41 citations · 150 across the 19 of their papers we have counts for
20 papers
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
Xingyu Fu, Sheng Zhang, Gukyeong Kwon +10
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Langua…
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
Wuwei Lan, Zhiguo Wang, Anuj Chauhan +15
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively e…
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Shuaichen Chang, Jun Wang, Mingwen Dong +13
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models ar…
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Donghan Yu, Sheng Zhang, Patrick Ng +7
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either gene…
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Andy T. Liu, Wei Xiao, Henghui Zhu +3
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label ef…
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
Dejiao Zhang, Wei Xiao, Henghui Zhu +2
Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain specific knowledge. This challenge is magnified in natu…
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu +5
Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world,…
Pairwise Supervised Contrastive Learning of Sentence Representations
Dejiao Zhang, Shang-Wen Li, Wei Xiao +4
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese los…
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
Alexander Hanbo Li, Patrick Ng, Peng Xu +3
The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information. However, a…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The f…
Supporting Clustering with Contrastive Learning
Dejiao Zhang, Feng Nan, Xiaokai Wei +6
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often ove…
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang +5
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models t…
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Shuyang Li, Jin Cao, Mukund Sridhar +4
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with…
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Peng Shi, Patrick Ng, Zhiguo Wang +5
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural langua…
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
Yifan Gao, Henghui Zhu, Patrick Ng +7
In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system ne…
An Ensemble Approach for Automatic Structuring of Radiology Reports
Morteza Pourreza Shahri, Amir Tahmasebi, Bingyang Ye +3
Automatic structuring of electronic medical records is of high demand for clinical workflow solutions to facilitate extraction, storage, and querying of patient care information. H…
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
Henghui Zhu, Feng Nan, Zhiguo Wang +2
Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of con…
Clinical Concept Extraction with Contextual Word Embedding
Henghui Zhu, Ioannis Ch. Paschalidis, Amir Tahmasebi
Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we…
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Henghui Zhu, Feng Nan, Ioannis Paschalidis +1
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number…
Learning Policies for Markov Decision Processes from Data
Manjesh K. Hanawal, Hao Liu, Henghui Zhu +1
We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the polic…