We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic.
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.
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.
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).
Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT).
This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages.
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.
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.
This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022).
no code implementations • • 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.
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.