Search Results for author: Lizhen Qu

Found 41 papers, 17 papers with code

Personal Information Leakage Detection in Conversations

1 code implementation EMNLP 2020 Qiongkai Xu, Lizhen Qu, Zeyu Gao, Gholamreza Haffari

In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.

Language Modelling

FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

1 code implementation27 May 2023 Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu, Gholamreza Haffari, Fei Li, Donghong Ji, Quan Hung Tran

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.

Graph Similarity Image Captioning +3

The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning

no code implementations22 May 2023 Zhuang Li, Lizhen Qu, Philip R. Cohen, Raj V. Tumuluri, Gholamreza Haffari

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.

Active Learning Semantic Parsing

Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding

1 code implementation8 May 2023 Bhanu Prakash Voutharoja, Lizhen Qu, Fatemeh Shiri

Our model parses a form into a word-relation graph in order to identify entities and relations jointly and reduce the time complexity of inference.

Semantic Parsing

Turning Flowchart into Dialog: Plan-based Data Augmentation for Low-Resource Flowchart-grounded Troubleshooting Dialogs

no code implementations2 May 2023 Haolan Zhan, Sameen Maruf, Lizhen Qu, YuFei Wang, Ingrid Zukerman, Gholamreza Haffari

Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (eg., vehicle, laptop), have been gaining research interest in recent years.

Data Augmentation Response Generation +1

Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery

1 code implementation2 Mar 2023 Tao Feng, Lizhen Qu, Gholamreza Haffari

In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work.

Causal Discovery Informativeness +1

Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation

no code implementations16 Feb 2023 Minghao Wu, George Foster, Lizhen Qu, Gholamreza Haffari

Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.

Machine Translation Translation

When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods

1 code implementation20 Dec 2022 Zhuo Zhang, Yuanhang Yang, Yong Dai, Lizhen Qu, Zenglin Xu

To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently.

Federated Learning

Let's Negotiate! A Survey of Negotiation Dialogue Systems

no code implementations18 Dec 2022 Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Gholamreza Haffari

Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.

Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation

1 code implementation27 Feb 2022 Zhuang Li, Lizhen Qu, Qiongkai Xu, Tongtong Wu, Tianyang Zhan, Gholamreza Haffari

In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.

Data Augmentation Disentanglement +3

Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation

1 code implementation10 Nov 2021 Chuang Lin, Yi Jiang, Jianfei Cai, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan

Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving.

Navigate Vision and Language Navigation

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers

1 code implementation EMNLP 2021 Zhuang Li, Lizhen Qu, Gholamreza Haffari

We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.

Continual Learning Semantic Parsing

On Robustness of Neural Semantic Parsers

no code implementations EACL 2021 Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan

In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.

Data Augmentation Semantic Parsing

Context Dependent Semantic Parsing: A Survey

1 code implementation COLING 2020 Zhuang Li, Lizhen Qu, Gholamreza Haffari

Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.

Semantic Parsing

COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering

1 code implementation COLING 2020 Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Gholamreza Haffari

However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations.

Question Answering

Privacy-Aware Text Rewriting

no code implementations WS 2019 Qiongkai Xu, Lizhen Qu, Chenchen Xu, Ran Cui

Biased decisions made by automatic systems have led to growing concerns in research communities.

Fairness Translation

ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

1 code implementation IJCNLP 2019 Qiongkai Xu, Chenchen Xu, Lizhen Qu

In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer.

Fairness Style Transfer +2

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

no code implementations ICLR 2019 Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu

Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.

Computer Security Vulnerability Detection

f-GANs in an Information Geometric Nutshell

1 code implementation NeurIPS 2017 Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson

In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --- and show tight connections with the three other key GAN parameters: loss, game and architecture.

Demographic Inference on Twitter using Recursive Neural Networks

no code implementations ACL 2017 Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, C{\'e}cile Paris

In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience.

Network Embedding

Collective Vertex Classification Using Recursive Neural Network

no code implementations24 Jan 2017 Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu

In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.

Classification General Classification

Automatic Generation of Grounded Visual Questions

no code implementations20 Dec 2016 Shijie Zhang, Lizhen Qu, ShaoDi You, Zhenglu Yang, Jiawan Zhang

In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image.

Question Generation Question-Generation

Named Entity Recognition for Novel Types by Transfer Learning

no code implementations EMNLP 2016 Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Timothy Baldwin

In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly.

named-entity-recognition Named Entity Recognition +2

Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

2 code implementations CVPR 2017 Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, Lizhen Qu

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.

Ranked #2 on Image Classification on Clothing1M (using clean data) (using extra training data)

Learning with noisy labels Noise Estimation

STransE: a novel embedding model of entities and relationships in knowledge bases

1 code implementation NAACL 2016 Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, Mark Johnson

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.

Knowledge Base Completion Link Prediction +1

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks

no code implementations21 Apr 2015 Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Nathan Schneider, Timothy Baldwin

Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications.

Chunking NER +3

Estimating Maximally Probable Constrained Relations by Mathematical Programming

no code implementations4 Aug 2014 Lizhen Qu, Bjoern Andres

Estimating (learning) a maximally probable measure, given (a training set of) related and unrelated pairs, is a convex optimization problem.

Clustering General Classification +1

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