We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022.
This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models.
Hope is an inherent part of human life and essential for improving the quality of life.
In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization.
This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language.
This overview paper describes the first shared task on fake news detection in Urdu language.
In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide.
Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different.
This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language.
According to the World health organization (WHO), approximately 450 million people are affected.
In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isnt much emphasis on psycho-linguistics analyses of social media text.
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
1 code implementation • 22 Dec 2020 • Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea
We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.
Ranked #1 on Causal Emotion Entailment on RECCON
The second model uses word embedding representation to extract the neighbor's relative distances across spaces and propose "the average of absolute differences" to estimate lexical semantic change.
We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020.
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #8 on Emotion Recognition in Conversation on EmoryNLP
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly.
We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.
Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA).
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Ranked #1 on Emotion Recognition in Conversation on SEMAINE
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches.
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure.
We compile baselines, along with dataset split, for multimodal sentiment analysis.