Search Results for author: Subhadarshi Panda

Found 11 papers, 2 papers with code

Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings

1 code implementation NAACL (NLP4IF) 2021 Subhadarshi Panda, Sarah Ita Levitan

We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic.

Misinformation

Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation

no code implementations ACL 2022 Subhadarshi Panda, Frank Palma Gomez, Michael Flor, Alla Rozovskaya

In a fill-in-the-blank exercise, a student is presented with a carrier sentence with one word hidden, and a multiple-choice list that includes the correct answer and several inappropriate options, called distractors.

Machine Translation Multiple-choice +1

Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection

no code implementations ACL 2022 Subhadarshi Panda, Sarah Ita Levitan

With the increase of deception and misinformation especially in social media, it has become crucial to be able to develop machine learning methods to automatically identify deceptive language.

Classification Deception Detection +2

NLPHut’s Participation at WAT2021

no code implementations ACL (WAT) 2021 Shantipriya Parida, Subhadarshi Panda, Ketan Kotwal, Amulya Ratna Dash, Satya Ranjan Dash, Yashvardhan Sharma, Petr Motlicek, Ondřej Bojar

Our submission tops in English→Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English→Hindi Multimodal translation task (text-only translation, and Hindi caption).

Image Captioning Translation

Silo NLP's Participation at WAT2022

1 code implementation2 Aug 2022 Shantipriya Parida, Subhadarshi Panda, Stig-Arne Grönroos, Mark Granroth-Wilding, Mika Koistinen

This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022).

Translation

Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation

no code implementations LREC 2022 Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud, Shantipriya Parida, Shamsuddeen Hassan Muhammad, Ibrahim Sa'id Ahmad, Subhadarshi Panda, Ondřej Bojar, Bashir Shehu Galadanci, Bello Shehu Bello

The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks.

Machine Translation Translation

Shuffled-token Detection for Refining Pre-trained RoBERTa

no code implementations NAACL 2021 Subhadarshi Panda, Anjali Agrawal, Jeewon Ha, Benjamin Bloch

Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks.

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