Building English-to-Serbian machine translation system for IMDb movie reviews

This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically

From Arabic user-generated content to machine translation: integrating automatic error correction

With the wide spread of the social media and online forums, individual users have been able to actively participate in the generation of online content in different languages and dialects. Arabic is one of the fastest growing languages used on Internet, but dialects (like Egyptian and Saudi Arabian) have a big share of the Arabic online content. There are many differences between Dialectal Arabic and Modern Standard Arabic which cause many challenges for Machine Translation of informal Arabic language. In this paper, we investigate the use of Automatic Error Correction method to improve the quality of Arabic User-Generated texts and its automatic translation. Our experiments show that the new system with automatic correction module outperforms the baseline system by nearly 22.59% of relative improvement

A systematic comparison between SMT and NMT on translating user-generated content

Twitter has become an immensely popular platform where the users can share information within a certain character limit (280 characters) which encourages them to deliver short and informal messages (tweets). In general, machine translation (MT) of tweets is a challenging task. However, for translating German tweets about football into English, it has been shown that a moderate translation performance in terms of the BLEU score can be achieved using the phrase-based translation engines built on a tiny parallel Twitter data set [1]. In this work, we propose to further increase the translation quality using the neural machine translation models and applying the following strategies: (i) we back translate a set of out-of-domain English tweets released by ”Harvard data set” in 2017 into German and add the synthetic parallel data to the tiny parallel data used in [1]; (ii) as tweets are short in general, we extract short text pairs from the large news-commentary parallel data and add it to the tiny Twitter parallel data set in order to restrict the length of the out-of-genre text segments. We build both phrase-based and neural MT systems (PBMT and NMT) using the above data combinations in order to perform a systematic comparison between the two approaches on translating tweets. Our experimental results reveal that the NMT system performs significantly worse than the PBMT system when using only the tiny Twitter data set for MT training. In contrast, when additional data is used for training, the results show huge improvements of the NMT system and produce very similar BLEU scores as the PBMT system even with only few hundred thousands of additional synthetic parallel data

Antimony doped tin oxide thin Films: Co gas sensor

in dioxide (SnO2) serves as an important base material in a variety of resistive type gas sensors. The widespread applicability of this semicoducting oxide is related both to its range of conductance variability and to the fact that it responds to both oxidising and reducing gases. The antimony doped tin-oxide films were prepared by spray pyrolysis method. The as-deposited films are blackish in colour. Addition of antimony impurity showed little increase in the thickness. The X-ray diffraction pattern shows characteristic tin oxide peaks with tetragonal structure. As the doping concentration of antimony was increased, new peak corresponding to Sb was observed. The intensity of this peak found to be increased when the Sb concentration was increased from 0.01 % to the 1 % which indicates the antimony was incorporated into the tin oxide. For gas sensing studies ohmic contacts were preferred to ensure the changes in resistance of sensor is due to only adsorption of gas molecule. The graph of I-V shows a straight line in nature which indicates the ohmic contact. The sensitivity of the sensor for CO gas was tested. The sensitivity of antimony doped tin oxide found to be increased with increasing Sb concentration. The maximum sensitivity was observed for Sb = 1 % at a working temperature of 250 °C. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2789

MultiNews: a web collection of an aligned multimodal and multilingual corpus

Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable document and their images in 9 languages from the web news articles of Euronews website.1 This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment

ADAPT at IJCNLP-2017 Task 4: a multinomial naive Bayes classification approach for customer feedback analysis task

In this age of the digital economy, promoting organisations attempt their best to engage the customers in the feedback provisioning process. With the assistance of customer insights, an organisation can develop a better product and provide a better service to its customer. In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i.e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification. The task is to determine what class(es) the customer feedback sentences should be annotated as in four languages. We propose following approaches to accomplish this task: (i) a multinomial naive bayes (MNB) approach for multilabel classification, (ii) MNB with one-vsrest classifier approach, and (iii) the combination of the multilabel classification based and the sentiment classification based approach. Our best system produces F-scores of 0.67, 0.83, 0.72 and 0.7 for English, Spanish, French and Japanese, respectively. The results are competitive to the best ones for all languages and secure 3 rd and 5 the position for Japanese and French, respectively, among all submitted systems

Using images to improve machine-translating E-commerce product listings

In this paper we study the impact of using images to machine-translate user-generated ecommerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multimodal NMT models for re-ranking n-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different nbest list sizes