Search Results for author: Navonil Majumder

Found 33 papers, 23 papers with code

Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering

1 code implementation RANLP 2021 Tapas Nayak, Navonil Majumder, Soujanya Poria

Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.

Relation Extraction

M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

1 code implementation3 Aug 2021 Dushyant Singh Chauhan, Gopendra Vikram Singh, Navonil Majumder, Amir Zadeh, Asif Ekbal, Pushpak Bhattacharyya, Louis-Philippe Morency, Soujanya Poria

We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.

Dialogue Understanding

Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication

1 code implementation22 Jun 2021 Navonil Majumder, Deepanway Ghosal, Devamanyu Hazarika, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria

We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.

Empathetic Response Generation Passage Retrieval +1

Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey

no code implementations31 Mar 2021 Tapas Nayak, Navonil Majumder, Pawan Goyal, Soujanya Poria

Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very difficult to keep track of so many papers.

Relation Extraction Word Embeddings

Recognizing Emotion Cause in Conversations

1 code implementation22 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.

Causal Emotion Entailment Emotion Cause Extraction

Improving Zero Shot Learning Baselines with Commonsense Knowledge

no code implementations11 Dec 2020 Abhinaba Roy, Deepanway Ghosal, Erik Cambria, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Zero shot learning -- the problem of training and testing on a completely disjoint set of classes -- relies greatly on its ability to transfer knowledge from train classes to test classes.

Word Embeddings Zero-Shot Learning

Persuasive Dialogue Understanding: the Baselines and Negative Results

no code implementations19 Nov 2020 Hui Chen, Deepanway Ghosal, Navonil Majumder, Amir Hussain, Soujanya Poria

Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.

Dialogue Understanding Intent Recognition +4

MIME: MIMicking Emotions for Empathetic Response Generation

1 code implementation EMNLP 2020 Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria

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.

Empathetic Response Generation Response Generation

Investigating Gender Bias in BERT

no code implementations10 Sep 2020 Rishabh Bhardwaj, Navonil Majumder, Soujanya Poria

As a result, predictions of downstream NLP models can vary noticeably by varying gender words, such as replacing "he" to "she", or even gender-neutral words.

text-classification Text Classification +1

Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks

1 code implementation10 Sep 2020 Hui Chen, Pengfei Hong, Wei Han, Navonil Majumder, Soujanya Poria

This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.

Ranked #7 on Dialog Relation Extraction on DialogRE (F1c (v1) metric)

Dialog Relation Extraction Graph Attention +1

Improving Aspect-Level Sentiment Analysis with Aspect Extraction

no code implementations3 May 2020 Navonil Majumder, Rishabh Bhardwaj, Soujanya Poria, Amir Zadeh, Alexander Gelbukh, Amir Hussain, Louis-Philippe Morency

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).

Aspect Extraction Word Embeddings

KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis

1 code implementation ACL 2020 Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis.

Domain Adaptation Sentiment Analysis

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

2 code implementations IJCNLP 2019 Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh

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.

Emotion Classification Emotion Recognition in Conversation

Variational Fusion for Multimodal Sentiment Analysis

no code implementations13 Aug 2019 Navonil Majumder, Soujanya Poria, Gangeshwar Krishnamurthy, Niyati Chhaya, Rada Mihalcea, Alexander Gelbukh

Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.

Multimodal Sentiment Analysis Question Answering

Recent Trends in Deep Learning Based Personality Detection

no code implementations7 Aug 2019 Yash Mehta, Navonil Majumder, Alexander Gelbukh, Erik Cambria

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.

BIG-bench Machine Learning Personality Trait Recognition

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2 code implementations1 Nov 2018 Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria

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.

Emotion Classification Emotion Recognition in Conversation +2

A Deep Learning Approach for Multimodal Deception Detection

no code implementations1 Mar 2018 Gangeshwar Krishnamurthy, Navonil Majumder, Soujanya Poria, Erik Cambria

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications.

Deception Detection

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