Lack of proper linguistic resources is the major challenges faced by the Machine Translation system developments when dealing with the resource poor languages. In this paper, we describe effective ways to utilize the lexical resources to improve the quality of statistical machine translation. Our research on the usage of lexical resources mainly focused on two ways, such as; augmenting the parallel corpus with more vocabulary and to provide various word forms. We have augmented the training corpus with various lexical resources such as lexical words, function words, kridanta pairs and verb phrases. We have described the case studies, evaluations and detailed error analysis for both Marathi to Hindi and Hindi to Marathi machine translation systems. From the evaluations we observed that, there is an incremental growth in the quality of machine translation as the usage of various lexical resources increases. Moreover, usage of various lexical resources helps to improve the coverage and quality of machine translation where limited parallel corpus is available.
Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research. In bilingual communities, the speakers commonly embed the words and phrases of a non-native language into the syntax of a native language in their day-to-day communications. The code-switching is a global phenomenon among multilingual communities, still very limited acoustic and linguistic resources are available as yet. For developing effective speech based applications, the ability of the existing language technologies to deal with the code-switched data can not be over emphasized. The code-switching is broadly classified into two modes: inter-sentential and intra-sentential code-switching. In this work, we have studied the intra-sentential problem in the context of code-switching language modeling task. The salient contributions of this paper includes: (i) the creation of Hindi-English code-switching text corpus by crawling a few blogging sites educating about the usage of the Internet (ii) the exploration of the parts-of-speech features towards more effective modeling of Hindi-English code-switched data by the monolingual language model (LM) trained on native (Hindi) language data, and (iii) the proposal of a novel textual factor referred to as the code-switch factor (CS-factor), which allows the LM to predict the code-switching instances. In the context of recognition of the code-switching data, the substantial reduction in the PPL is achieved with the use of POS factors and also the proposed CS-factor provides independent as well as additive gain in the PPL.
Use of social media has grown dramatically during the last few years. Users follow informal languages in communicating through social media. The language of communication is often mixed in nature, where people transcribe their regional language with English and this technique is found to be extremely popular. Natural language processing (NLP) aims to infer the information from these text where Part-of-Speech (PoS) tagging plays an important role in getting the prosody of the written text. For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier. In order to tackle the problem effectively, we have focused on extracting rich linguistic features. We participate in three different language pairs, ie. English-Hindi, English-Bengali and English-Telugu on three different social media platforms, Twitter, Facebook & WhatsApp. The proposed system is able to successfully assign coarse as well as fine-grained PoS tag labels for a given a code-mixed sentence. Experiments show that our system is quite generic that shows encouraging performance levels on all the three language pairs in all the domains.
In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlation coefficient values, our methods perform more than two times better (nearly 0.62) in predicting the borrowing likeliness compared to the best performing baseline (nearly 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88 percent of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.
Inspired by the success of Deep Learning based approaches to English scene text recognition, we pose and bench-mark scene text recognition for three Indic scripts - Devanagari, Telugu and Malayalam. Synthetic word images rendered from Unicode fonts are used for training the recognition system. And the performance is bench-marked on a new - IIIT-ILST dataset comprising of hundreds of real scene images containing text in the above mentioned scripts. We use a segmentation free, hybrid but end-to-end trainable CNN-RNN deep neural network for transcribing the word images to the corresponding texts. The cropped word images need not be segmented into the sub-word units and the error is calculated and backpropagated for the the given word image at once. The network is trained using CTC loss, which is proven quite effective for sequence-to-sequence transcription tasks. The CNN layers in the network learn to extract robust feature representations from word images. The sequence of features learnt by the convolutional block is transcribed to a sequence of labels by the RNN+CTC block. The transcription is not bound by word length or a lexicon and is ideal for Indian languages which are highly inflectional.