Search Results for author:
Found 45 papers, 34 papers with code
Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization
These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt.
Caught in the Quicksand of Reasoning, Far from AGI Summit: Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions
Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance.
Mustango: Toward Controllable Text-to-Music Generation
Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models such as MusicGen and AudioLDM2.
Ranked #1 on
Text-to-Music Generation
on MusicBench
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts
Visual question answering (VQA) is the task of answering questions about an image.
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning
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.
ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation
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.
Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding
The pre-trained speech encoder wav2vec 2. 0 performs very well on various spoken language understanding (SLU) tasks.
A Review of Deep Learning Techniques for Speech Processing
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.
Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model
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.
Ranked #4 on
Audio Generation
on AudioCaps
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding
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.
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion.
Ranked #2 on
Sentence Completion
on HellaSwag
Multiview Contextual Commonsense Inference: A New Dataset and Task
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)
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs
In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization.
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
This paper addresses the problem of dialogue reasoning with contextualized commonsense inference.
Ranked #1 on
Answer Generation
on CICERO
STaCK: Sentence Ordering with Temporal Commonsense Knowledge
Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document.
Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering
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.
Aspect Sentiment Triplet Extraction Using Reinforcement Learning
We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment.
Ranked #6 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect Sentiment Triplet Extraction
reinforcement-learning
+2
M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.
Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication
We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
More Identifiable yet Equally Performant Transformers for Text Classification
In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights.
CIDER: Commonsense Inference for Dialogue Explanation and Reasoning
Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing.
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
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.
Recognizing Emotion Cause in Conversations
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.
Ranked #1 on
Recognizing Emotion Cause in Conversations
on RECCON
Improving Zero Shot Learning Baselines with Commonsense Knowledge
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.
Persuasive Dialogue Understanding: the Baselines and Negative Results
Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #11 on
Emotion Recognition in Conversation
on DailyDialog
MIME: MIMicking Emotions for Empathetic Response Generation
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.
Utterance-level Dialogue Understanding: An Empirical Study
Most of these approaches account for the context for effective understanding.
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
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)
Investigating Gender Bias in BERT
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.
Improving Aspect-Level Sentiment Analysis with Aspect Extraction
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).
KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis
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.
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago.
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
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.
Ranked #1 on
Emotion Recognition in Conversation
on SEMAINE
Variational Fusion for Multimodal Sentiment Analysis
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
Recent Trends in Deep Learning Based Personality Detection
This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches.
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
Ranked #6 on
Emotion Recognition in Conversation
on EC
Sentiment and Sarcasm Classification with Multitask Learning
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
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.
Ranked #3 on
Emotion Recognition in Conversation
on SEMAINE
Emotion Classification
Emotion Recognition in Conversation
+2
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Sentiment analysis has immense implications in e-commerce through user feedback mining.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
We compile baselines, along with dataset split, for multimodal sentiment analysis.
A Deep Learning Approach for Multimodal Deception Detection
Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications.
Context-Dependent Sentiment Analysis in User-Generated Videos
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
Ranked #3 on
Emotion Recognition in Conversation
on CPED
Emotion Recognition in Conversation
General Classification
+4
