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Nicholas Yuan

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4 papers

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IJCAI Conference 2022 Conference Paper

Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph

Tong Zhu
Xiaoye Qu
Wenliang Chen
Zhefeng Wang
Baoxing Huai
Nicholas Yuan
Min Zhang

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG. In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers. Compared to the previous systems, our system achieves competitive results with 19. 8% of parameters and much lower resource consumption, taking only 3. 8% GPU hours for training and up to 8. 5 times faster for inference. Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements. Codes are available at https: //github. com/Spico197/DocEE.

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AAAI Conference 2022 Conference Paper

Flow-Based Unconstrained Lip to Speech Generation

JinZheng He
Zhou Zhao
Yi Ren
Jinglin Liu
Baoxing Huai
Nicholas Yuan

Unconstrained lip-to-speech aims to generate corresponding speeches based on silent facial videos with no restriction to head pose or vocabulary. It is desirable to generate intelligible and natural speech with a fast speed in unconstrained settings. Currently, to handle the more complicated scenarios, most existing methods adopt the autoregressive architecture, which is optimized with the MSE loss. Although these methods have achieved promising performance, they are prone to bring issues including high inference latency and melspectrogram over-smoothness. To tackle these problems, we propose a novel flow-based non-autoregressive lip-to-speech model (GlowLTS) to break autoregressive constraints and achieve faster inference. Concretely, we adopt a flow-based decoder which is optimized by maximizing the likelihood of the training data and is capable of more natural and fast speech generation. Moreover, we devise a condition module to improve the intelligibility of generated speech. We demonstrate the superiority of our proposed method through objective and subjective evaluation on Lip2Wav-Chemistry- Lectures and Lip2Wav-Chess-Analysis datasets. Our demo video can be found at https: //glowlts. github. io/.

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AAAI Conference 2022 Conference Paper

Parallel and High-Fidelity Text-to-Lip Generation

Jinglin Liu
Zhiying Zhu
Yi Ren
Wencan Huang
Baoxing Huai
Nicholas Yuan
Zhou Zhao

As a key component of talking face generation, lip movements generation determines the naturalness and coherence of the generated talking face video. Prior literature mainly focuses on speech-to-lip generation while there is a paucity in text-to-lip (T2L) generation. T2L is a challenging task and existing end-to-end works depend on the attention mechanism and autoregressive (AR) decoding manner. However, the AR decoding manner generates current lip frame conditioned on frames generated previously, which inherently hinders the inference speed, and also has a detrimental effect on the quality of generated lip frames due to error propagation. This encourages the research of parallel T2L generation. In this work, we propose a parallel decoding model for fast and high-fidelity text-to-lip generation (ParaLip). Specifically, we predict the duration of the encoded linguistic features and model the target lip frames conditioned on the encoded linguistic features with their duration in a non-autoregressive manner. Furthermore, we incorporate the structural similarity index loss and adversarial learning to improve perceptual quality of generated lip frames and alleviate the blurry prediction problem. Extensive experiments conducted on GRID and TCD-TIMIT datasets demonstrate the superiority of proposed methods.

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AAAI Conference 2021 Conference Paper

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Wenkai Zhang
Hongyu Lin
Xianpei Han
Le Sun
Huidan Liu
Zhicheng Wei
Nicholas Yuan

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weaklylabeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.

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