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. |
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. |
Speechformer: Reducing Information Loss in Direct Speech Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to speech-to-text translation (ST) use a pipeline of two sub-components - an automatic speech recognition (ASR) and a machine translation (MT) model. |
| Approach: | They propose an architecture that avoids initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. |
| Outcome: | The proposed architecture achieves gains of up to 0.8 BLEU on the standard MuST-C corpus and up to 4.0 BLUE in a low resource scenario. |
Simulating ASR errors for training SLU systems (L18-1)
Copied to clipboard
| Challenge: | Existing methods to simulate automatic speech recognition errors from manual transcriptions are not available during training of the SLU model. |
| Approach: | They propose to use acoustic and linguistic word embeddings to define a similarity measure between words to predict ASR confusions. |
| Outcome: | The proposed method significantly improves the performance of spoken language understanding systems. |
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing methods to integrate hypotheses into speech recognition systems are noisy and can cause information loss. |
| Approach: | They propose to integrate hypotheses into multi-task learning and transfer learning to improve performance. |
| Outcome: | The proposed model improves domain and intent classification by 19% and 37% compared to current methods . the proposed model could recover transcription and rewrite the query for a better understanding . |
Federated Learning for Spoken Language Understanding (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to improve robustness of models focus on a single dataset . but, there are few studies on how to combine merits of different datasets . |
| Approach: | They propose a federated learning framework that could unify datasets and tasks . they propose MV-Encoder as backbone of the framework to provide multi-granularity text representations . |
| Outcome: | The proposed framework improves on two SLU benchmark datasets and federated learning settings. |
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. |
The importance of fillers for text representations of speech transcripts (2020.emnlp-main)
Copied to clipboard
| Challenge: | Fillers are a type of disfluency that can be a sound ("um" or "uh") filling a pause in an utterance or conversation. |
| Approach: | They propose to represent fillers with deep contextualised embeddings to improve modelling of spoken language and two downstream tasks . |
| Outcome: | The proposed representations improve modelling of spoken language and two downstream tasks, predicting a speaker’s stance and expressed confidence. |
Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current automatic speech recognition systems rely on only audio information, ignoring multi-modal context. |
| Approach: | They propose to integrate visual context into existing automatic speech recognition systems to integrate presentation slides with multi-modal information. |
| Outcome: | The proposed model reduces word error rate by approximately 34% across all words and 35% for domain-specific terms compared to baseline model. |
Lattice Transformer for Speech Translation (P19-1)
Copied to clipboard
| Challenge: | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture. |
| Approach: | They propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) they propose 'controllable' lattica attention mechanism to consume latent representations. |
| Outcome: | The proposed model outperforms baseline and lattice LSTM on the Chinese-English translation task. |
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT (2023.acl-long)
Copied to clipboard
| Challenge: | NLP literature has not given enough attention to the phenomenon of negative transfer . positive transfer refers to the facilitating effects of one language in acquiring another and negative transfer refer to the negative effects between the learner's native [L1] and target [L2] languages. |
| Approach: | They build a Mutlilingual Age Ordered CHILDES dataset to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
| Outcome: | The proposed model enables us to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |