6 citations · 22 across the 12 of their papers we have counts for
8 papers · 1 filter
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering
Jinxin Liu, Shulin Cao, Jiaxin Shi +5
Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases. A typical approach to KBQA is semantic parsing, which translat…
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering
Jiajie Zhang, Shulin Cao, Tingjia Zhang +5
Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single know…
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Zhongwei Wan, Yichun Yin, Wei Zhang +5
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs…
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi +5
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and…
Schema-Free Dependency Parsing via Sequence Generation
Boda Lin, Zijun Yao, Jiaxin Shi +6
Dependency parsing aims to extract syntactic dependency structure or semantic dependency structure for sentences. Existing methods suffer the drawbacks of lacking universality or h…
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Shulin Cao, Jiaxin Shi, Liangming Pan +6
Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribut…
Learning to Embed Sentences Using Attentive Recursive Trees
Jiaxin Shi, Lei Hou, Juanzi Li +2
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks…
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Jiaxin Shi, Chen Liang, Lei Hou +3
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience scor…