Multilingual pre-trained models make it possible to develop natural language processing (NLP) applications for low-resource languages (LRLs) using the model of resource-rich languages (RRLs). However, the structural characteristics of the target languages can impact task-specific learning. In this paper, we investigate the influence of structural diversities of languages on the system’s overall performance. Specifically, we propose a customized approach to leverage task-specific data of low-resource language families via transfer learning from RRL. Our findings are based on question-answering tasks using the XLM-R, mBERT, and IndicBERT transformer models and Indic languages (Hindi, Bengali, and Telugu). On the XQuAD-Hindi dataset, the few-shot learning using Bengali improves the benchmark mBERT (F1/EM) score by +(10.86/7.87) and XLM-R score by +(3.84/4.42). Few-shot learning using Telugu has also improved the mBERT score by +(10.42/7.36) and +(3.04/2.72) for XLM-R. In addition, our model has demonstrated benchmark-compatible performance in a zero-shot setup with single-epoch task learning. This approach can be adapted for other NLP tasks for LRLs.

Morphological analysis is a crucial preprocessing stage for building the state-of-the-art natural language processing applications. We propose a bidirectional LSTM (long short-term memory)-based approach to develop the morphological analyzer for the Gujarati language. Our morph analyzer predicts a root word and the morphological features for the given inflected word. We have experimented with two different methods for label representation for predicting morphological features: the monolithic representation method and the individual label representation method. We have also created the gold morphological dataset of 16,234 unique words for the Gujarati language. The dataset contains morpheme splitting and grammatical feature information for each inflected word. Due to the change in the label representation technique in the proposed model, the accuracy of the present baseline system is improved by a large margin. The proposed system performs very well across the POS categories without the knowledge of language-specific suffix rules.

We present optical UBVRI photometric and low-resolution spectroscopic follow-up observations of a type II SN 2012aw in a nearby (~10 Mpc) galaxy M95 during 4 to 270 days post-explosion. The evolution characteristics of optical brightness and color are found to have striking similarity with the archetypal type IIP SN 1999em. The mid-plateau MV is −16.7 mag and the ejected nickel mass is ~0.06 M⊙. The presence and evolution of optical spectral features during 7d to 104d are also similar to SN 1999em as well as other normal type IIP events. The mid-plateau photospheric velocity is around 4200 km s−1 which is same as that of SN 2004et at similar phases, indicating similar energy of explosion i.e. 2 × 1051 erg s−1.

Supernova 2012A was discovered on 7.39UT, January, 2012 in the nearby galaxy NGC 3239 at an unfiltered magnitude of 14.6 and classified spectroscopically as a Type IIP event. Here, we present the optical photometric and spectroscopic follow-up of the event during 14d to 130d post explosion.

The UBVRI photometric follow-up of SN 2011fu has been initiated a few days after the explosion, shows a rise followed by steep decay in all bands and shares properties very similar to that seen in case of SN 1993J, with a possible detection of the adiabatic cooling phase at very early epochs. The spectral modeling performed with SYNOW suggests that the early-phase line velocities for H and Fe ii features were ~ 16000 km s−1 and ~ 14000 km s−1, respectively. Studies of rare class of type IIb SNe are important to understand the evolution of the possible progenitors of core-collapse SNe in more details.