5 research outputs found
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
Product compatibility and their functionality are of utmost importance to
customers when they purchase products, and to sellers and manufacturers when
they sell products. Due to the huge number of products available online, it is
infeasible to enumerate and test the compatibility and functionality of every
product. In this paper, we address two closely related problems: product
compatibility analysis and function satisfiability analysis, where the second
problem is a generalization of the first problem (e.g., whether a product works
with another product can be considered as a special function). We first
identify a novel question and answering corpus that is up-to-date regarding
product compatibility and functionality information. To allow automatic
discovery product compatibility and functionality, we then propose a deep
learning model called Dual Attention Network (DAN). Given a QA pair for a
to-be-purchased product, DAN learns to 1) discover complementary products (or
functions), and 2) accurately predict the actual compatibility (or
satisfiability) of the discovered products (or functions). The challenges
addressed by the model include the briefness of QAs, linguistic patterns
indicating compatibility, and the appropriate fusion of questions and answers.
We conduct experiments to quantitatively and qualitatively show that the
identified products and functions have both high coverage and accuracy,
compared with a wide spectrum of baselines
Review Conversational Reading Comprehension
Inspired by conversational reading comprehension (CRC), this paper studies a
novel task of leveraging reviews as a source to build an agent that can answer
multi-turn questions from potential consumers of online businesses. We first
build a review CRC dataset and then propose a novel task-aware pre-tuning step
running between language model (e.g., BERT) pre-training and domain-specific
fine-tuning. The proposed pre-tuning requires no data annotation, but can
greatly enhance the performance on our end task. Experimental results show that
the proposed approach is highly effective and has competitive performance as
the supervised approach. The dataset is available at
\url{https://github.com/howardhsu/RCRC
Product Function Need Recognition via Semi-supervised Attention Network
Functionality is of utmost importance to customers when they purchase
products. However, it is unclear to customers whether a product can really
satisfy their needs on functions. Further, missing functions may be
intentionally hidden by the manufacturers or the sellers. As a result, a
customer needs to spend a fair amount of time before purchasing or just
purchase the product on his/her own risk. In this paper, we first identify a
novel QA corpus that is dense on product functionality information
\footnote{The annotated corpus can be found at
\url{https://www.cs.uic.edu/~hxu/}.}. We then design a neural network called
Semi-supervised Attention Network (SAN) to discover product functions from
questions. This model leverages unlabeled data as contextual information to
perform semi-supervised sequence labeling. We conduct experiments to show that
the extracted function have both high coverage and accuracy, compared with a
wide spectrum of baselines
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
One key task of fine-grained sentiment analysis of product reviews is to
extract product aspects or features that users have expressed opinions on. This
paper focuses on supervised aspect extraction using deep learning. Unlike other
highly sophisticated supervised deep learning models, this paper proposes a
novel and yet simple CNN model employing two types of pre-trained embeddings
for aspect extraction: general-purpose embeddings and domain-specific
embeddings. Without using any additional supervision, this model achieves
surprisingly good results, outperforming state-of-the-art sophisticated
existing methods. To our knowledge, this paper is the first to report such
double embeddings based CNN model for aspect extraction and achieve very good
results.Comment: ACL 201
Lifelong Domain Word Embedding via Meta-Learning
Learning high-quality domain word embeddings is important for achieving good
performance in many NLP tasks. General-purpose embeddings trained on
large-scale corpora are often sub-optimal for domain-specific applications.
However, domain-specific tasks often do not have large in-domain corpora for
training high-quality domain embeddings. In this paper, we propose a novel
lifelong learning setting for domain embedding. That is, when performing the
new domain embedding, the system has seen many past domains, and it tries to
expand the new in-domain corpus by exploiting the corpora from the past domains
via meta-learning. The proposed meta-learner characterizes the similarities of
the contexts of the same word in many domain corpora, which helps retrieve
relevant data from the past domains to expand the new domain corpus.
Experimental results show that domain embeddings produced from such a process
improve the performance of the downstream tasks.Comment: 7 page