Search Results for author: Soujanya Poria

Found 106 papers, 72 papers with code

CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French

no code implementations EMNLP 2020 AmirAli Bagher Zadeh, Yansheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency

It covers a diverse set topics and speakers, and carries supervision of 20 labels including sentiment (and subjectivity), emotions, and attributes.

Knowledge Enhanced Reflection Generation for Counseling Dialogues

no code implementations ACL 2022 Siqi Shen, Veronica Perez-Rosas, Charles Welch, Soujanya Poria, Rada Mihalcea

We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses.


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 +1

Contrastive Chain-of-Thought Prompting

1 code implementation15 Nov 2023 Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, Lidong Bing

Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.

Language Modelling valid

Mustango: Toward Controllable Text-to-Music Generation

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

With recent advancements in text-to-audio and text-to-music based on latent diffusion models, the quality of generated content has been reaching new heights.

Data Augmentation Denoising +4

Adapter Pruning using Tropical Characterization

no code implementations30 Oct 2023 Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

Adapters are widely popular parameter-efficient transfer learning approaches in natural language processing that insert trainable modules in between layers of a pre-trained language model.

Language Modelling Transfer Learning

Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

1 code implementation22 Oct 2023 Rishabh Bhardwaj, Soujanya Poria

On open-source models such as VICUNA-7B and LLAMA-2-CHAT 7B AND 13B, it shows an attack success rate of more than 91%.

Language Modelling

MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks

1 code implementation13 Oct 2023 Xiaocui Yang, Wenfang Wu, Shi Feng, Ming Wang, Daling Wang, Yang Li, Qi Sun, Yifei Zhang, XiaoMing Fu, Soujanya Poria

Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.

Large Language Models for Automated Open-domain Scientific Hypotheses Discovery

1 code implementation6 Sep 2023 Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria

Raw web corpora that are necessary for developing hypotheses in the published papers are also collected in the dataset, with the final goal of creating a system that automatically generates valid, novel, and helpful (to human researchers) hypotheses, given only a pile of raw web corpora.


Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment

1 code implementation18 Aug 2023 Rishabh Bhardwaj, Soujanya Poria

In this work, we propose a new safety evaluation benchmark RED-EVAL that carries out red-teaming.

Text Generation

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

INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models

2 code implementations7 Jun 2023 Yew Ken Chia, Pengfei Hong, Lidong Bing, Soujanya Poria

Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents.

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

Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction

no code implementations23 May 2023 Yew Ken Chia, Hui Chen, Wei Han, Guizhen Chen, Sharifah Mahani Aljunied, Soujanya Poria, Lidong Bing

Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

Beyond Words: A Comprehensive Survey of Sentence Representations

no code implementations22 May 2023 Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria

In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques.

Question Answering Representation Learning +4

Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction

1 code implementation18 May 2023 Qi Sun, Kun Huang, Xiaocui Yang, Pengfei Hong, Kun Zhang, Soujanya Poria

Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction.

Denoising Document-level Relation Extraction

ReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation

no code implementations4 May 2023 Pengfei Hong, Rishabh Bhardwaj, Navonil Majumdar, Somak Aditya, Soujanya Poria

Our experiments empirically show that the counterfactual samples sourced from our masked text lead to improved domain transfer on 10 out of 12 domain sentiment classification settings, with an average of 2% accuracy improvement over the state-of-the-art for unsupervised domain adaptation (UDA).

counterfactual intent-classification +5

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

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

2 code implementations4 Apr 2023 Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Ka-Wei Lee

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e. g., ChatDoctor) or instruction data (e. g., Alpaca).

Arithmetic Reasoning Language Modelling

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

UDApter -- Efficient Domain Adaptation Using Adapters

1 code implementation7 Feb 2023 Bhavitvya Malik, Abhinav Ramesh Kashyap, Min-Yen Kan, Soujanya Poria

We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0. 85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters.

Language Modelling Natural Language Inference +3

A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach

1 code implementation18 Nov 2022 Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria

Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.

graph construction Hyper-Relational Extraction

Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts

1 code implementation12 Nov 2022 Xiaocui Yang, Shi Feng, Daling Wang, Pengfei Hong, Soujanya Poria

To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.

Language Modelling Multimodal Sentiment Analysis

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 +1

SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training

1 code implementation23 Oct 2022 Hui Chen, Wei Han, Soujanya Poria

Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning.

