Machine Translated texts are often far from perfect and postediting is essential to get publishable quality. Post-editing may not always be a pleasant task. However, modern machine translation (MT) approaches like Statistical MT (SMT) and Neural MT (NMT) seem to hold greater promise. In this work, we present a quantitative method for scoring translations and computing the post-editability of MT system outputs. We show that the scores we get correlate well with MT evaluation metrics as also with the actual time and effort required for post-editing. We compare the outputs of three modern MT systems namely phrase-based SMT (PBMT), NMT, and Google translate for their Post-Editability for English to Hindi translation. Further, we explore the effect of various kinds of errors in MT outputs on postediting time and effort. Including an Indian language in this kind of post-editability study and analyzing the influence of errors on postediting time and effort for NMT are highlights of this work.

The interface between the translators and their ecological environment becomes vital in understanding the nature of the translation carried out and the final shape the target texts take. The translators’ subjectivity can only be understood in relation to their context of production, circulation, and reception. It is therefore important in any product-oriented research to study the ecological environment of the translators and its influence on their decision-making process and the translation strategy that they adopt. The present paper is an attempt in that direction. It presents a case study of two different translations of a Telugu classical text, Kanyasulkam, in English. The study reveals how the overall context of translation becomes a major agency in conditioning the work of the translators and how it accounts for the divergence between the two translations of the text selected. It also brings to the fore a very interesting technique of translating a classical text from India by a transnational translator in an alien environment for the consumption of the distant other.

Nineteenth-century Calcutta has been widely researched to understand its role in the making of a ‘modern’ India. However, the ‘translational’ culture of this period has not received enough attention. The present article traces what it terms Calcutta’s ‘translational culture’ by examining a palimpsest of languages and genres through the mediating role of translation. Nineteenth-century was a time when several languages were competing for space in the making of modern Bengali prose. Most of the writers of the time were negotiating a plural and multilingual domain and experimenting with new styles of prose and poetry writing. Two such examples can be seen in the works of Michael Madhusudan Dutt (1824 – 1873), and Kaliprassana Singha (1841 -1870). These writers were instrumental in the making of new genres and were negotiating multiple languages and linguistic registers that included – Sanskrit, Bengali with its different elite and colloquial registers, English, and several European languages and literatures. In juxtaposing Dutt and Singha, the present article attempts to point towards a parallel history of the nineteenth-century Calcutta traced through moments of transactions, translations, and negotiations among languages, ideas, and world views. Languages and literary genres in this case become a testimony to the rich texture of social and cultural negotiations that went into the making of the modernist Bengali prose and indicative of its palimpsestic and translational nature.

Aithihyamala (1909) is a compilation of oral legends and folktales in Malayalam by Kottarathil Shankunni. A hundred years since its first publication, and many translations hence, re-translating it into English to suit the contemporary reader comes with its own share of challenges. Overcoming the barrier of archaic language was one thing as was the translation of cultural contexts and culture itself. But more demanding was the employment of a contemporary politically correct lens to the stories themselves, and exercising it in translation in such a manner that while the translation and the translator do remain invisible, the text is suitably modified in places so that blatant prejudices and partisanship inherent in the text do not overshadow the stories themselves. The paper discusses how the translator employed either domestication or foreignization and sometimes a combination of both in order to make sense of the canonical Malayalam text in English, and the rationale for employing each approach so as to make the text relevant and meaningful to the contemporary reader.

This paper is an attempt in building a rule-based dependency parser for Telugu which can parse simple sentences. This study adopts Pa?ini’s Grammatical (PG) tradition i.e., the dependency model to parse sentences. A detailed description of mapping semantic relations to vibhaktis (case suffixes and postpositions) in Telugu using PG is presented. The paper describes the algorithm and the linguistic knowledge employed while developing the parser. The research further provides results, which suggest that enriching the current parser with linguistic inputs can increase the accuracy and tackle ambiguity better than existing data-driven methods.

The paper is an attempt to compare Hyderabad Telugu Treebank (HTTB) and HCU-IIIT-H Telugu Treebank from a statistical point of view. HTTB has 2,715 annotated sentences and HCU-IIIT-H TTB has 3,222 annotated sentences. Both the Treebanks were annotated by following Paninian Grammar Formalism proposed by Bharati, A.; Sharma, D.M.; Husain, S.; Bai, L.; Begam, R. and Sangal, R. (2009). HTTB is an interchunk-based treebank data. HCU-IIIT-H TTB is an intrachunk-based treebank data. Both the treebanks’ data size is random. Later, the paper discusses the Telugu Treebanks in detail. The paper focuses on statistical frequencies viz. POS, Chunk and Syntactic labels. VM (3807 times) and NN (5486 times) are the frequent POS labels in HTTB and HCU-IIIT-H TTB respectively. NP (7954 and 6223 times) is the frequent phrasal category in both the treebanks. The most frequent klabels are karta(k1) (2375-2381 times) and karma(k2) (1408- 1437 times) and non-frequent label is kara?a(k3) (17-39 times) in both the treebanks. The most frequent non-k-labels are verb modifier (vmod) (949 times) and noun modifier (nmod) (1033 times) in both the treebanks. The statistical distribution mentions the coverage of the labels (karaka, non-karaka) of both the Telugu treebanks. Later it discusses the comparison of both the treebanks and tries to provide the reasons for the highest and lowest frequencies in both the treebanks. k1 and k2 have 60% of the coverage in karaka labels, vmod, nmod, adv, ccof, pof also has 60% of the coverage in non-karaka labels. This kind of statistical study can help to boost the accuracy of the parser.