Sanasam Ranbir Singh

Also published as: Sanasam Ranbir Singh


2023

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Jack-flood at SemEval-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation
Sujit Kumar | Aditya Sinha | Soumyadeep Jana | Rahul Mishra | Sanasam Ranbir Singh
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The rise of social media has exponentially witnessed the use of clickbait posts that grab users’ attention. Although work has been done to detect clickbait posts, this is the first task focused on generating appropriate spoilers for these potential clickbaits. This paper presents our approach in this direction. We use different encoding techniques that capture the context of the post text and the target paragraph. We propose hierarchical encoding with count and document length feature-based model for spoiler type classification which uses Recurrence over Pretrained Encoding. We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval. For multipart spoiler retrieval, we combine the above two spoiler retrieval methods. Experimental results over the benchmark suggest that our proposed spoiler retrieval methods are able to retrieve spoilers that are semantically very close to the ground truth spoilers.

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Subwords to Word Back Composition for Morphologically Rich Languages in Neural Machine Translation
Telem Joyson Singh | Sanasam Ranbir Singh | Priyankoo Sarmah
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

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Assamese Back Transliteration - An Empirical Study Over Canonical and Non-canonical Datasets
Hemanta Baruah | Sanasam Ranbir Singh | Priyankoo Sarmah
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

2022

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Detecting Incongruent News Articles Using Multi-head Attention Dual Summarization
Sujit Kumar | Gaurav Kumar | Sanasam Ranbir Singh
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

With the increasing use of influencing incongruent news headlines for spreading fake news, detecting incongruent news articles has become an important research challenge. Most of the earlier studies on incongruity detection focus on estimating the similarity between the headline and the encoding of the body or its summary. However, most of these methods fail to handle incongruent news articles created with embedded noise. Motivated by the above issue, this paper proposes a Multi-head Attention Dual Summary (MADS) based method which generates two types of summaries that capture the congruent and incongruent parts in the body separately. From various experimental setups over three publicly available datasets, it is evident that the proposed model outperforms the state-of-the-art baseline counterparts.

2021

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Manipuri-English Machine Translation using Comparable Corpus
Lenin Laitonjam | Sanasam Ranbir Singh
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)

Unsupervised Machine Translation (MT) model, which has the ability to perform MT without parallel sentences using comparable corpora, is becoming a promising approach for developing MT in low-resource languages. However, majority of the studies in unsupervised MT have considered resource-rich language pairs with similar linguistic characteristics. In this paper, we investigate the effectiveness of unsupervised MT models over a Manipuri-English comparable corpus. Manipuri is a low-resource language having different linguistic characteristics from that of English. This paper focuses on identifying challenges in building unsupervised MT models over the comparable corpus. From various experimental observations, it is evident that the development of MT over comparable corpus using unsupervised methods is feasible. Further, the paper also identifies future directions of developing effective MT for Manipuri-English language pair under unsupervised scenarios.

2020

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Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding
Loitongbam Gyanendro Singh | Anasua Mitra | Sanasam Ranbir Singh
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.

2016

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Automatic Syllabification for Manipuri language
Loitongbam Gyanendro Singh | Lenin Laitonjam | Sanasam Ranbir Singh
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Development of hand crafted rule for syllabifying words of a language is an expensive task. This paper proposes several data-driven methods for automatic syllabification of words written in Manipuri language. Manipuri is one of the scheduled Indian languages. First, we propose a language-independent rule-based approach formulated using entropy based phonotactic segmentation. Second, we project the syllabification problem as a sequence labeling problem and investigate its effect using various sequence labeling approaches. Third, we combine the effect of sequence labeling and rule-based method and investigate the performance of the hybrid approach. From various experimental observations, it is evident that the proposed methods outperform the baseline rule-based method. The entropy based phonotactic segmentation provides a word accuracy of 96%, CRF (sequence labeling approach) provides 97% and hybrid approach provides 98% word accuracy.