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Found 12 papers, 2 papers with code
Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings
We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic.
Silo NLP’s Participation at WAT2022
This paper provides the system description of “Silo NLP’s” submission to the Workshop on Asian Translation (WAT2022).
Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)
This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages.
Multimodal Neural Machine Translation System for English to Bengali
Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT).
HunterSpeechLab at GermEval 2021: Does Your Comment Claim A Fact? Contextualized Embeddings for German Fact-Claiming Comment Classification
In this paper we investigate the efficacy of using contextual embeddings from multilingual BERT and German BERT in identifying fact-claiming comments in German on social media.
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems
As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language.
NLPHut’s Participation at WAT2021
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).
Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection
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.
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation
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.
Silo NLP's Participation at WAT2022
This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022).
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
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.
Shuffled-token Detection for Refining Pre-trained RoBERTa
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.
