Papers by Navonil Majumder
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (P19-1)
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| Challenge: | Emotion recognition in conversations has gained popularity due to its potential applications. Until now, a large multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. |
| Approach: | They propose to extend and enhance EmotionLines by combining 13,000 utterances from Friends dialogues with emotion and sentiment labels. |
| Outcome: | The proposed dataset contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. |
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020.findings-emnlp)
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| Challenge: | Current methods for emotion recognition in conversations often face difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. |
| Approach: | They propose a framework that incorporates mental states, events, and causal relations to learn interactions between interlocutors participating in a conversation. |
| Outcome: | The proposed framework improves on four conversational benchmark datasets. |
Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study (2021.findings-acl)
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| Challenge: | utterance-level dialogue understanding tasks are often performed at utterrance level and are often conjoined together under the umbrella of utterence-level dialog understanding. |
| Approach: | They propose to use a contextual utterance-level dialogue understanding baseline as a strong framework for six dialogue-understanding tasks. |
| Outcome: | The proposed framework can be easily adapted for other tasks for similar purposes. |
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts (2023.findings-emnlp)
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| Challenge: | Visual question answering (VQA) is a task that requires an understanding of both the image and the question to provide a natural language answer. |
| Approach: | They propose a multimodal framework that leverages language guidance to answer questions more accurately. |
| Outcome: | The proposed framework improves on the multi-choice question-answering task using CLIP and BLIP models. |
MIME: MIMicking Emotions for Empathetic Response Generation (2020.emnlp-main)
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Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
| Challenge: | Empathy is a fundamental human trait that reflects our ability to understand and reflect the thoughts and feelings of the people we interact with. |
| Approach: | They propose to use polarity-based emotion clusters to generate empathetic responses . they also introduce stochasticity into the emotion mixture that yields emotionally more varied responses compared to the previous work . |
| Outcome: | The proposed methods improve empathy and contextual relevance of the response, and introduce stochasticity into the emotion mixture that yields emotionally more varied responses than the previous work. |
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |
More Identifiable yet Equally Performant Transformers for Text Classification (2021.acl-long)
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| Challenge: | Current studies prove that attention weights are not unique and therefore unfit for interpretation. |
| Approach: | They propose a transformer encoder layer that decouples the relationship between key and value vector and provides identifiable weights up to the desired length of the input. |
| Outcome: | The proposed model is more identifiable than previously thought but still prone to be non-unique attentions that make them unfit for interpretation. |
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues (2022.acl-long)
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| Challenge: | Fig. 1a shows an example where commonsense knowledge is crucial in sifting relevant information from the context. |
| Approach: | They curate a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, subsequent event, prerequisite, motivation, and emotional reaction. |
| Outcome: | The dataset contains 53,105 of such inferences from 5,672 dialogues. |
STaCK: Sentence Ordering with Temporal Commonsense Knowledge (2021.emnlp-main)
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| Challenge: | Existing approaches to sentence order prediction ignore the importance of document level global information, i.e., while predicting relative order of two sentences (s i , s j) other sentences sk from the same document do not play any role. |
| Approach: | They propose a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict relative order of sentences. |
| Outcome: | The proposed method is naturally suitable for order prediction on five different datasets and has potential applications in the evaluation of the quality of machinegenerated documents. |
Evaluating LLMs’ Mathematical and Coding Competency through Ontology-guided Interventions (2025.findings-acl)
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| Challenge: | Current large language models have shown impressive performance on logical reasoning benchmarks . however, the true depth of their competencies and robustness in reasoning tasks remains an open question . |
| Approach: | They propose a general ontology of perturbations and a semi-automatic method to apply perturbations to arithmetic reasoning and code generation datasets to test their LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing models on arithmetic reasoning and code generation tasks. |
A Robust Information-Masking Approach for Domain Counterfactual Generation (2023.findings-acl)
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| Challenge: | Domain shift is a big challenge in NLP, but many approaches fail to leverage domain-specific nuances relevant to the task at hand. |
| Approach: | They propose a method that uses frequency-based masking to transform a text from the source domain to a target domain. |
| Outcome: | The proposed method outperforms baselines on 10 out of 12 domain-counterfactual classification settings with an average of 1.7% improvement in accuracy metric. |
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (D19-1)
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
| Outcome: | The proposed method outperforms the current state-of-the-art on a number of benchmark emotion classification datasets while minimizing context propagation issues. |
Mustango: Toward Controllable Text-to-Music Generation (2024.naacl-long)
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| Challenge: | Mustango is a text-to-music system that allows music-domain-knowledge-informed text-based music generation. |
| Approach: | They propose a music-domain-knowledge-inspired text-to-music system based on diffusion that generates music with captions that include specific instructions related to chords, beats, key and tempo. |
| Outcome: | The proposed system outperforms existing models in music generation tasks. |
KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis (2020.acl-main)
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| Challenge: | Existing approaches to cross-domain sentiment analysis cannot be reliably deployed due to the distributional mismatch between training and evaluation domains. |
| Approach: | They propose a framework that uses ConceptNet to enrich semantics of documents by providing domain-specific and domain-general background concepts. |
| Outcome: | The proposed framework improves on a domain-adversarial baseline method and can be used in domain adaptation. |
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering (2022.emnlp-main)
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| Challenge: | Existing approaches to multi-choice question answering are based on binary classifications instead of scoring each answer as a single class. |
| Approach: | They propose a simple refactoring of multi-choice question answering tasks as a series of binary classifications and propose re-framing to make them more efficient. |
| Outcome: | The proposed approach is significantly more effective across different tasks and models. |
kNN-CM: A Non-parametric Inference-Phase Adaptation of Parametric Text Classifiers (2023.findings-emnlp)
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| Challenge: | Existing studies on text-discriminating properties of semi-parametric models have not been done on non-parameter models. |
| Approach: | They propose an inference-phase approach that incorporates a neighborhood search into a model to enhance the capacity of a pre-trained parametric text classifier. |
| Outcome: | The proposed model improves performance on eight SuperGLUE tasks, three adversarial natural language inference datasets, 11 question-answering (QA) datasets and two sentiment classification datasets. |
Reward-Guided Tree Search for Inference Time Alignment of Large Language Models (2025.naacl-long)
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| Challenge: | Inference-time computation methods enhance performance of Large Language Models by leveraging additional computational resources. |
| Approach: | They propose an inference-time alignment method that leverages a reward model to achieve alignment through reward-guided tree search. |
| Outcome: | The proposed method outperforms other inference-time alignment methods on two benchmarks . it achieves comparable performance to preference-tuned models on both benchmarks, authors show . |