Search Results for author: Henghui Zhu

Found 26 papers, 16 papers with code

You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL

no code implementations18 Sep 2024 Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng

While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high inference cost and often overlooking crucial database knowledge.

Text-To-SQL

Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks

1 code implementation31 Jan 2024 Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang

Our analysis over the chain-of-thought generation of edited models further uncover key reasons behind the inadequacy of existing knowledge editing methods from a reasoning standpoint, involving aspects on fact-wise editing, fact recall ability, and coherence in generation.

counterfactual knowledge editing

UNITE: A Unified Benchmark for Text-to-SQL Evaluation

1 code implementation25 May 2023 Wuwei Lan, Zhiguo Wang, Anuj Chauhan, Henghui Zhu, Alexander Li, Jiang Guo, Sheng Zhang, Chung-Wei Hang, Joseph Lilien, Yiqun Hu, Lin Pan, Mingwen Dong, Jun Wang, Jiarong Jiang, Stephen Ash, Vittorio Castelli, Patrick Ng, Bing Xiang

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.

Text-To-SQL

DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

1 code implementation30 Sep 2022 Donghan Yu, Sheng Zhang, Patrick Ng, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Yiqun Hu, William Wang, Zhiguo Wang, Bing Xiang

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.

Entity Linking Question Answering +1

QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition

1 code implementation3 Mar 2022 Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold

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 efficiency.

Few-shot NER Named Entity Recognition +2

Virtual Augmentation Supported Contrastive Learning of Sentence Representations

2 code implementations Findings (ACL) 2022 Dejiao Zhang, Wei Xiao, Henghui Zhu, Xiaofei Ma, Andrew O. Arnold

We then define an instance discrimination task regarding this neighborhood and generate the virtual augmentation in an adversarial training manner.

Contrastive Learning Data Augmentation +2

Pairwise Supervised Contrastive Learning of Sentence Representations

1 code implementation EMNLP 2021 Dejiao Zhang, Shang-Wen Li, Wei Xiao, Henghui Zhu, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

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 loss.

Contrastive Learning Natural Language Inference +5

Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering

1 code implementation ACL 2021 Alexander Hanbo Li, Patrick Ng, Peng Xu, Henghui Zhu, Zhiguo Wang, Bing Xiang

However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.

Open-Domain Question Answering

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

3 code implementations18 Dec 2020 Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang

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 language models with self-supervised learning objectives, such as Masked Language Model (MLM).

Language Modelling Self-Supervised Learning +1

Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base

1 code implementation COLING 2020 Boran Hao, Henghui Zhu, Ioannis Paschalidis

Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain.

Language Modelling Natural Language Inference

An Ensemble Approach for Automatic Structuring of Radiology Reports

no code implementations EMNLP (ClinicalNLP) 2020 Morteza Pourreza Shahri, Amir Tahmasebi, Bingyang Ye, Henghui Zhu, Javed Aslam, Timothy Ferris

We present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels.

Sentence

Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

1 code implementation25 Nov 2019 Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to.

Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

no code implementations WS 2019 Elena Sergeeva, Henghui Zhu, Amir Tahmasebi, Peter Szolovits

Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks.

Negation Sentence

Clinical Concept Extraction with Contextual Word Embedding

1 code implementation24 Oct 2018 Henghui Zhu, Ioannis Ch. Paschalidis, Amir Tahmasebi

Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model.

Clinical Concept Extraction

Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget

no code implementations31 May 2017 Henghui Zhu, Feng Nan, Ioannis Paschalidis, Venkatesh Saligrama

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 of applications.

Decision Making Feature Engineering +2

Learning Policies for Markov Decision Processes from Data

no code implementations21 Jan 2017 Manjesh K. Hanawal, Hao liu, Henghui Zhu, Ioannis Ch. Paschalidis

We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs.

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