Navonil Majumder


2022

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CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
Deepanway Ghosal | Siqi Shen | Navonil Majumder | Rada Mihalcea | Soujanya Poria
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper addresses the problem of dialogue reasoning with contextualized commonsense inference. We curate CICERO, a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, subsequent event, prerequisite, motivation, and emotional reaction. The dataset contains 53,105 of such inferences from 5,672 dialogues. We use this dataset to solve relevant generative and discriminative tasks: generation of cause and subsequent event; generation of prerequisite, motivation, and listener’s emotional reaction; and selection of plausible alternatives. Our results ascertain the value of such dialogue-centric commonsense knowledge datasets. It is our hope that CICERO will open new research avenues into commonsense-based dialogue reasoning.

2021

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CIDER: Commonsense Inference for Dialogue Explanation and Reasoning
Deepanway Ghosal | Pengfei Hong | Siqi Shen | Navonil Majumder | Rada Mihalcea | Soujanya Poria
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing. Explaining human conversations poses a great challenge as it requires contextual understanding, planning, inference, and several aspects of reasoning including causal, temporal, and commonsense reasoning. In this work, we introduce CIDER – a manually curated dataset that contains dyadic dialogue explanations in the form of implicit and explicit knowledge triplets inferred using contextual commonsense inference. Extracting such rich explanations from conversations can be conducive to improving several downstream applications. The annotated triplets are categorized by the type of commonsense knowledge present (e.g., causal, conditional, temporal). We set up three different tasks conditioned on the annotated dataset: Dialogue-level Natural Language Inference, Span Extraction, and Multi-choice Span Selection. Baseline results obtained with transformer-based models reveal that the tasks are difficult, paving the way for promising future research. The dataset and the baseline implementations are publicly available at https://github.com/declare-lab/CIDER.

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STaCK: Sentence Ordering with Temporal Commonsense Knowledge
Deepanway Ghosal | Navonil Majumder | Rada Mihalcea | Soujanya Poria
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order. In this paper, we introduce STaCK — a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences. Our graph network accumulates temporal evidence using knowledge of ‘past’ and ‘future’ and formulates sentence ordering as a constrained edge classification problem. We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction. The implementation of this work is available at: https://github.com/declare-lab/sentence-ordering.

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Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study
Deepanway Ghosal | Navonil Majumder | Rada Mihalcea | Soujanya Poria
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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More Identifiable yet Equally Performant Transformers for Text Classification
Rishabh Bhardwaj | Navonil Majumder | Soujanya Poria | Eduard Hovy
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transformer’s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them unfit for interpretation. To tackle this issue, we provide a variant of the encoder layer that decouples the relationship between key and value vector and provides identifiable weights up to the desired length of the input. We prove the applicability of such variations by providing empirical justifications on varied text classification tasks. The implementations are available at https://github.com/declare-lab/identifiable-transformers.

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Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering
Tapas Nayak | Navonil Majumder | Soujanya Poria
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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. In distant supervision, a sentence is considered as a source of a tuple if the sentence contains both entities of the tuple. However, this condition is too permissive and does not guarantee the presence of relevant relation-specific information in the sentence. As such, distantly supervised training data contains much noise which adversely affects the performance of the models. In this paper, we propose a self-ensemble filtering mechanism to filter out the noisy samples during the training process. We evaluate our proposed framework on the New York Times dataset which is obtained via distant supervision. Our experiments with multiple state-of-the-art neural relation extraction models show that our proposed filtering mechanism improves the robustness of the models and increases their F1 scores.

2020

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KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis
Deepanway Ghosal | Devamanyu Hazarika | Abhinaba Roy | Navonil Majumder | Rada Mihalcea | Soujanya Poria
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.

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MIME: MIMicking Emotions for Empathetic Response Generation
Navonil Majumder | Pengfei Hong | Shanshan Peng | Jiankun Lu | Deepanway Ghosal | Alexander Gelbukh | Rada Mihalcea | Soujanya Poria
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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 argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.

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COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
Deepanway Ghosal | Navonil Majumder | Alexander Gelbukh | Rada Mihalcea | Soujanya Poria
Findings of the Association for Computational Linguistics: EMNLP 2020

In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-theart methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.

2019

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DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
Deepanway Ghosal | Navonil Majumder | Soujanya Poria | Niyati Chhaya | Alexander Gelbukh
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Soujanya Poria | Devamanyu Hazarika | Navonil Majumder | Gautam Naik | Erik Cambria | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.

2018

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IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Navonil Majumder | Soujanya Poria | Alexander Gelbukh | Md. Shad Akhtar | Erik Cambria | Asif Ekbal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.

2017

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Context-Dependent Sentiment Analysis in User-Generated Videos
Soujanya Poria | Erik Cambria | Devamanyu Hazarika | Navonil Majumder | Amir Zadeh | Louis-Philippe Morency
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.