Unsupervised deep structured semantic models for commonsense reasoning
Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models base...
Saved in:
Main Authors: | WANG, Shuohang, ZHANG, Sheng, SHEN, Yelong, LIU, Xiaodong, LIU, Jingjing, GAO, Jianfeng, JIANG, Jing |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2019
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4789 https://ink.library.smu.edu.sg/context/sis_research/article/5792/viewcontent/Unsupervised_deep_structured_semantic_models_2019_NACL_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
NOAHQA: Numerical reasoning with interpretable graph question answering dataset
by: ZHANG, Qiyuan, et al.
Published: (2021) -
Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning
by: LIM, Ee Peng, et al.
Published: (1992) -
Unsupervised Information Extraction with Distributional Prior Knowledge
by: LEUNG, Cane Wing-ki, et al.
Published: (2011) -
Multi-level head-wise match and aggregation in transformer for textual sequence matching
by: WANG, Shuohang, et al.
Published: (2020) -
Semantic-Sensitive Classification for Large Image Library
by: SHEN, Jialie, et al.
Published: (2005)