Search Results for author:
Found 16 papers, 9 papers with code
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.
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.
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.
DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer
Also, the best version of DatUS^2 outperforms the existing state-of-the-art method for the unsupervised dense semantic segmentation task with 15. 02% MiOU and 21. 47% Pixel accuracy on the SUIM dataset.
Ranked #1 on
Unsupervised Semantic Segmentation
on SUIM
AV-RIR: Audio-Visual Room Impulse Response Estimation
We propose AV-RIR, a novel multi-modal multi-task learning approach to accurately estimate the RIR from a given reverberant speech signal and the visual cues of its corresponding environment.
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.
RECAP: Retrieval-Augmented Audio Captioning
We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and effective audio captioning system that generates captions conditioned on an input audio and other captions similar to the audio retrieved from a datastore.
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.
Span Classification with Structured Information for Disfluency Detection in Spoken Utterances
Existing approaches in disfluency detection focus on solving a token-level classification task for identifying and removing disfluencies in text.
Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments
We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model.
Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings
This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides.
