah Ma'a Ash-Syajarah by Yacoub Al-Sharouni

This research aims to compare the translation results of Google Translate and Microsoft Translator based on grammatical aspects devoted to fi'il ma’lum-majhul, zaman al-fi'l, and dhamir. This type of study is qualitative, comparative, and descriptive. The main source of data in this research is the novel Mughamarah Zahrah Ma'a Ash-Shajarah by Yacoub Al-Sharouni. Secondary data sources are literature related to Arabic grammatical rules, google Translate translation, and Microsoft translator. The data collection technique is translating a novel by a sentence from Arabic to Indonesian using Google Translate and Microsoft Translator and then recording it. Data validation techniques are performed by triangulation of data and time. Data analysis techniques use data reduction techniques, data presentation, and conclusions. The result of this research is that Google Translate produces good translations in terms of fi'il ma'lum-majhul. Google Translate and Microsoft Translator are inconsistent in translating zaman al-fi'l, while Microsoft Translator produces good translations in terms of dhamir

A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content

International audienceThis work compares the performances achieved by Phrase-Based Statistical Ma- chine Translation systems (PBSMT) and attention-based Neural Machine Transla- tion systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be ex- pected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models

Sentence Similarity and Machine Translation

Neural machine translation (NMT) systems encode an input sentence into an intermediate representation and then decode that representation into the output sentence. Translation requires deep understanding of language; as a result, NMT models trained on large amounts of data develop a semantically rich intermediate representation. We leverage this rich intermediate representation of NMT systems—in particular, multilingual NMT systems, which learn to map many languages into and out of a joint space—for bitext curation, paraphrasing, and automatic machine translation (MT) evaluation. At a high level, all of these tasks are rooted in similarity: sentence and document alignment requires measuring similarity of sentences and documents, respectively; paraphrasing requires producing output which is similar to an input; and automatic MT evaluation requires measuring the similarity between MT system outputs and corresponding human reference translations. We use multilingual NMT for similarity in two ways: First, we use a multilingual NMT model with a fixed-size intermediate representation (Artetxe and Schwenk, 2018) to produce multilingual sentence embeddings, which we use in both sentence and document alignment. Second, we train a multilingual NMT model and show that it generalizes to the task of generative paraphrasing (i.e., “translating” from Russian to Russian), when used in conjunction with a simple generation algorithm to discourage copying from the input to the output. We also use this model for automatic MT evaluation, to force decode and score MT system outputs conditioned on their respective human reference translations. Since we leverage multilingual NMT models, each method works in many languages using a single model. We show that simple methods, which leverage the intermediate representation of multilingual NMT models trained on large amounts of bitext, outperform prior work in paraphrasing, sentence alignment, document alignment, and automatic MT evaluation. This finding is consistent with recent trends in the natural language processing community, where large language models trained on huge amounts of unlabeled text have achieved state-of-the-art results on tasks such as question answering, named entity recognition, and parsing

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

In general, advances in translation technology tools have enhanced translation quality significantly. Unfortunately, however, it seems that this is not the case for all language pairs. A concern arises when the users of translation tools want to work between different language families such as Arabic and English. The main problems facing ArabicEnglish translation tools lie in Arabic’s characteristic free word order, richness of word inflection – including orthographic ambiguity – and optionality of diacritics, in addition to a lack of data resources. The aim of this study is to compare the performance of translation memory (TM) and machine translation (MT) systems in translating between Arabic and English.The research evaluates the two systems based on specific criteria relating to needs and expected results. The first part of the thesis evaluates the performance of a set of well-known TM systems when retrieving a segment of text that includes an Arabic linguistic feature. As it is widely known that TM matching metrics are based solely on the use of edit distance string measurements, it was expected that the aforementioned issues would lead to a low match percentage. The second part of the thesis evaluates multiple MT systems that use the mainstream neural machine translation (NMT) approach to translation quality. Due to a lack of training data resources and its rich morphology, it was anticipated that Arabic features would reduce the translation quality of this corpus-based approach. The systems’ output was evaluated using both automatic evaluation metrics including BLEU and hLEPOR, and TAUS human quality ranking criteria for adequacy and fluency.The study employed a black-box testing methodology to experimentally examine the TM systems through a test suite instrument and also to translate Arabic English sentences to collect the MT systems’ output. A translation threshold was used to evaluate the fuzzy matches of TM systems, while an online survey was used to collect participants’ responses to the quality of MT system’s output. The experiments’ input of both systems was extracted from ArabicEnglish corpora, which was examined by means of quantitative data analysis. The results show that, when retrieving translations, the current TM matching metrics are unable to recognise Arabic features and score them appropriately. In terms of automatic translation, MT produced good results for adequacy, especially when translating from Arabic to English, but the systems’ output appeared to need post-editing for fluency. Moreover, when retrievingfrom Arabic, it was found that short sentences were handled much better by MT than by TM. The findings may be given as recommendations to software developers