arXiv subjects

Kai Yu

Publications and source records attributed to Kai Yu.

At least 271 records · Page 15Linked to original sources

arXiv2021

Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events

Automated Audio Captioning is a cross-modal task, generating natural language descriptions to summarize the audio clips' sound events. However, grounding the actual sound events in the given audio based on its corresponding caption has not been investigated. This paper contributes an AudioGrounding dataset, which provides the correspondence between sound events and the captions provided in Audiocaps, along with the location (timestamps) of each present sound event. Based on such, we propose the text-to-audio grounding (TAG) task, which interactively considers the relationship between audio processing and language understanding. A baseline approach is provided, resulting in an event-F1 score of 28.3% and a Polyphonic Sound Detection Score (PSDS) score of 14.7%.

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arXiv2021

A 3D Non-stationary MmWave Channel Model for Vacuum Tube Ultra-High-Speed Train Channels

As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios. In this paper, a three-dimensional non-stationary millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed to investigate the channel characteristics of UHST channels in vacuum tube scenarios, taking into account the waveguide effect and the impact of tube wall roughness on channel. Then, based on the proposed model, some important time-variant channel statistical properties are studied and compared with those in existing HST and tunnel channels. The results obtained show that the multipath effect in vacuum tube scenarios will be more obvious than tunnel scenarios but less than existing HST scenarios, which will provide some insights for future research on vacuum tube UHST wireless communications.

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arXiv2021

Towards duration robust weakly supervised sound event detection

Sound event detection (SED) is the task of tagging the absence or presence of audio events and their corresponding interval within a given audio clip. While SED can be done using supervised machine learning, where training data is fully labeled with access to per event timestamps and duration, our work focuses on weakly-supervised sound event detection (WSSED), where prior knowledge about an event's duration is unavailable. Recent research within the field focuses on improving segment- and event-level localization performance for specific datasets regarding specific evaluation metrics. Specifically, well-performing event-level localization requires fully labeled development subsets to obtain event duration estimates, which significantly benefits localization performance. Moreover, well-performing segment-level localization models output predictions at a coarse-scale (e.g., 1 second), hindering their deployment on datasets containing very short events (< 1 second). This work proposes a duration robust CRNN (CDur) framework, which aims to achieve competitive performance in terms of segment- and event-level localization. This paper proposes a new post-processing strategy named "Triple Threshold" and investigates two data augmentation methods along with a label smoothing method within the scope of WSSED. Evaluation of our model is done on the DCASE2017 and 2018 Task 4 datasets, and URBAN-SED. Our model outperforms other approaches on the DCASE2018 and URBAN-SED datasets without requiring prior duration knowledge. In particular, our model is capable of similar performance to strongly-labeled supervised models on the URBAN-SED dataset. Lastly, ablation experiments to reveal that without post-processing, our model's localization performance drop is significantly lower compared with other approaches.

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arXiv2021

A relic sketch extraction framework based on detail-aware hierarchical deep network

As the first step of the restoration process of painted relics, sketch extraction plays an important role in cultural research. However, sketch extraction suffers from serious disease corrosion, which results in broken lines and noise. To overcome these problems, we propose a deep learning-based hierarchical sketch extraction framework for painted cultural relics. We design the sketch extraction process into two stages: coarse extraction and fine extraction. In the coarse extraction stage, we develop a novel detail-aware bi-directional cascade network that integrates flow-based difference-of-Gaussians (FDoG) edge detection and a bi-directional cascade network (BDCN) under a transfer learning framework. It not only uses the pre-trained strategy to extenuate the requirements of large datasets for deep network training but also guides the network to learn the detail characteristics by the prior knowledge from FDoG. For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch. Specifically, all the features extracted from multiple intermediate layers in the decoder of MSU-Net are fused for sketch predication. Experimental results showed that the proposed method outperforms the other seven state-of-the-art methods in terms of visual and quantitative metrics and can also deal with complex backgrounds.

cs.CV↗

arXiv2020

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing

One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.

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arXiv2020

Audio Caption in a Car Setting with a Sentence-Level Loss

Captioning has attracted much attention in image and video understanding while a small amount of work examines audio captioning. This paper contributes a Mandarin-annotated dataset for audio captioning within a car scene. A sentence-level loss is proposed to be used in tandem with a GRU encoder-decoder model to generate captions with higher semantic similarity to human annotations. We evaluate the model on the newly-proposed Car dataset, a previously published Mandarin Hospital dataset and the Joint dataset, indicating its generalization capability across different scenes. An improvement in all metrics can be observed, including classical natural language generation (NLG) metrics, sentence richness and human evaluation ratings. However, though detailed audio captions can now be automatically generated, human annotations still outperform model captions on many aspects.

