215 research outputs found

    Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information

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    For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic information can influence the speech interpretation of the target utterance, previous works on PSP mainly focus on utilizing intrautterance linguistic information of the current utterance only. This work proposes to use inter-utterance linguistic information to improve the performance of PSP. Multi-level contextual information, which includes both inter-utterance and intrautterance linguistic information, is extracted by a hierarchical encoder from character level, utterance level and discourse level of the input text. Then a multi-task learning (MTL) decoder predicts prosodic boundaries from multi-level contextual information. Objective evaluation results on two datasets show that our method achieves better F1 scores in predicting prosodic word (PW), prosodic phrase (PPH) and intonational phrase (IPH). It demonstrates the effectiveness of using multi-level contextual information for PSP. Subjective preference tests also indicate the naturalness of synthesized speeches are improved.Comment: Accepted by Interspeech202

    Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

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    Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.Comment: Accepted to ACL 2022 (main conference

    Hypoglycemic activity and the activation of phosphorylation of a triterpenoid-rich extract from Euryale shell on streptozotocin-induced diabetic mice

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    In the present study, we examined the hypoglycemic properties and the effective mechanisms of a triterpenoid-rich extract from the Euryale shell (ES) in streptozotocin-induced (STZ) diabetic mice. The hydroalcoholic extract of ES (200, 400 and 600 mg/kg/day) was orally administered to STZ-diabetic mice for 4 weeks. We observed that in the liver of diabetic mice, the ES extract caused a sharp reduction in the gene expression of protein tyrosine phosphatase-1B (PTP1B) but induced the gene expression of phosphatidyl-inositol-3-kinase (PI-3K) and protein kinase B (PKB) compared with that of untreated diabetic mice. Additionally, a significant increase in the phosphorylation of the PKB protein was observed (p<0.01). This was corroborated by the inhibition of PTP1B and by the regulation of glucose uptake via PI-3K activation, which together demonstrate that the reduction of PTP1B can modulate key insulin signaling events downstream of the insulin receptor.
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