Search Results for author: Devamanyu Hazarika

Found 38 papers, 24 papers with code

Causal Augmentation for Causal Sentence Classification

1 code implementation EMNLP (CINLP) 2021 Fiona Anting Tan, Devamanyu Hazarika, See-Kiong Ng, Soujanya Poria, Roger Zimmermann

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.

Classification counterfactual +3

Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language

no code implementations24 Nov 2023 Di Jin, Shikib Mehri, Devamanyu Hazarika, Aishwarya Padmakumar, Sungjin Lee, Yang Liu, Mahdi Namazifar

Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations.

KILM: Knowledge Injection into Encoder-Decoder Language Models

1 code implementation17 Feb 2023 Yan Xu, Mahdi Namazifar, Devamanyu Hazarika, Aishwarya Padmakumar, Yang Liu, Dilek Hakkani-Tür

Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.

Entity Disambiguation

Role of Bias Terms in Dot-Product Attention

no code implementations16 Feb 2023 Mahdi Namazifar, Devamanyu Hazarika, Dilek Hakkani-Tur

Moreover, we argue that the bias term of the value linear transformation has a more prominent role than that of the bias term of the query linear transformation.

Language Modelling Natural Language Understanding +1

Using In-Context Learning to Improve Dialogue Safety

no code implementations2 Feb 2023 Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tür

For instance, using automatic evaluation, we find our best fine-tuned baseline only generates safe responses to unsafe dialogue contexts from DiaSafety 4. 04% more than our approach.

In-Context Learning Re-Ranking +1

Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning

1 code implementation26 Oct 2022 Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning.

Language Modelling Natural Language Understanding +1

Analyzing Modality Robustness in Multimodal Sentiment Analysis

1 code implementation NAACL 2022 Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao, Roger Zimmermann, Soujanya Poria

In this work, we hope to address that by (i) Proposing simple diagnostic checks for modality robustness in a trained multimodal model.

Multimodal Sentiment Analysis

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

Zero-Shot Controlled Generation with Encoder-Decoder Transformers

no code implementations11 Jun 2021 Devamanyu Hazarika, Mahdi Namazifar, Dilek Hakkani-Tür

In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero-shot.

Document Summarization Machine Translation +1

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

Multimodal Research in Vision and Language: A Review of Current and Emerging Trends

no code implementations19 Oct 2020 Shagun Uppal, Sarthak Bhagat, Devamanyu Hazarika, Navonil Majumdar, Soujanya Poria, Roger Zimmermann, Amir Zadeh

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data.

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

Conversational Transfer Learning for Emotion Recognition

1 code implementation11 Oct 2019 Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea

We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).

Emotion Recognition in Conversation Sentence +1

Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)

1 code implementation ACL 2019 Santiago Castro, Devamanyu Hazarika, Ver{\'o}nica P{\'e}rez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria

As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.

Sarcasm Detection

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

1 code implementation5 Jun 2019 Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria

As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.

Sarcasm Detection

Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining

no code implementations22 Feb 2019 Rajiv Bajpai, Devamanyu Hazarika, Kunal Singh, Sruthi Gorantla, Erik Cambria, Roger Zimmerman

With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task.

Opinion Mining Sentiment Analysis

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

Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation

no code implementations30 Aug 2017 Amarjot Singh, Devamanyu Hazarika, Aniruddha Bhattacharya

Automation of brain matter segmentation from MR images is a challenging task due to the irregular boundaries between the grey and white matter regions.


Recent Trends in Deep Learning Based Natural Language Processing

3 code implementations9 Aug 2017 Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria

Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains.

Benchmarking Multimodal Sentiment Analysis

no code implementations29 Jul 2017 Erik Cambria, Devamanyu Hazarika, Soujanya Poria, Amir Hussain, R. B. V. Subramaanyam

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities.

Benchmarking Emotion Recognition +1

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