Challenge: Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks.
Approach: They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task .
Outcome: The proposed model is superior in learning speech-text alignment and multi-turn dialog context.

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Challenge: Recent advances in speech-text pretraining rely on parallel speech- text data . however, data accessibility is a challenge due to the limited data available.
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Efficient Training for Cross-lingual Speech Language Models (2026.findings-acl)

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Challenge: Currently, large language models (LLMs) focus on the text modality, making speech modeling difficult.
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Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)

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Challenge: Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations.
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FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)

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Challenge: Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice.
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Challenge: Pre-trained language models (PrLMs) have shown impressive improvements for various downstream tasks including various dialogue related ones.
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PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)

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Challenge: Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal .
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Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition (2024.lrec-main)

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Challenge: Existing methods for pre-training for automatic speech recognition (ASR) focus on single-stage pre-train followed by fine-tuning on downstream task.
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Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)

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Challenge: Existing models for dialogue comprehension are not available for the pre-training of such a model.
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Challenge: Existing approaches to building task-oriented dialog systems require a substantial amount of annotations and thus are labor-intensive.
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Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)

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Challenge: despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal.
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