Search Results for author: Navonil Majumder

Found 45 papers, 34 papers with code

Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization

1 code implementation15 Apr 2024 Navonil Majumder, Chia-Yu Hung, Deepanway Ghosal, Wei-Ning Hsu, Rada Mihalcea, Soujanya Poria

These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt.

Audio Generation

Caught in the Quicksand of Reasoning, Far from AGI Summit: Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions

1 code implementation17 Jan 2024 Pengfei Hong, Deepanway Ghosal, Navonil Majumder, Somak Aditya, Rada Mihalcea, Soujanya Poria

Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance.

Arithmetic Reasoning Code Generation +3

Mustango: Toward Controllable Text-to-Music Generation

1 code implementation14 Nov 2023 Jan Melechovsky, Zixun Guo, Deepanway Ghosal, Navonil Majumder, Dorien Herremans, Soujanya Poria

Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models such as MusicGen and AudioLDM2.

Data Augmentation Denoising +4

Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning

1 code implementation5 Jul 2023 Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria

Interestingly, despite being introduced four years ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general problem-solving skills.

Language Modelling Large Language Model

ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation

1 code implementation29 May 2023 Ambuj Mehrish, Abhinav Ramesh Kashyap, Li Yingting, Navonil Majumder, Soujanya Poria

There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-represented in the training data.

Speech Synthesis

Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding

1 code implementation20 May 2023 Yi Xuan Tan, Navonil Majumder, Soujanya Poria

The pre-trained speech encoder wav2vec 2. 0 performs very well on various spoken language understanding (SLU) tasks.

Knowledge Distillation Sentence +1

A Review of Deep Learning Techniques for Speech Processing

no code implementations30 Apr 2023 Ambuj Mehrish, Navonil Majumder, Rishabh Bhardwaj, Rada Mihalcea, Soujanya Poria

The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications.

Automatic Speech Recognition Emotion Recognition +4

Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model

1 code implementation24 Apr 2023 Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, Soujanya Poria

The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks.

AudioCaps Audio Generation

Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding

1 code implementation2 Mar 2023 Yingting Li, Ambuj Mehrish, Shuai Zhao, Rishabh Bhardwaj, Amir Zadeh, Navonil Majumder, Rada Mihalcea, Soujanya Poria

To mitigate this issue, parameter-efficient transfer learning algorithms, such as adapters and prefix tuning, have been proposed as a way to introduce a few trainable parameters that can be plugged into large pre-trained language models such as BERT, and HuBERT.

Speech Synthesis Transfer Learning

Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering

1 code implementation29 Oct 2022 Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion.

Binary Classification Science Question Answering +2

Multiview Contextual Commonsense Inference: A New Dataset and Task

1 code implementation6 Oct 2022 Siqi Shen, Deepanway Ghosal, Navonil Majumder, Henry Lim, Rada Mihalcea, Soujanya Poria

Our results show that the proposed pre-training objectives are effective at adapting the pre-trained T5-Large model for the contextual commonsense inference task.

 Ranked #1 on Multiview Contextual Commonsense Inference on CICERO (using extra training data)

Multiview Contextual Commonsense Inference

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 Relation Extraction +1

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 +2

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.

Document-level Relation Extraction Relation +2

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.

Attribute Dialogue Understanding +7

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

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 +2

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

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-Based Sentiment Analysis Aspect Extraction +1

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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