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Found 7 papers, 2 papers with code
What BERT Based Language Model Learns in Spoken Transcripts: An Empirical Study
We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues.
Towards Probing Contact Center Large Language Models
Additionally, we compare the performance of OOB-LLMs and CC-LLMs on the widely used SentEval dataset, and assess their capabilities in terms of surface, syntactic, and semantic information through probing tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision
In Weak Supervised Learning (WSL), a model is trained over noisy labels obtained from semantic rules and task-specific pre-trained models.
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
Our method achieves state-of-the-art results on 1-shot and 5-shot intent detection task with gains ranging from 2-8\% points in F1 score on four benchmark datasets.
What BERT Based Language Models Learn in Spoken Transcripts: An Empirical Study
We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues.
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript
In this work, we propose a BERT-style language model, referred to as PhonemeBERT, that learns a joint language model with phoneme sequence and ASR transcript to learn phonetic-aware representations that are robust to ASR errors.
Gated Mechanism for Attention Based Multimodal Sentiment Analysis
Multimodal sentiment analysis has recently gained popularity because of its relevance to social media posts, customer service calls and video blogs.
Ranked #4 on
Multimodal Sentiment Analysis
on MOSI
