PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding (2024.findings-emnlp)
Copied to clipboard
Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
| Challenge: | End-to-end models for Spoken Language Understanding have been autoregressive, resulting in higher latencies. |
| Approach: | They propose a method that uses Connectionist Temporal Classification to train robust non-autoregressive deliberation models. |
| Outcome: | The proposed method achieves 10x latency reduction over autoregressive models while preserving ability to correct ASR mistranscriptions. |
Similar Papers
MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
Copied to clipboard
| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
| Approach: | They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts . |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark SLU datasets. |
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | End-to-end spoken language understanding systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. |
| Approach: | They propose to model sequence labeling as a sequence prediction task . their systems explicitly separate the added complexity of recognizing spoken mentions from the NLU task of sequence labelling . |
| Outcome: | The proposed systems outperform both cascaded and direct models on a labeling task of named entity recognition across SLU benchmarks. |
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Modern artificial intelligence is characterized by large pretrained language models with strong language capabilities to be adapted to various downstream tasks. |
| Approach: | They propose to use the task of speech translation (ST) to pretrain speech models for end-to-end SLU on intra- and cross-lingual scenarios. |
| Outcome: | The proposed model achieves higher performance over baselines on monolingual and multilingual intent classification as well as spoken question answering using SLURP, MINDS-14, and NMSQA benchmarks. |
N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses (2021.acl-short)
Copied to clipboard
| Challenge: | Spoken Language Understanding systems parse spoken utterances into semantic structures like dialog acts and slots. |
| Approach: | They propose to use concatenated N-best ASR alternatives to represent utterances . they propose to employ a simpler utteration representation with no special delimiter . |
| Outcome: | The proposed model outperforms the prior state-of-the-art model on DSTC2 dataset. |
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation (2021.findings-acl)
Copied to clipboard
| Challenge: | Using connectionist temporal classification (CTC) for speech-to-text translation is counter-intuitive due to its monotonicity assumption. |
| Approach: | They propose to build a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC) their work shows transformer encoders can change the word order and points out the future research direction that needs to be explored more on non-Autoregressives speech translation. |
| Outcome: | The proposed model improves translation performance by using transformer encoders. |
PCAD: Towards ASR-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling (2024.acl-long)
Copied to clipboard
| Challenge: | Spoken language understanding (SLU) suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. |
| Approach: | They propose a framework which calibrates bias and errors and achieves adaptive-balanced decoupling training by a prototype-based loss model. |
| Outcome: | The proposed framework outperforms existing approaches and achieves state-of-the-art performance on three datasets. |
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (2024.naacl-long)
Copied to clipboard
| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
| Approach: | They propose a module-based approach to learn intent in a noisy-channel model . they correlate error patterns between clean and noisy ASR transcripts . |
| Outcome: | The proposed method outperforms existing methods and improves in noisy environments. |
A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing translation pipelines require additional cascade components to achieve speech-to-speech translation. |
| Approach: | They propose a non-autoregressive generation framework for simultaneous speech translation . it integrates both text-to-text and speech-tospeech tasks into a unified framework . |
| Outcome: | The proposed framework outperforms state-of-the-art models in speech-to-text and speech- to-speech tasks. |
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding (2023.findings-acl)
Copied to clipboard
| Challenge: | Despite efforts to improve ASR robustness, errors from pipeline approaches can lead to error propagation. |
| Approach: | They propose a framework for improving ASR robustness in SLU by using mutual learning and large-margin contrastive learning. |
| Outcome: | The proposed framework outperforms existing models and achieves new state-of-the-art performance on three datasets. |
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks (2023.acl-long)
Copied to clipboard
Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding (SLU) tasks have received little attention and resources compared to lower-level tasks like speech and speaker recognition. |
| Approach: | They propose annotated SLU benchmark tasks based on freely available speech data to complement existing benchmarks and address gaps in the evaluation landscape. |
| Outcome: | The proposed benchmarks complement existing benchmarks and address gaps in the evaluation landscape. |