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arXiv2020

An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models

Recently, pre-trained language models like BERT have shown promising performance on multiple natural language processing tasks. However, the application of these models has been limited due to their huge size. To reduce its size, a popular and efficient way is quantization. Nevertheless, most of the works focusing on BERT quantization adapted primary linear clustering as the quantization scheme, and few works try to upgrade it. That limits the performance of quantization significantly. In this paper, we implement k-means quantization and compare its performance on the fix-precision quantization of BERT with linear quantization. Through the comparison, we verify that the effect of the underlying quantization scheme upgrading is underestimated and there is a huge development potential of k-means quantization. Besides, we also compare the two quantization schemes on ALBERT models to explore the robustness differences between different pre-trained models.

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arXiv2020

Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.

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arXiv2020

Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding

Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems. In this paper, we propose a vector projection network for few-shot slot tagging, which exploits projections of contextual word embeddings on each target label vector as word-label similarities. Essentially, this approach is equivalent to a normalized linear model with an adaptive bias. The contrastive experiment demonstrates that our proposed vector projection based similarity metric can significantly surpass other variants. Specifically, in the five-shot setting on benchmarks SNIPS and NER, our method outperforms the strongest few-shot learning baseline by $6.30$ and $13.79$ points on F$_1$ score, respectively. Our code will be released at https://github.com/sz128/few_shot_slot_tagging_and_NER.

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arXiv2020

Deep Reinforcement Learning for On-line Dialogue State Tracking

Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In this paper, a novel companion teaching based deep reinforcement learning (DRL) framework for on-line DST optimization is proposed. To the best of our knowledge, this is the first effort to optimize the DST module within DRL framework for on-line task-oriented spoken dialogue systems. In addition, dialogue policy can be further jointly updated. Experiments show that on-line DST optimization can effectively improve the dialogue manager performance while keeping the flexibility of using predefined policy. Joint training of both DST and policy can further improve the performance.

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arXiv2020

Structured Hierarchical Dialogue Policy with Graph Neural Networks

Dialogue policy training for composite tasks, such as restaurant reservation in multiple places, is a practically important and challenging problem. Recently, hierarchical deep reinforcement learning (HDRL) methods have achieved good performance in composite tasks. However, in vanilla HDRL, both top-level and low-level policies are all represented by multi-layer perceptrons (MLPs) which take the concatenation of all observations from the environment as the input for predicting actions. Thus, traditional HDRL approach often suffers from low sampling efficiency and poor transferability. In this paper, we address these problems by utilizing the flexibility of graph neural networks (GNNs). A novel ComNet is proposed to model the structure of a hierarchical agent. The performance of ComNet is tested on composited tasks of the PyDial benchmark. Experiments show that ComNet outperforms vanilla HDRL systems with performance close to the upper bound. It not only achieves sample efficiency but also is more robust to noise while maintaining the transferability to other composite tasks.

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arXiv2020

Dual Learning for Dialogue State Tracking

In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history. Dialogue state tracking (DST) is to estimate the dialogue state at each turn. Due to the dependency on complicated dialogue history contexts, DST data annotation is more expensive than single-sentence language understanding, which makes the task more challenging. In this work, we formulate DST as a sequence generation problem and propose a novel dual-learning framework to make full use of unlabeled data. In the dual-learning framework, there are two agents: the primal tracker agent (utterance-to-state generator) and the dual utterance generator agent (state-to-utterance genera-tor). Compared with traditional supervised learning framework, dual learning can iteratively update both agents through the reconstruction error and reward signal respectively without labeled data. Reward sparsity problem is hard to solve in previous DST methods. In this work, the reformulation of DST as a sequence generation model effectively alleviates this problem. We call this primal tracker agent dual-DST. Experimental results on MultiWOZ2.1 dataset show that the proposed dual-DST works very well, especially when labelled data is limited. It achieves comparable performance to the system where labeled data is fully used.

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arXiv2020

CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking

In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history. While previous approaches often define dialogue states as a combination of separate triples ({\em domain-slot-value}), in this paper, we employ a structured state representation and cast dialogue state tracking as a sequence generation problem. Based on this new formulation, we propose a {\bf C}oa{\bf R}s{\bf E}-to-fine {\bf DI}alogue state {\bf T}racking ({\bf CREDIT}) approach. Taking advantage of the structured state representation, which is a marked language sequence, we can further fine-tune the pre-trained model (by supervised learning) by optimizing natural language metrics with the policy gradient method. Like all generative state tracking methods, CREDIT does not rely on pre-defined dialogue ontology enumerating all possible slot values. Experiments demonstrate our tracker achieves encouraging joint goal accuracy for the five domains in MultiWOZ 2.0 and MultiWOZ 2.1 datasets.

