Search Results for author:
Found 7 papers, 4 papers with code
GraphQ IR: Unifying Semantic Parsing of Graph Query Language with Intermediate Representation
Subject to the semantic gap lying between natural and formal language, neural semantic parsing is typically bottlenecked by the paucity and imbalance of data.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Schema-Free Dependency Parsing via Sequence Generation
To remedy these drawbacks, we propose to achieve universal and schema-free Dependency Parsing (DP) via Sequence Generation (SG) DPSG by utilizing only the pre-trained language model (PLM) without any auxiliary structures or parsing algorithms.
Program Transfer for Answering Complex Questions over Knowledge Bases
In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
OpenKE: An Open Toolkit for Knowledge Embedding
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
