2 research outputs found
Incremental Knowledge Based Question Answering
In the past years, Knowledge-Based Question Answering (KBQA), which aims to
answer natural language questions using facts in a knowledge base, has been
well developed. Existing approaches often assume a static knowledge base.
However, the knowledge is evolving over time in the real world. If we directly
apply a fine-tuning strategy on an evolving knowledge base, it will suffer from
a serious catastrophic forgetting problem. In this paper, we propose a new
incremental KBQA learning framework that can progressively expand learning
capacity as humans do. Specifically, it comprises a margin-distilled loss and a
collaborative exemplar selection method, to overcome the catastrophic
forgetting problem by taking advantage of knowledge distillation. We reorganize
the SimpleQuestion dataset to evaluate the proposed incremental learning
solution to KBQA. The comprehensive experiments demonstrate its effectiveness
and efficiency when working with the evolving knowledge base
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
The study of question answering has received increasing attention in recent
years. This work focuses on providing an answer that compatible with both user
intent and conditioning information corresponding to the question, such as
delivery status and stock information in e-commerce. However, these conditions
may be wrong or incomplete in real-world applications. Although existing
question answering systems have considered the external information, such as
categorical attributes and triples in knowledge base, they all assume that the
external information is correct and complete. To alleviate the effect of
defective condition values, this paper proposes condition aware and revise
Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value
based on the whole conversation and original conditions values, and (2) it
encodes the revised conditions and utilizes the conditions embedding to select
an answer. Experimental results on a real-world customer service dataset
demonstrate that the CAR-Transformer can still select an appropriate reply when
conditions corresponding to the question exist wrong or missing values, and
substantially outperforms baseline models on automatic and human evaluations.
The proposed CAR-Transformer can be extended to other NLP tasks which need to
consider conditioning information