A survey on non-autoregressive generation for neural machine translation and beyond
Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …
(NMT) to speed up inference, has attracted much attention in both machine learning and …
Token-level self-evolution training for sequence-to-sequence learning
Adaptive training approaches, widely used in sequence-to-sequence models, commonly
reweigh the losses of different target tokens based on priors, eg word frequency. However …
reweigh the losses of different target tokens based on priors, eg word frequency. However …
Findings of the IWSLT 2022 Evaluation Campaign.
The evaluation campaign of the 19th International Conference on Spoken Language
Translation featured eight shared tasks:(i) Simultaneous speech translation,(ii) Offline …
Translation featured eight shared tasks:(i) Simultaneous speech translation,(ii) Offline …
Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …
better comprehend long complicated sentences and obtain accurate aspect-specific …
GL-GIN: Fast and accurate non-autoregressive model for joint multiple intent detection and slot filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing
attention. However, the state-of-the-art joint models heavily rely on autoregressive …
attention. However, the state-of-the-art joint models heavily rely on autoregressive …
Mrrl: Modifying the reference via reinforcement learning for non-autoregressive joint multiple intent detection and slot filling
With the rise of non-autoregressive approach, some non-autoregressive models for joint
multiple intent detection and slot filling have obtained the promising inference speed …
multiple intent detection and slot filling have obtained the promising inference speed …
Towards spoken language understanding via multi-level multi-grained contrastive learning
X Cheng, W Xu, Z Zhu, H Li, Y Zou - Proceedings of the 32nd ACM …, 2023 - dl.acm.org
Spoken language understanding (SLU) is a core task in task-oriented dialogue systems,
which aims at understanding user's current goal through constructing semantic frames. SLU …
which aims at understanding user's current goal through constructing semantic frames. SLU …
Multitask learning for multilingual intent detection and slot filling in dialogue systems
Dialogue systems are becoming an ubiquitous presence in our everyday lives having a
huge impact on business and society. Spoken language understanding (SLU) is the critical …
huge impact on business and society. Spoken language understanding (SLU) is the critical …
Co-guiding net: Achieving mutual guidances between multiple intent detection and slot filling via heterogeneous semantics-label graphs
Recent graph-based models for joint multiple intent detection and slot filling have obtained
promising results through modeling the guidance from the prediction of intents to the …
promising results through modeling the guidance from the prediction of intents to the …
NLU++: A multi-label, slot-rich, generalisable dataset for natural language understanding in task-oriented dialogue
We present NLU++, a novel dataset for natural language understanding (NLU) in task-
oriented dialogue (ToD) systems, with the aim to provide a much more challenging …
oriented dialogue (ToD) systems, with the aim to provide a much more challenging …