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Register a new userLow Resource Multimodal Neural Machine Translation of English-Hindi in News Domain
Low Resource Multimodal الآلة العصبية ترجمة باللغة الإنجليزية الهندية في مجال الأخبار
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إن دمج طرائق الإدخال المتعددة في نظام الترجمة الآلي (MT) يكتسب شعبية بين الباحثين MT. على عكس مجموعة البيانات المتاحة للجمهور لمهام ترجمة الآلات متعددة الوسائط، حيث تكون التسميات التوضيحية أوصاف صورة قصيرة، توفر التعليق الأخبار وصفا أكثر تفصيلا لمحتويات الصور. نتيجة لذلك، يتم العثور على العديد من الكيانات المسماة المتعلقة بالأشخاص المحددين والمواقع وما إلى ذلك. في هذه الورقة، يكتسبان مجموعة بيانات أخبار أحادية أحادية الأبعاد التي أبلغت باللغة الإنجليزية والهندية مقترنة بالصور لتوليد كوربوس موازية من اللغة الإنجليزية الهندية الاصطناعية. يستخدم Corpus الموازي لتدريب الترجمة الآلية العصبية باللغة الإنجليزية (NMT) ونظام MMT باللغة الإنجليزية من خلال دمج ميزة الصورة المقترنة مع Corpus الموازي المقابلة. نحن أيضا إجراء تحليل منهجي لتقييم أنظمة MT الإنجليزية-الهندية مع 1) المزيد من البيانات الاصطناعية و 2) عن طريق إضافة البيانات المترجمة إلى الوراء. يؤدي النتيجة لدينا إلى تحسن من حيث درجات BLEU لكل من أنظمة NMT (+8.05) و MMT (+11.03).
Incorporating multiple input modalities in a machine translation (MT) system is gaining popularity among MT researchers. Unlike the publicly available dataset for Multimodal Machine Translation (MMT) tasks, where the captions are short image descriptions, the news captions provide a more detailed description of the contents of the images. As a result, numerous named entities relating to specific persons, locations, etc., are found. In this paper, we acquire two monolingual news datasets reported in English and Hindi paired with the images to generate a synthetic English-Hindi parallel corpus. The parallel corpus is used to train the English-Hindi Neural Machine Translation (NMT) and an English-Hindi MMT system by incorporating the image feature paired with the corresponding parallel corpus. We also conduct a systematic analysis to evaluate the English-Hindi MT systems with 1) more synthetic data and 2) by adding back-translated data. Our finding shows improvement in terms of BLEU scores for both the NMT (+8.05) and MMT (+11.03) systems.
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