BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis

NAACL 2019 Hu XuBing LiuLei ShuPhilip S. Yu

Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions.~We call this problem Review Reading Comprehension (RRC)... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Aspect-Based Sentiment Analysis SemEval 2014 Task 4 Sub Task 2 BERT-PT Restaurant (Acc) 84.95 # 4
Laptop (Acc) 78.07 # 7
Mean Acc (Restaurant + Laptop) 81.51 # 7

Methods used in the Paper