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Found 33 papers, 23 papers with code
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.
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.
Stable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech Recognition
Specifically, first, we perform vanilla continued pre-training on an initial SSL pre-trained model on the target domain ASR dataset and call it the teacher.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning
Continued pre-training (CP) offers multiple advantages, like target domain adaptation and the potential to exploit the continuous stream of unlabeled data available online.
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.
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.
UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation
Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels for unsupervised fine-tuning before the actual fine-tuning step.
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.
data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setup
In this paper, we propose a new Self-Supervised Learning (SSL) algorithm called data2vec-aqc, for speech representation learning from unlabeled speech data.
Automatic Speech Recognition (ASR)
Representation Learning
+1
MAST: Multiscale Audio Spectrogram Transformers
We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST).
SLICER: Learning universal audio representations using low-resource self-supervised pre-training
We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on unlabeled audio data that reduces the need for large amounts of labeled data for audio and speech classification.
CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations
While Self-Supervised Learning has helped reap the benefit of the scale from the available unlabeled data, the learning paradigms are continuously being bettered.
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
Expression of emotions is a crucial part of daily human communication.
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition
Self-supervised learning (SSL) to learn high-level speech representations has been a popular approach to building Automatic Speech Recognition (ASR) systems in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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)
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations
To alleviate this issue, we propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant weights from models pre-trained on large amounts of out-of-domain (OOD) data.
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.
DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning
Inspired by the recent progress in self-supervised learning for computer vision, in this paper we introduce DeLoRes, a new general-purpose audio representation learning approach.
Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets
In the current era of the internet, where social media platforms are easily accessible for everyone, people often have to deal with threats, identity attacks, hate, and bullying due to their association with a cast, creed, gender, religion, or even acceptance or rejection of a notion.
DECAR: Deep Clustering for learning general-purpose Audio Representations
We introduce DECAR, a self-supervised pre-training approach for learning general-purpose audio representations.
DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances
We believe that our dataset would act as a benchmark for the relatively new and un-explored Spoken Language Processing task of detecting toxicity from spoken utterances and boost further research in this space.
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.
End-to-end Named Entity Recognition from English Speech
Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