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arXiv2020

Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management

The task-oriented spoken dialogue system (SDS) aims to assist a human user in accomplishing a specific task (e.g., hotel booking). The dialogue management is a core part of SDS. There are two main missions in dialogue management: dialogue belief state tracking (summarising conversation history) and dialogue decision-making (deciding how to reply to the user). In this work, we only focus on devising a policy that chooses which dialogue action to respond to the user. The sequential system decision-making process can be abstracted into a partially observable Markov decision process (POMDP). Under this framework, reinforcement learning approaches can be used for automated policy optimization. In the past few years, there are many deep reinforcement learning (DRL) algorithms, which use neural networks (NN) as function approximators, investigated for dialogue policy.

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arXiv2020

End-to-End Speaker-Dependent Voice Activity Detection

Voice activity detection (VAD) is an essential pre-processing step for tasks such as automatic speech recognition (ASR) and speaker recognition. A basic goal is to remove silent segments within an audio, while a more general VAD system could remove all the irrelevant segments such as noise and even unwanted speech from non-target speakers. We define the task, which only detects the speech from the target speaker, as speaker-dependent voice activity detection (SDVAD). This task is quite common in real applications and usually implemented by performing speaker verification (SV) on audio segments extracted from VAD. In this paper, we propose an end-to-end neural network based approach to address this problem, which explicitly takes the speaker identity into the modeling process. Moreover, inference can be performed in an online fashion, which leads to low system latency. Experiments are carried out on a conversational telephone dataset generated from the Switchboard corpus. Results show that our proposed online approach achieves significantly better performance than the usual VAD/SV system in terms of both frame accuracy and F-score. We also used our previously proposed segment-level metric for a more comprehensive analysis.

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arXiv2020

Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding

Spoken Language Understanding (SLU) converts hypotheses from automatic speech recognizer (ASR) into structured semantic representations. ASR recognition errors can severely degenerate the performance of the subsequent SLU module. To address this issue, word confusion networks (WCNs) have been used to encode the input for SLU, which contain richer information than 1-best or n-best hypotheses list. To further eliminate ambiguity, the last system act of dialogue context is also utilized as additional input. In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly. It can integrate both structural information and ASR posterior probabilities of WCNs in the BERT architecture. Experiments on DSTC2, a benchmark of SLU, show that the proposed method is effective and can outperform previous state-of-the-art models significantly.

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arXiv2020

Robust Spoken Language Understanding with RL-based Value Error Recovery

Spoken Language Understanding (SLU) aims to extract structured semantic representations (e.g., slot-value pairs) from speech recognized texts, which suffers from errors of Automatic Speech Recognition (ASR). To alleviate the problem caused by ASR-errors, previous works may apply input adaptations to the speech recognized texts, or correct ASR errors in predicted values by searching the most similar candidates in pronunciation. However, these two methods are applied separately and independently. In this work, we propose a new robust SLU framework to guide the SLU input adaptation with a rule-based value error recovery module. The framework consists of a slot tagging model and a rule-based value error recovery module. We pursue on an adapted slot tagging model which can extract potential slot-value pairs mentioned in ASR hypotheses and is suitable for the existing value error recovery module. After the value error recovery, we can achieve a supervision signal (reward) by comparing refined slot-value pairs with annotations. Since operations of the value error recovery are non-differentiable, we exploit policy gradient based Reinforcement Learning (RL) to optimize the SLU model. Extensive experiments on the public CATSLU dataset show the effectiveness of our proposed approach, which can improve the robustness of SLU and outperform the baselines by significant margins.

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arXiv2020

Voice activity detection in the wild via weakly supervised sound event detection

Traditional supervised voice activity detection (VAD) methods work well in clean and controlled scenarios, with performance severely degrading in real-world applications. One possible bottleneck is that speech in the wild contains unpredictable noise types, hence frame-level label prediction is difficult, which is required for traditional supervised VAD training. In contrast, we propose a general-purpose VAD (GPVAD) framework, which can be easily trained from noisy data in a weakly supervised fashion, requiring only clip-level labels. We proposed two GPVAD models, one full (GPV-F), trained on 527 Audioset sound events, and one binary (GPV-B), only distinguishing speech and noise. We evaluate the two GPV models against a CRNN based standard VAD model (VAD-C) on three different evaluation protocols (clean, synthetic noise, real data). Results show that our proposed GPV-F demonstrates competitive performance in clean and synthetic scenarios compared to traditional VAD-C. Further, in real-world evaluation, GPV-F largely outperforms VAD-C in terms of frame-level evaluation metrics as well as segment-level ones. With a much lower requirement for frame-labeled data, the naive binary clip-level GPV-B model can still achieve comparable performance to VAD-C in real-world scenarios.

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