Pseudo Label Semi-Supervised Text Classification

MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences

1 code implementation23 Oct 2022 Wei Han, Hui Chen, Min-Yen Kan, Soujanya Poria

Existing multimodal tasks mostly target at the complete input modality setting, i. e., each modality is either complete or completely missing in both training and test sets.

Denoising Imputation

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

DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

1 code implementation COLING 2022 Hui Chen, Wei Han, Diyi Yang, Soujanya Poria

This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification.

Text Augmentation text-classification +1

SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

1 code implementation COLING 2022 Wei Han, Hui Chen, Zhen Hai, Soujanya Poria, Lidong Bing

With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.

Contrastive Learning

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

Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding

1 code implementation23 May 2022 Rishabh Bhardwaj, Amrita Saha, Steven C. H. Hoi, Soujanya Poria

VIP particularly focuses on two aspects -- contextual prompts that learns input-specific contextualization of the soft prompt tokens through a small-scale sentence encoder and quantized prompts that maps the contextualized prompts to a set of learnable codebook vectors through a Vector quantization network.

Natural Language Understanding NER +2

KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks

2 code implementations COLING 2022 Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network.

Emotion Recognition in Conversation Knowledge Distillation +4

PIP: Physical Interaction Prediction via Mental Simulation with Span Selection

no code implementations10 Sep 2021 Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, Cheston Tan

However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects.

Friction Semantic Object Interaction Classification

Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis

2 code implementations EMNLP 2021 Wei Han, Hui Chen, Soujanya Poria

In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion.

Multimodal Sentiment Analysis

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 Extraction

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

Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis

2 code implementations28 Jul 2021 Wei Han, Hui Chen, Alexander Gelbukh, Amir Zadeh, Louis-Philippe Morency, Soujanya Poria

Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.

Multimodal Deep Learning 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

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 Word Embeddings

Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

no code implementations4 Jan 2021 Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents.

Machine Reading Comprehension Open-Domain Question Answering

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.

Dialogue Understanding Intent Recognition +5

MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences

1 code implementation NAACL 2021 Jianing Yang, Yongxin Wang, Ruitao Yi, Yuying Zhu, Azaan Rehman, Amir Zadeh, Soujanya Poria, Louis-Philippe Morency

Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed.

Emotion Recognition Multimodal Sentiment Analysis

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.

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

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

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 +1

Discriminative Dictionary Design for Action Classification in Still Images and Videos

no code implementations20 May 2020 Abhinaba Roy, Biplab Banerjee, Amir Hussain, Soujanya Poria

Specifically, we pose the selection of potent local descriptors as filtering based feature selection problem which ranks the local features per category based on a novel measure of distinctiveness.

Action Classification Action Recognition +2

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

Factorized Multimodal Transformer for Multimodal Sequential Learning

no code implementations22 Nov 2019 Amir Zadeh, Chengfeng Mao, Kelly Shi, Yiwei Zhang, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency

As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area.

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 Transfer Learning

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

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

Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning

no code implementations23 Jan 2019 Haiyun Peng, Yukun Ma, Soujanya Poria, Yang Li, Erik Cambria

Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.

Chinese Sentiment Analysis reinforcement-learning +2

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

Anaphora and Coreference Resolution: A Review

no code implementations30 May 2018 Rhea Sukthanker, Soujanya Poria, Erik Cambria, Ramkumar Thirunavukarasu

Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research.

coreference-resolution Entity Resolution +3

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

Memory Fusion Network for Multi-view Sequential Learning

2 code implementations3 Feb 2018 Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, Louis-Philippe Morency

In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time.

Basic tasks of sentiment analysis

no code implementations18 Oct 2017 Iti Chaturvedi, Soujanya Poria, Erik Cambria

Subjectivity detection is the task of identifying objective and subjective sentences.

Aspect Extraction Sentiment Analysis

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

Tensor Fusion Network for Multimodal Sentiment Analysis

1 code implementation EMNLP 2017 Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis-Philippe Morency

Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language.

Multimodal Sentiment Analysis

Developing a concept-level knowledge base for sentiment analysis in Singlish

no code implementations14 Jul 2017 Rajiv Bajpai, Soujanya Poria, Danyun Ho, Erik Cambria

In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value.

Common Sense Reasoning Graph Mining +1

SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

no code implementations COLING 2016 Erik Cambria, Soujanya Poria, Rajiv Bajpai, Bjoern Schuller

An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions.

Clustering Dimensionality Reduction +1

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