Search Results for author:
Found 7 papers, 3 papers with code
TamilATIS: Dataset for Task-Oriented Dialog in Tamil
Task-Oriented Dialogue (TOD) systems allow users to accomplish tasks by giving directions to the system using natural language utterances.
Span Extraction Aided Improved Code-mixed Sentiment Classification
Sentiment classification is a fundamental NLP task of detecting the sentiment polarity of a given text.
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP.
A Closer Look at the Limitations of Instruction Tuning
Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets.
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
Composite Diffusion | whole >= Σparts
This paper introduces Composite Diffusion as a means for artists to generate high-quality images by composing from the sub-scenes.
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme.
