Abstract

Semantic segmentation provides a pixel-level understanding of an image essential for various scene-understanding vision tasks. However, semantic segmentation models demand significant computational resources during training and inference. These requirements pose a challenge in resource-constraint scenarios. To address this issue, we present a compression algorithm based on differentiable meta-pruning through hypernetwork: MPHyp. Our proposed method MPHyp utilizes hypernetworks that take latent vectors as input and output weight matrices for the segmentation model. L1 sparsification follows the proximal gradient optimizer, updates the latent vectors and introduces sparsity leading to automatic model pruning. The proposed method offers the benefit of achieving controllable compression during the training and significantly reducing the training time. We compare our methodology with a popular pruning approach and demonstrate its efficacy by reducing the number of parameters and floating point operations while maintaining the mean Intersection over Union (mIoU) metric. We conduct experiments on two widely accepted semantic segmentation architectures: UNet and ERFNet. Our experiments and ablation study demonstrate the effectiveness of our proposed methodology by achieving efficient and reasonable segmentation results.

Abstract

Lately, instruction-based techniques have made significant strides in improving performance in few-shot learning scenarios. They achieve this by bridging the gap between pre-trained language models and fine-tuning for specific downstream tasks. Despite these advancements, the performance of Large Language Models (LLMs) in information extraction tasks like Named Entity Recognition (NER), using prompts or instructions, still falls short of supervised baselines. The reason for this performance gap can be attributed to the fundamental disparity between NER and LLMs. NER is inherently a sequence labeling task, where the model must assign entity-type labels to individual tokens within a sentence. In contrast, LLMs are designed as a text generation task. This distinction between semantic labeling and text generation leads to subpar performance. In this paper, we transform the NER task into a text-generation task that can be readily adapted by LLMs. This involves enhancing source sentences with task-specific instructions and answer choices, allowing for the identification of entities and their types within natural language. We harness the strength of LLMs by integrating supervised learning within them. The goal of this combined strategy is to boost the performance of LLMs in extraction tasks like NER while simultaneously addressing hallucination issues often observed in LLM-generated content. A novel corpus Contract NER comprising seven frequently observed contract categories, encompassing named entities associated with 18 distinct legal entity types is released along with our baseline models. Our models and dataset are available to the community for future research

Abstract

Proceedings of the Eighth Conference on Machine Translation (WMT), pages 246–251 December 6–7, 2023. ©2023 Association for Computational Linguistics 246IIIT HYD’s Submission for WMT23 Test-suite task Ananya Mukherjee and Manish Shrivastava Machine Translation - Natural Language Processing Lab Language Technologies Research Centre International Institute of Information Technology - Hyderabad ananya.mukherjee@research.iiit.ac.in m.shrivastava@iiit.ac.in Abstract This paper summarizes the results of our test suite evaluation on 12 machine translation systems submitted at the Shared Task of the 8th Conference of Machine Translation (WMT23) for English-German (en-de) language pair. Our test suite covers five specific domains (entertainment, environ- ment, health, science, legal) and spans five distinct writing styles (descriptive, judgments, narrative, reporting, technical-writing). We present our analysis through automatic evaluation methods, conducted with a focus on domain-specific and writing style-specific evaluations.

Abstract

This paper presents our contributions to the WMT2023 shared metrics task, consisting of two distinct evaluation approaches: a) Unsu- pervised Metric (MEE4) and b) Supervised Metric (XLSim). MEE4 represents an unsu- pervised, reference-based assessment metric that quantifies linguistic features, encompass- ing lexical, syntactic, semantic, morphologi- cal, and contextual similarities, leveraging em- beddings. In contrast, XLsim is a supervised reference-based evaluation metric, employing a Siamese Architecture, which regresses on Di- rect Assessments (DA) from previous WMT News Translation shared tasks from 2017-2022. XLsim is trained using XLM-RoBERTa (base) on English-German reference and mt pairs with human scores. Here are the links for MEE4 1 and XLsim2 metrics.

