179 citations · 1.4k across the 46 of their papers we have counts for
46 papers
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
Yusen Zhang, Jun Wang, Zhiguo Wang +1
Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms…
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…
Benchmarking Diverse-Modal Entity Linking with Generative Models
Sijia Wang, Alexander Hanbo Li, Henry Zhu +9
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality…
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…
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
Yiyun Zhao, Jiarong Jiang, Yiqun Hu +14
Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and di…
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…
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
Jun Wang, Patrick Ng, Alexander Hanbo Li +5
Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing…
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
Sheng Zhang, Patrick Ng, Zhiguo Wang +1
Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extr…
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…
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Peng Shi, Tao Yu, Patrick Ng +1
In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better enc…
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…
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…
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…
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Siamak Shakeri, Cicero Nogueira dos Santos, Henry Zhu +5
We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate bo…
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
Alexander R. Fabbri, Patrick Ng, Zhiguo Wang +2
Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has bee…
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…
Universal Text Representation from BERT: An Empirical Study
Xiaofei Ma, Zhiguo Wang, Patrick Ng +2
We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transfe…
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
Zhiguo Wang, Patrick Ng, Xiaofei Ma +2
BERT model has been successfully applied to open-domain QA tasks. However, previous work trains BERT by viewing passages corresponding to the same question as independent training…
Multi-Granular Text Encoding for Self-Explaining Categorization
Zhiguo Wang, Yue Zhang, Mo Yu +5
Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input…
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Kun Xu, Liwei Wang, Mo Yu +4
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities…
Semantic Neural Machine Translation using AMR
Linfeng Song, Daniel Gildea, Yue Zhang +2
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many…