Search Results for author: Kun Qian

Found 28 papers, 7 papers with code

AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All

no code implementations15 Dec 2021 Lei Zuo, Kun Qian, Bowen Yang, Zhou Yu

A commonly observed problem of the state-of-the-art natural language technologies, such as Amazon Alexa and Apple Siri, is that their services do not extend to most developing countries' citizens due to language barriers.


Database Search Results Disambiguation for Task-Oriented Dialog Systems

no code implementations15 Dec 2021 Kun Qian, Ahmad Beirami, Satwik Kottur, Shahin Shayandeh, Paul Crook, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar

We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.

Multi-Task Learning

$\boldsymbolγ$-Net: Superresolving SAR Tomographic Inversion via Deep Learning

no code implementations8 Dec 2021 Kun Qian, Yuanyuan Wang, Yilei Shi, Xiao Xiang Zhu

This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers.

Compressive Sensing Super-Resolution

Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals

1 code implementation CVPR 2021 Kun Qian, Shilin Zhu, Xinyu Zhang, Li Erran Li

Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving.

Autonomous Driving

Annotation Inconsistency and Entity Bias in MultiWOZ

no code implementations SIGDIAL (ACL) 2021 Kun Qian, Ahmad Beirami, Zhouhan Lin, Ankita De, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar

In this work, we identify an overlooked issue with dialog state annotation inconsistencies in the dataset, where a slot type is tagged inconsistently across similar dialogs leading to confusion for DST modeling.

Text Generation

A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting

no code implementations6 Apr 2021 Kun Qian, Wei Wei, Zhou Yu

The most recent researches on domain adaption focus on giving the model a better initialization, rather than optimizing the adaptation process.

Domain Adaptation Meta-Learning

Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview

no code implementations8 Dec 2020 Kun Qian, Bjorn W. Schuller, Yoshiharu Yamamoto

Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e. g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds).

Towards SAR Tomographic Inversion via Sparse Bayesian Learning

no code implementations24 Nov 2020 Kun Qian, Yuanyuan Wang, Xiaoxiang Zhu

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive.

Learning Structured Representations of Entity Names using Active Learning and Weak Supervision

1 code implementation EMNLP 2020 Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, Lucian Popa

Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation.

Active Learning

A Survey of the State of Explainable AI for Natural Language Processing

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen

Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable.

MeDaS: An open-source platform as service to help break the walls between medicine and informatics

no code implementations12 Jul 2020 Liang Zhang, Johann Li, Ping Li, Xiaoyuan Lu, Peiyi Shen, Guangming Zhu, Syed Afaq Shah, Mohammed Bennarmoun, Kun Qian, Björn W. Schuller

To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side.

Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases

no code implementations WS 2020 Nikita Bhutani, Xinyi Zheng, Kun Qian, Yunyao Li, H. Jagadish

Knowledge-based question answering (KB{\_}QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB.

Question Answering

deepSELF: An Open Source Deep Self End-to-End Learning Framework

no code implementations11 May 2020 Tomoya Koike, Kun Qian, Björn W. Schuller, Yoshiharu Yamamoto

To the best of our knowledge, it is the first public toolkit assembling a series of state-of-the-art deep learning technologies.

Image Generation

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

no code implementations30 Apr 2020 Jing Han, Kun Qian, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu, Huaiyuan Zheng, Wei Ji, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, Björn W. Schuller

In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.

Sleep Quality

COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis

no code implementations24 Mar 2020 Björn W. Schuller, Dagmar M. Schuller, Kun Qian, Juan Liu, Huaiyuan Zheng, Xiao Li

We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

End-to-End Trainable Non-Collaborative Dialog System

1 code implementation25 Nov 2019 Yu Li, Kun Qian, Weiyan Shi, Zhou Yu

End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task.

How to Build User Simulators to Train RL-based Dialog Systems

1 code implementation IJCNLP 2019 Weiyan Shi, Kun Qian, Xuewei Wang, Zhou Yu

We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly.

Adabot: Fault-Tolerant Java Decompiler

no code implementations14 Aug 2019 Zhiming Li, Qing Wu, Kun Qian

Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94. 50% and 2. 65% on the redundant test set; 92. 30% and 3. 48% on the purified test set.

Machine Translation Translation

Low-resource Deep Entity Resolution with Transfer and Active Learning

no code implementations ACL 2019 Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, Lucian Popa

Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records.

Active Learning Entity Resolution +2

Domain Adaptive Dialog Generation via Meta Learning

1 code implementation ACL 2019 Kun Qian, Zhou Yu

We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain.

Domain Adaptation Meta-Learning

Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

no code implementations29 Mar 2019 Zixing Zhang, Jing Han, Kun Qian, Christoph Janott, Yanan Guo, Bjoern Schuller

One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data.

Data Augmentation General Classification

Knowledge Refinement via Rule Selection

no code implementations29 Jan 2019 Phokion G. Kolaitis, Lucian Popa, Kun Qian

In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules.

Entity Resolution

Learning audio sequence representations for acoustic event classification

no code implementations27 Jul 2017 Zixing Zhang, Ding Liu, Jing Han, Kun Qian, Björn Schuller

Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.

General Classification

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