Abstract

Background The Empathizing-Systemizing theory proposes that individuals with Autism Spectrum Disorder are systemizers and prefer Intense music characterized as loud , distorted, electric and percussive musical attributes and aggressive emotional attributes, typical of genres such as heavy metal, hard rock. However, there have been no studies that have tested this theory in a cross cultural setting. Aim The aim of this cross-cultural study is to gather music preferences by Indians and Non-Indians (US and Europe) and verify Baron-Cohen’s hypothesis. Method The Indian population of 20 participants (n=20; male, aged - 18-24 years), were contacted directly while for the Non-Indian population of 30 participants (n=25; US, n=5; Europe, male, aged 18-24) , we used social media platform Reddit (r/asperges and r/autism sub-reddit groups). Semi structured interviews were conducted to gather their demographic information, music they liked and disliked and how listening to music helps them in everyday life. Subsequently a mix of 10 songs each were curated comprising 5 songs they liked and out of the remaining 5 songs , 3 are of the genre they liked and 2 of the genre they disliked, taken from other individual’s playlist in a controlled setting. While playing each song, their likert responses (Like, Neutral, Dislike) were recorded per stimulus. Music Preferences were assessed based on the MUSIC model framework. Results Out of 20 participants, 18 preferred a mix of relaxing, non-aggressive, slow, quiet and instrumental musical attributes categorized as mix of Mellow and Sophisticated factor which helped in improved sleep and social skills and disliked music with loud and rebellious musical attributes categorized as Intense factor which shows that music serves for emotion regulation towards positive emotions in Indian population. In comparison , out of the 30 participants 22 preferred music categorized as Intense factor which brought in a calming factor when stressed and disliked music categorized as a mix of Mellow and Sophisticated factors which shows that music serves to discharge negative emotions in the Non-Indian group. Implications These results provide support to the Empathizing–Systemizing Theory for Non-Indian population but not for Indian population. It also suggests that cultural differences affect emotional arousal. These findings are helpful to build a recommendation system based on an individual’s preferences that can help in arousal regulation, sensory modulation difficulties commonly found among individuals with ASD.

Abstract

Crucial phases in aerial transportation and de- livery of suspended payloads are the clasping and unclasping of the payload to the cable. During these phases, along with the uncertainties in the quadrotor and in the environment, the inevitable payload swings induced by the human interaction or by other external interaction will create additional state- dependent uncertainties; such uncertainties pose a significant challenge in terms of control. If they continue unabated, these uncertainties can cause safety hazard for the quadrotor, the payload and, most importantly, for the human operating the clasping/unclasping tasks. As the state-of-the-art adaptive controllers cannot tackle such uncertainties or considers them as bounded terms, this paper presents an adaptive anti-swing controller where all uncertainties are taken in a state-dependent form. This choice is made to better capture uncertain clasping and unclasping operations of the suspended payload. The closed-loop stability is studied analytically and the real-time experiments confirm significant performance improvements for the proposed scheme over the state of the art.

Abstract

We present CoPara, the first publicly available paragraph-level (n-way aligned) multilingual parallel corpora for Dravidian languages. The collection contains 2856 paragraph/passage pairs between English and four Dravidian languages. We source the parallel paragraphs from the New India Samachar magazine and align them with English as a pivot language. We do human and artificial evaluations to validate the high-quality alignment and richness of the parallel paragraphs of a range of lengths. To show one of the many ways this dataset can be wielded, we finetuned IndicBART, an S2S transformer model on NMT for all XX-En pairs of languages in CoPara which perform better than existing sentence-level models on standard benchmarks (like BLEU) on sentence level translations and longer text too. We show how this dataset can enrich a model trained for a task like this, with more contextual cues and beyond sentence understanding even in low-resource settings like that of Dravidian languages. Finally, the dataset and models are made available publicly at GitHub to help advance research in Dravidian NLP, parallel multilingual, and beyond sentence-level tasks like NMT, etc.

Abstract

Deep learning assisted disease diagnosis using chest radiol- ogy images to assess severity of various respiratory conditions has gar- nered a lot of attention after the recent COVID-19 pandemic. Under- standing characteristic features associated with the disease in radiology images, along with variations observed from patient-to-patient and with the progression of disease, is important while building such models. In this work, we carried out comparative analysis of various deep architec- tures with the proposed attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) in classifying chest CT images into three categories, COVID-19, Normal, and Pneumonia. We show that the attention model with fewer parame- ters achieved better classification performance compared to state-of-the- art deep architectures such as EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and customized models proposed in similar studies such as COVIDNet-CT, CTnet-10, COVID-19Net, etc.

Abstract

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Abstract

Parkinson’s disease (PD) is a neurological disorder that affects the nerves responsible for controlling movements. Automatic detection methods used for PD detection from the speech signal are important as they offer benefits such as reliability, repeatability, and cost-effectiveness. In this paper, cepstral features derived from zero-time windowing (ZTW) based time-frequency analysis method for automatic detection of PD were explored. This study used the PC-GITA database to evaluate the performance using support vector machine classifier and neural networks. The performance of the system is also compared with conventional mel-frequency cepstral coeffcients (MFCCs) features. Cepstral features derived from ZTW method shown better performance than baseline feature. The best performance of the PD detection system obtained using the cepstral features derived from ZTW method in terms of classification accuracy is 73.6% . Index Terms—Parkinson’s Disease, Zero-time windowing, Automatic detection methods using speech signal.