Co-reference is always treated as one of challenging tasks under natural language processing and has been explored only in the domain of anaphora resolution to an extent. However, the benefit of it to identify the relations between multiple entities in a single context can be explored better while we aim to identify intent and sentiment from the utterances of a dialogue or conversation. The utilization of co-reference becomes more elegant while tracking users’ intents with respect to their corresponding sentiments explored in a specialized domain like judiciary. Thus, in the present attempt, we have identified not only intent and sentiment expressions at token level in an individual manner, we also classified the utterances and identified the co-reference between intent and sentiment entities in utterance level context. Last but not the least, the deep learning algorithms have shown improvements over traditional machine learning in all cases.

Mythology is a collection of myths, especially one belonging to a particular religious or cultural tradition. We observed that an annotation tool is essential to identify important and complex information from any mythological texts or corpora. Additionally, obtaining highquality annotated corpora for complex information extraction including labeled text segments is an expensive and timeconsuming process. Hence, in this paper, we have designed and deployed an annotation tool for Hindu mythology which is presented as Mytho-Annotator. Its easy-to-use web-based text annotation tool is powered by Natural Language Processing (NLP). This tool primarily labels three different categories such as named entities, relationships, and event entities. This annotation tool offers a comprehensive and adaptable annotation paradigm.

Translation systems rely on a large and goodquality parallel corpus for producing reliable translations. However, obtaining such a corpus for low-resourced languages is a challenge. New research has shown that transfer learning can mitigate this issue by augmenting lowresourced MT systems with high-resourced ones. In this work, we explore two types of transfer learning techniques, namely, crosslingual transfer learning and multilingual training, both with information augmentation, to examine the degree of performance improvement following the augmentation. Furthermore, we use languages of the same family (Romanic, in our case), to investigate the role of the shared linguistic property, in producing dependable translations.