114 citations · 390 across the 23 of their papers we have counts for
24 papers
Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding
Bhanu Prakash Voutharoja, Lizhen Qu, Fatemeh Shiri
Recent works on form understanding mostly employ multimodal transformers or large-scale pre-trained language models. These models need ample data for pre-training. In contrast, hum…
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Minghao Wu, George Foster, Lizhen Qu +1
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.…
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
Zhuo Zhang, Yuanhang Yang, Yong Dai +2
With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks.…
Learning Object-Language Alignments for Open-Vocabulary Object Detection
Chuang Lin, Peize Sun, Yi Jiang +5
Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding…
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei +5
We introduce ViLPAct, a novel vision-language benchmark for human activity planning. It is designed for a task where embodied AI agents can reason and forecast future actions of hu…
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
Zhuang Li, Lizhen Qu, Qiongkai Xu +3
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 l…
Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation
Chuang Lin, Yi Jiang, Jianfei Cai +3
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…
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
Sheng Bi, Xiya Cheng, Yuan-Fang Li +5
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation. In this pap…
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
Zhuang Li, Lizhen Qu, Gholamreza Haffari
This paper investigates continual learning for semantic parsing. In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previ…
Neural-Symbolic Commonsense Reasoner with Relation Predictors
Farhad Moghimifar, Lizhen Qu, Yue Zhuo +2
Commonsense reasoning aims to incorporate sets of commonsense facts, retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about ordinary situations. The dynamic na…
On Robustness of Neural Semantic Parsers
Shuo Huang, Zhuang Li, Lizhen Qu +1
Semantic parsing maps natural language (NL) utterances into logical forms (LFs), which underpins many advanced NLP problems. Semantic parsers gain performance boosts with deep neur…
Few-Shot Semantic Parsing for New Predicates
Zhuang Li, Lizhen Qu, Shuo Huang +1
In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with utterance-logical form pairs per new predicate.…
COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering
Farhad Moghimifar, Lizhen Qu, Yue Zhuo +2
Commonsense reasoning refers to the ability of evaluating a social situation and acting accordingly. Identification of the implicit causes and effects of a social context is the dr…
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
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 simplifi…
D-PAGE: Diverse Paraphrase Generation
Qiongkai Xu, Juyan Zhang, Lizhen Qu +2
In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs…
f-GANs in an Information Geometric Nutshell
Richard Nock, Zac Cranko, Aditya Krishna Menon +2
Nowozin \textit{et al} showed last year how to extend the GAN \textit{principle} to all -divergences. The approach is elegant but falls short of a full description of the superv…
Collective Vertex Classification Using Recursive Neural Network
Qiongkai Xu, Qing Wang, Chenchen Xu +1
Collective classification of vertices is a task of assigning categories to each vertex in a graph based on both vertex attributes and link structure. Nevertheless, some existing ap…
Automatic Generation of Grounded Visual Questions
Shijie Zhang, Lizhen Qu, Shaodi You +2
In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic…
Named Entity Recognition for Novel Types by Transfer Learning
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +2
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. In this paper, we propose a…
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon +2
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss…
STransE: a novel embedding model of entities and relationships in knowledge bases
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases…
Neighborhood Mixture Model for Knowledge Base Completion
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mi…
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +3
Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing application…
Estimating Maximally Probable Constrained Relations by Mathematical Programming
Lizhen Qu, Bjoern Andres
Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified ele…