Papers by Devamanyu Hazarika
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (N18-2)
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Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
| Challenge: | Present neural-based models exploit aspect and its contextual information in the sentence but ignore inter-aspect dependencies. |
| Approach: | They propose to combine aspect-based sentiment analysis with temporal dependency processing to incorporate this pattern into a sentence. |
| Outcome: | The proposed approach is based on the SemEval 2014 dataset and shows that it is effective for predicting sentiments of aspects in sentences with multiple aspects. |
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer (2022.acl-long)
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| Challenge: | Automated transfer of text between domains does not maintain other attributes between the source and translated text. |
| Approach: | They propose a method for automatic transfer of text between domains that preserves semantic content but changes other attributes. |
| Outcome: | The proposed method retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. |
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. |
Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs (2025.emnlp-main)
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| Challenge: | a zero-shot merging framework for large language models consolidates specialized domain experts into a single model without any further training. |
| Approach: | They propose a zero-shot merging framework that consolidates specialized domain experts into a single model without further training. |
| Outcome: | Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of the proposed framework. |
Methods for Numeracy-Preserving Word Embeddings (2020.emnlp-main)
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
| Challenge: | Word embedding models capture semantic relationships between words but fail to capture numerical properties associated with numbers. |
| Approach: | They propose a method to assign and learn embeddings for numbers using word embedders. |
| Outcome: | The proposed model outperforms pre-trained word embedding models across multiple examples of two tasks. |
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (D18-1)
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| Challenge: | Existing studies do not explicitly consider inter-personal influences that thrive in the emotional dynamics of dialogues. |
| Approach: | They propose a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. |
| Outcome: | The proposed model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets. |
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (P19-1)
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
| Challenge: | sarcasm is often expressed through multiple verbal and non-verbal cues, such as a change of tone, overemphasis, drawn-out syllables, or a straight looking face. |
| Approach: | They propose to use multimodal cues to improve sarcasm detection using audiovisual utterances annotated with sarcasm labels to improve the accuracy. |
| Outcome: | The proposed dataset reduces the error rate of sarcasm detection by 12.9% . it is based on audiovisual utterances annotated with sarcasm labels . |
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)
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| Challenge: | Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task . |
| Approach: | They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun . |
| Outcome: | The proposed method improves prefix-tuning performance by training only a small portion of parameters. |
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)
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| Challenge: | a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities . |
| Approach: | They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses . |
| Outcome: | The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response. |
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient. |
| Approach: | They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters. |
| Outcome: | The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks. |
Analyzing Modality Robustness in Multimodal Sentiment Analysis (2022.naacl-main)
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| Challenge: | despite its importance, little attention has been paid to improving the robustness of multimodal models. |
| Approach: | They propose simple diagnostic checks for modality robustness in a trained multimodal model . they find MSA models highly sensitive to a single modality, which creates issues . |
| Outcome: | The proposed checks show that models are highly sensitive to a single modality, which creates issues in their robustness. |
Using In-Context Learning to Improve Dialogue Safety (2023.findings-emnlp)
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Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Recent work has highlighted safety issues with large neural-based conversational models. |
| Approach: | They propose a retrieval-based approach for reducing bias and toxicity in chatbot responses . they retrieve demonstrations of safe responses to similar dialogue contexts to generate a response . |
| Outcome: | The proposed method reduces bias and toxicity in three chatbot models . it can be used in compliment to existing dialogue safety approaches, such as RLHF. |
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. |
Domain Divergences: A Survey and Empirical Analysis (2021.naacl-main)
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| Challenge: | Existing literature on divergence measures is lacking in predicting performance of models in new domains. |
| Approach: | They propose a taxonomy of divergence measures consisting of three classes — Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them. |
| Outcome: | The proposed measures are based on three novel use-cases and identify that they are prevalent in three domains and higher-order measures are more common in two. |
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information (2023.eacl-main)
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Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Intent detection is a fundamental element in task-oriented dialogue systems, usually occurring within the Natural Language Understanding component. |
| Approach: | They propose an in-context data augmentation approach that fine-tunes a pre-trained language model and synthesizes new datapoints that correspond to given intents. |
| Outcome: | The proposed method produces training data that achieves state-of-the-art on three challenging intent detection datasets and performs on par with the state- of-the art in full-shot settings. |
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums (C18-1)
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| Challenge: | Existing studies on sarcasm detection focus on lexical, syntactic and semantic cues, but sarcasm can be expressed implicitly without such cue. |
| Approach: | They propose a ContextuAl SarCasm DEtector which extracts contextual information from the discourse of a discussion thread. |
| Outcome: | The proposed model improves on a large Reddit corpus. |
KILM: Knowledge Injection into Encoder-Decoder Language Models (2023.acl-long)
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| Challenge: | Large pre-trained language models retain implicit knowledge within their parameters, but are susceptible to memorizing the pretraining corpora rather than capturing the knowledge within them. |
| Approach: | They propose to inject entity-related knowledge into encoder-decoder PLMs via a generative knowledge infilling objective through continued pre-training. |
| Outcome: | The proposed approach outperforms state-of-the-art models on general NLU and NLG tasks while maintaining their original performance. |
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs (2023.emnlp-main)
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Taha Aksu, Devamanyu Hazarika, Shikib Mehri, Seokhwan Kim, Dilek Hakkani-Tur, Yang Liu, Mahdi Namazifar
| Challenge: | Instruction-based multitasking has played a critical role in the success of large language models (LLMs) when exposed to complex instructions with multiple constraints, they lag against state-of-the-art models like ChatGPT. |
| Approach: | They propose a framework that unifies a large number of dialog tasks in the same format and allows programmatic induction of complex instructions without manual effort. |
| Outcome: | The proposed framework unifies a large number of dialog tasks in the same format and allows programmatic induction of complex instructions without manual effort. |
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (N18-1)
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Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
| Challenge: | Existing methods for recognizing emotions in conversations ignore inter-speaker dependency relations . dyadic conversations are a form of dialogue between two entities . |
| Approach: | They propose a deep neural framework which leverages contextual information from the conversation history to model past utterances of each speaker into memories. |
| Outcome: | The proposed framework improves by 3 4% over the state-of-the-art in recognizing emotions in dyadic conversational videos. |