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Found 10 papers, 5 papers with code
Hopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset.
“Did you really mean what you said?” : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence.
Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models
Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given.
AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets
Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.
Hopeful_Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
Our solution got a weighted F1 score of 0. 93, 0. 75 and 0. 49 for English, Malayalam and Tamil respectively.
Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach
In the last few years, emotion detection in social-media text has become a popular problem due to its wide ranging application in better understanding the consumers, in psychology, in aiding human interaction with computers, designing smart systems etc.
Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT
This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI).
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings
We were ranked first and second, in terms of partial and exact match, respectively.
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence.
Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting
This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets.
