Generating image description automatically from the content of an image is one of the fundamental problems in artificial intelligence. This task involves the knowledge of both computer vision and natural language processing, called “Image caption generation” Many researches have been carried out in this field, but it was mainly focused on generating image descriptions in English, as existing image caption datasets are mostly in English. However, the image captioning should not be restricted by language. The lack of image captioning dataset other than English is a problem, especially for a morphologically rich language such as Hindi. Thus, to tackle this problem, this research constructed Hindi image caption dataset based on images from Flickr8k dataset using Google cloud translator, which is called Flickr8k-Hindi Datasets The Flickr8k-Hindi Datasets consist of four datasets based on a number of descriptions per image and clean or unclean descriptions. This study also finds the best effective method to generate image description using the encoder-decoder neural network model. The experiments showed that training the model with a single clean description per image generates higher-quality caption than a model trained with five uncleaned descriptions per image. Although model trained with five uncleaned descriptions per image achieved BLEU-I score of 0.585, which is the current state of the art.

Deep learning apporach for image captioning in Hindi language

Machine translation is a process of translating one natural language to another without much human interaction. Evaluation of any Machine Translation System (MTS) is the most important factor in a machine learning environment. There are many techniques existing to determine and optimize the quality of output in any MTS. Earlier methods are based on human judgments. Even though human evaluation methods are very much reliable, they suffer due to some disadvantages such as high cost, more time consuming and also poor re-usability. Hence, automatic methods have been proposed to reduce time and cost. In this survey, we have discussed different metrics under the automatic evaluation techniques in order to evaluate the output quality of MTS. It is believed that machine learning system developers at large would get befitted by this survey.

A Comparative Study and Analysis of Evaluation Matrices in Machine Translation

Machine learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Emerging Trends and Applications in Cognitive Computing

In many existing solutions of image-to-image rendering problems, the only focus is to find the closest output of the Generative Adversarial Network (GAN). In this research article, authors propose a generative adversarial network, a solution to pixel-to-pixel rendering problems and reduced the loss function to the maximum under all interactions. For achieving the best result, we have considered the mean square loss function in the generator and binary cross for the discriminator. Our proposed model deals with not only images but also read sketches where the edges are not sharp too. We have used a facade dataset to test our proposed model.

http://it-in-industry.org/index.php/itii/article/download/458/402

Conditional gans as a solution to image-to-image rendering problems

This study seeks to investigate the quality of translation generated by a popular Machine Translation (MT) platform, viz. Google Translate (GT) in translating the content of a Russian flight charterer’s website from English into Indonesian. To analyze the translation quality produced by MT, we compared the original translation of the website texts translated by a human translator and the machine-translated version. The translation quality is limited to the readability level. The texts serving as the data were collected from the menus of a Russian flight chartering service website which was originally translated by an Indonesian translator. An analysis further discovered that the translation produced by MT is divided into two quality categories: less readable and readable. The less readable translation is caused by MT’s inability to adjust to stylistic forms in Indonesian, failure to do adaptation strategy and in some cases, MT tends to follow the same sentence structure of the original text (source text).  

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Machine Translation in Website Localization: Assessing its Translation Quality for Language Learning

The task of the Evaluation of Machine Translation (MT) is very difficult and challenging too. The difficulty comes from the fact that translation is not a science but it is more an art; most of the sentences can be translated in many acceptable forms. Consequently, there is no such fix standard against which a particular translation can be evaluated. If it has been possible to make an independent algorithm that would be able to evaluate a specific Machine Translation, then the belief is that this evaluation algorithm will be a better algorithm than the translating algorithm itself. Initially, MT evaluation used to be done by human beings which was a time-consuming task and highly subjective too. Also evaluation results may vary from one human evaluator to another for the same sentence pair. Therefore we need automatic evaluation systems, which are quick and objective. Different methods for Automatic Evaluation of Machine Translation have been projected in recent years, out of which many of them have been accepted willingly by the MT community. In the proposed work, we have checked the applicability of BLEU metric and of its modified versions for English to Hindi Machine Translation(s) particularly for Agriculture Domain. Further, we have incorporated some additional features like synonym replacement and shallow parsing modules and after that we have calculated the final score by using BLEU and M-BLEU metrics. The sentences which have been tested are taken from Agriculture Domain. The BLEU metric does not consider the synonym problem and it considers synonym as different words thereby lowering down the final calculated score while comparing the human translations with the machine translations. To overcome this drawback of BLEU, we have incorporated a synonym replacement module in our algorithm. For this, first of all the word is replaced by its synonym present in any of the reference human translations and then it is compared with the reference human translation.

An improvement in BLEU metric for English-Hindi machine translation evaluation

Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi). To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language. In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics. Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods. Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score. This is found to provide more accuracy than prevailing methods. Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.

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Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

Machine Translation, sometimes referred by the acronym MT, is one of the important fields of study of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of atomic words in one natural language for words in another language. Around the world, numerous systems are available in the market for the assessment of the translation being done by the various translation systems. Even within India, a large number of such evaluation systems are available and a lot of research is still going on to develop a better evaluation system which can beat the results produced by Human Evaluators. Even the main challenge before Indian Researchers is that the evaluation systems which are giving unbeatable results for the translation of Foreign languages (such as German, French, Chinese, etc.) are not even giving considerable results for the translation of Indian Languages (Hindi, Tamil, Telugu, Punjabi, etc.). So at par these evaluation systems cannot be applied as it is to evaluate Machine Translations of Indian Languages. Indian languages require a novel approach because of the relatively unrestricted order of words within a word group. In this paper, we are presenting an algorithm (by incorporating different modules of language models like synonym replacement, root word extraction and shallow parsing) which when applied upon the translation of English to Hindi text gives better evaluation results as compared to those algorithms which do not incorporate all these modules. Moreover, our study is limited to English to Hindi language pair and the testing is being with the corpora of agriculture domain.

An algorithm for the better assessment of machine translation

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Key Issues in Machine Translation Evaluation of English-Indian Languages



Automatic Machine Translation (AMT) Evaluation Metrics have become popular
in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation
engines and Machine Translation as a field itself. Translator is a very important tool to break barriers
between communities especially in countries like India, where people speak 22 different languages and their
many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates
how well these are performing. This is where machine translation evaluation enters.



This paper discusses the importance of Automatic Machine Translation Evaluation and compares
various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and
human evaluations to find out which metric has the highest correlation with human scores.



The correlation between the Automatic and Human Evaluation Scores and the correlation between
the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up
and p-values are calculated to find out how significant these correlations are.



The results of the statistical analysis of the scores of various metrics and human scores are shown
in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation
Evaluation metrics and human scores.



Out of the five metrics considered for the study, METEOR shows the highest correlation with
human scores as compared to the other metrics.


Anurag Singh Baghel

Papers

An improvement in BLEU metric for English-Hindi machine translation evaluation

Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

An algorithm for the better assessment of machine translation

Key Issues in Machine Translation Evaluation of English-Indian Languages

Statistical Analysis of Machine Translation Evaluation Systems for English- Hindi Language Pair