Neural Knowledge Transfer For Low-source Sentiment Analysis: Cross-domain, Cross-task & Cross-lingual

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Neural Knowledge Transfer For Low-source Sentiment Analysis: 
Cross-domain, Cross-task & Cross-lingual"

By

Mr. Zheng LI


Abstract

Opinions are key influences on human behaviors and are central to almost all 
human activities. Our cognition of the world and the decisions we make are 
considerably conditioned on how others see and evaluate the world. For this 
reason, sentiment analysis, aiming to automatically characterize human beings' 
opinions, stances, and attitudes from textual data, has been actively 
investigated over the past decades.

Recent advances in deep learning enable breakthroughs in a variety of NLP 
tasks. However, the huge success highly relies on the availability of massive 
labeled data, which hinders its potentials to the low-resource scenarios where 
the labeled data is scarce and costly to obtain. On the contrary, humans 
possess the ability to recognize new objects or perceive abstract concepts with 
a few examples. The significant gap between human learning ability and deep 
learning has spurred on a promising direction, namely transfer learning, which 
aims to leverage knowledge from a source domain, task, or language that is 
sufficiently labeled to improve the predictive learning in a target one with 
minimal supervision.

In this thesis, we focus on developing deep transfer learning methodologies for 
low-resource sentiment analysis (LRSA) at different levels, varying from 
coarse-grained sentiment analysis (CGSA) to fine-grained opinion mining, i.e., 
aspect-based sentiment analysis (ABSA) and end-to-end ABSA (E2E-ABSA). To 
coincide with existing limitations of these different subtasks, we consider 
different perspectives of knowledge, including cross-domain, cross-task, and 
cross-lingual settings, to be transferred.

Specifically, we begin with domain adaptation in CGSA and propose to address 
(1) how to explicitly and automatically identify both domain-invariant and 
domain-specific information as transferable knowledge to, to a considerable 
degree, minimize the discrepancy between domains. We further push the boundary 
to explore the less studied knowledge transfer in fine-grained opinion mining 
that concerns more with aspect-oriented opinions. In ABSA, we propose a new 
cross-task and cross-domain setting by considering the effect of aspects with 
different granularity, where we study (2) how to transfer aspect-specific 
knowledge cross both different aspect-based tasks and domains. However, the 
limitation of specifying the input aspects in advance for ABSA hinders its 
potential applications in practice. This inspires us to continuously explore 
(3) how to transfer cross-domain knowledge in E2E-ABSA that aims to jointly 
extract aspects and aspect-oriented sentiments across domains.  Due to the 
diversity of human languages around the world, cross-lingual sentiment analysis 
(CLSA) remains to be another critical problem, where both the feature space and 
feature distribution are different across languages. Motivated by human beings' 
ability to learn new tasks rapidly with a few examples by extracting 
accumulated meta-knowledge from previous tasks, we are also curious about (4) 
whether we can leverage previous cross-lingual transfer experiences to enhance 
the transfer effectiveness in new cross-lingual tasks. We evaluate and validate 
the proposed models and algorithms on multiple public and real-world industrial 
datasets. This thesis will also introduce the research frontier and points out 
promising research directions for future investigation.


Date:			Friday, 3 July 2020

Time:			10:00am - 12:00noon

Zoom Meeting:		https://hkust.zoom.com.cn/j/8075887760

Chairman:		Prof. Tao LIU (PHYS)

Committee Members:	Prof. Qiang YANG (Supervisor)
   			Prof. Kai CHEN
 			Prof. Yangqiu SONG
 			Prof. Yang WANG (MATH)
 			Prof. Yuhong GUO (Carleton University)


**** ALL are Welcome ****