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Found 12 papers, 6 papers with code
Do Vision-Language Models Understand Compound Nouns?
We curate Compun, a novel benchmark with 400 unique and commonly used CNs, to evaluate the effectiveness of VLMs in interpreting CNs.
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.
DALE: Generative Data Augmentation for Low-Resource Legal NLP
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP.
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
In this paper, we propose CompA, a collection of two expert-annotated benchmarks with a majority of real-world audio samples, to evaluate compositional reasoning in ALMs.
AdVerb: Visually Guided Audio Dereverberation
We present AdVerb, a novel audio-visual dereverberation framework that uses visual cues in addition to the reverberant sound to estimate clean audio.
ASPIRE: Language-Guided Augmentation for Robust Image Classification
This paper presents ASPIRE (Language-guided data Augmentation for SPurIous correlation REmoval), a simple yet effective solution for expanding the training dataset with synthetic images without spurious features.
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e. g., biomedical).
BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
Though data augmentation has shown to be highly effective for low-resource NER in general, existing data augmentation techniques fail to produce factual and diverse augmentations for BioNER.
CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network
The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech, affecting people from various demographics.
A novel multimodal dynamic fusion network for disfluency detection in spoken utterances
Based on early-fusion and self-attention-based multimodal interaction between text and acoustic modalities, in this paper, we propose a novel multimodal architecture for disfluency detection from individual utterances.
MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition.
Ranked #2 on
Speech Emotion Recognition
on IEMOCAP
(using extra training data)
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
Expression of emotions is a crucial part of daily human communication.
