37 research outputs found
Addressing Complex and Subjective Product-Related Queries with Customer Reviews
Online reviews are often our first port of call when considering products and
purchases online. When evaluating a potential purchase, we may have a specific
query in mind, e.g. `will this baby seat fit in the overhead compartment of a
747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer
such questions we must either wade through huge volumes of consumer reviews
hoping to find one that is relevant, or otherwise pose our question directly to
the community via a Q/A system.
In this paper we hope to fuse these two paradigms: given a large volume of
previously answered queries about products, we hope to automatically learn
whether a review of a product is relevant to a given query. We formulate this
as a machine learning problem using a mixture-of-experts-type framework---here
each review is an `expert' that gets to vote on the response to a particular
query; simultaneously we learn a relevance function such that `relevant'
reviews are those that vote correctly. At test time this learned relevance
function allows us to surface reviews that are relevant to new queries
on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million
questions (and answers) and 13 million reviews. We show quantitatively that it
is effective at addressing both binary and open-ended queries, and
qualitatively that it surfaces reviews that human evaluators consider to be
relevant.Comment: WWW 2016; 14 pages, 5 figure
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
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems
We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models
for reliably recommending the N top-ranked items. The proposed models optimize
a variant of the widely used discounted cumulative gain (DCG) objective
function which differs from DCG in two important aspects: (i) It limits the
evaluation of DCG only on the top N items in the ranked lists, thereby
eliminating the impact of low-ranked items on the learned ranking function; and
(ii) it incorporates weights that allow the model to leverage multiple types of
implicit feedback with differing levels of reliability or trustworthiness.
Because the resulting objective function is non-smooth and hence challenging to
optimize, we consider two smooth approximations of the objective function,
using the traditional sigmoid function and the rectified linear unit (ReLU). We
propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any
smooth objective function. Then, a more efficient variant, Top-N-Rank.ReLU, is
introduced, which effectively exploits the properties of ReLU function to
reduce the computational complexity of Top-N-Rank from quadratic to linear in
the average number of items rated by users. The results of our experiments
using two widely used benchmarks, namely, the MovieLens data set and the Amazon
Video Games data set demonstrate that: (i) The `top-N truncation' of the
objective function substantially improves the ranking quality of the top N
recommendations; (ii) using the ReLU for smoothing the objective function
yields significant improvement in both ranking quality as well as runtime as
compared to using the sigmoid; and (iii) Top-N-Rank.ReLU substantially
outperforms the well-performing list-wise ranking methods in terms of ranking
quality.Comment: paper accepted by the 2018 IEEE International Conference on Big Dat
A Repository of Conversational Datasets
Progress in Machine Learning is often driven by the availability of large
datasets, and consistent evaluation metrics for comparing modeling approaches.
To this end, we present a repository of conversational datasets consisting of
hundreds of millions of examples, and a standardised evaluation procedure for
conversational response selection models using '1-of-100 accuracy'. The
repository contains scripts that allow researchers to reproduce the standard
datasets, or to adapt the pre-processing and data filtering steps to their
needs. We introduce and evaluate several competitive baselines for
conversational response selection, whose implementations are shared in the
repository, as well as a neural encoder model that is trained on the entire
training set
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
Open-domain question answering remains a challenging task as it requires
models that are capable of understanding questions and answers, collecting
useful information, and reasoning over evidence. Previous work typically
formulates this task as a reading comprehension or entailment problem given
evidence retrieved from search engines. However, existing techniques struggle
to retrieve indirectly related evidence when no directly related evidence is
provided, especially for complex questions where it is hard to parse precisely
what the question asks. In this paper we propose a retriever-reader model that
learns to attend on essential terms during the question answering process. We
build (1) an essential term selector which first identifies the most important
words in a question, then reformulates the query and searches for related
evidence; and (2) an enhanced reader that distinguishes between essential terms
and distracting words to predict the answer. We evaluate our model on multiple
open-domain multiple-choice QA datasets, notably performing at the level of the
state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset
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
Open Information Extraction from Question-Answer Pairs
Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the information that users care about, such information is
particularly desirable to extend a knowledge base with.
NeurON addresses several challenges. First, an answer text is often hard to
understand without knowing the question, and second, relevant information can
span multiple sentences. To address these, NeurON formulates extraction as a
multi-source sequence-to-sequence learning task, wherein it combines
distributed representations of a question and an answer to generate knowledge
facts. We describe experiments on two real-world datasets that demonstrate that
NeurON can find a significant number of new and interesting facts to extend a
knowledge base compared to state-of-the-art OpenIE methods.Comment: NAACL 201
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
The users often have many product-related questions before they make a
purchase decision in E-commerce. However, it is often time-consuming to examine
each user review to identify the desired information. In this paper, we propose
a novel review-driven framework for answer generation for product-related
questions in E-commerce, named RAGE. We develope RAGE on the basis of the
multi-layer convolutional architecture to facilitate speed-up of answer
generation with the parallel computation. For each question, RAGE first
extracts the relevant review snippets from the reviews of the corresponding
product. Then, we devise a mechanism to identify the relevant information from
the noise-prone review snippets and incorporate this information to guide the
answer generation. The experiments on two real-world E-Commerce datasets show
that the proposed RAGE significantly outperforms the existing alternatives in
producing more accurate and informative answers in natural language. Moreover,
RAGE takes much less time for both model training and answer generation than
the existing RNN based generation models
Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing
Knowledge bases (KBs) have attracted increasing attention due to its great
success in various areas, such as Web and mobile search.Existing KBs are
restricted to objective factual knowledge, such as city population or fruit
shape, whereas,subjective knowledge, such as big city, which is commonly
mentioned in Web and mobile queries, has been neglected. Subjective knowledge
differs from objective knowledge in that it has no documented or observed
ground truth. Instead, the truth relies on people's dominant opinion. Thus, we
can use the crowdsourcing technique to get opinion from the crowd. In our work,
we propose a system, called crowdsourced subjective knowledge acquisition
(CoSKA),for subjective knowledge acquisition powered by crowdsourcing and
existing KBs. The acquired knowledge can be used to enrich existing KBs in the
subjective dimension which bridges the gap between existing objective knowledge
and subjective queries.The main challenge of CoSKA is the conflict between
large scale knowledge facts and limited crowdsourcing resource. To address this
challenge, in this work, we define knowledge inference rules and then select
the seed knowledge judiciously for crowdsourcing to maximize the inference
power under the resource constraint. Our experimental results on real knowledge
base and crowdsourcing platform verify the effectiveness of CoSKA system