Mayank Jobanputra


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OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches
Mayank Jobanputra | Lorena Martín Rodríguez
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

We investigate the capabilities of pre-trained models, without any fine-tuning, for a document-level multilingual news similarity task of SemEval-2022. We utilize title and news content with appropriate pre-processing techniques. Our system derives 14 different similarity features using a combination of state-of-the-art methods (MPNet) with well-known statistical methods (i.e. TF-IDF, Word Mover’s distance). We formulate multilingual news similarity task as a regression task and approximate the overall similarity between two news articles using these features. Our best-performing system achieved a correlation score of 70.1% and was ranked 20th among the 34 participating teams. In this paper, in addition to a system description, we also provide further analysis of our results and an ablation study highlighting the strengths and limitations of our features. We make our code publicly available at

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Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Gowtham Ramesh | Sumanth Doddapaneni | Aravinth Bheemaraj | Mayank Jobanputra | Raghavan AK | Ajitesh Sharma | Sujit Sahoo | Harshita Diddee | Mahalakshmi J | Divyanshu Kakwani | Navneet Kumar | Aswin Pradeep | Srihari Nagaraj | Kumar Deepak | Vivek Raghavan | Anoop Kunchukuttan | Pratyush Kumar | Mitesh Shantadevi Khapra
Transactions of the Association for Computational Linguistics, Volume 10

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly available parallel corpora, and additionally mine 37.4 million sentence pairs from the Web, resulting in a 4× increase. We mine the parallel sentences from the Web by combining many corpora, tools, and methods: (a) Web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at Samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.


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Unsupervised Question Answering for Fact-Checking
Mayank Jobanputra
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural language tasks such as question-answering, natural language inference (NLI), and textual entailment. These tasks not only require the contextual knowledge but also the reasoning abilities to be solved efficiently. In this paper, we propose an unsupervised question-answering based approach for a similar task, fact-checking. We transform the FEVER dataset into a Cloze-task by masking named entities provided in the claims. To predict the answer token, we utilize pre-trained Bidirectional Encoder Representations from Transformers (BERT). The classifier computes label based on the correctly answered questions and a threshold. Currently, the classifier is able to classify the claims as “SUPPORTS” and “MANUAL_REVIEW”. This approach achieves a label accuracy of 80.2% on the development set and 80.25% on the test set of the transformed dataset.