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Found 24 papers, 7 papers with code
Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications.
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools
In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works.
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding
Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling.
Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling
Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT.
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting. We assess the performance of small language models in zero-shot text classification, challenging the prevailing dominance of large models. Across 15 datasets, our investigation benchmarks language models from 77M to 40B parameters using different architectures and scoring functions.
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
A combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model.
mALBERT: Is a Compact Multilingual BERT Model Still Worth It?
Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models.
Semantic enrichment towards efficient speech representations
Over the past few years, self-supervised learned speech representations have emerged as fruitful replacements for conventional surface representations when solving Spoken Language Understanding (SLU) tasks.
Benchmarking Transformers-based models on French Spoken Language Understanding tasks
In this paper, we propose a unified benchmark, focused on evaluating models quality and their ecological impact on two well-known French spoken language understanding tasks.
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation
We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms.
Where are we in semantic concept extraction for Spoken Language Understanding?
More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task
In this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task.
LIMSI\_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
This paper describes the participation of LIMSI{\_}UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
A Metric Learning Approach to Misogyny Categorization
The task of automatic misogyny identification and categorization has not received as much attention as other natural language tasks have, even though it is crucial for identifying hate speech in social Internet interactions.
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification
Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification.
End-to-end named entity extraction from speech
Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation
We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Utilisation des repr\'esentations continues des mots et des param\`etres prosodiques pour la d\'etection d'erreurs dans les transcriptions automatiques de la parole (Combining continuous word representation and prosodic features for ASR error detection)
Dans cet article, nous proposons d{'}{\'e}tudier leur utilisation dans une architecture neuronale pour la t{\^a}che de d{\'e}tection des erreurs au sein de transcriptions automatiques de la parole.
Word Embedding Evaluation and Combination
Different approaches have been introduced to calculate word embeddings through neural networks.
