13 research outputs found
EQG-RACE: Examination-Type Question Generation
Question Generation (QG) is an essential component of the automatic
intelligent tutoring systems, which aims to generate high-quality questions for
facilitating the reading practice and assessments. However, existing QG
technologies encounter several key issues concerning the biased and unnatural
language sources of datasets which are mainly obtained from the Web (e.g.
SQuAD). In this paper, we propose an innovative Examination-type Question
Generation approach (EQG-RACE) to generate exam-like questions based on a
dataset extracted from RACE. Two main strategies are employed in EQG-RACE for
dealing with discrete answer information and reasoning among long contexts. A
Rough Answer and Key Sentence Tagging scheme is utilized to enhance the
representations of input. An Answer-guided Graph Convolutional Network (AG-GCN)
is designed to capture structure information in revealing the inter-sentences
and intra-sentence relations. Experimental results show a state-of-the-art
performance of EQG-RACE, which is apparently superior to the baselines. In
addition, our work has established a new QG prototype with a reshaped dataset
and QG method, which provides an important benchmark for related research in
future work. We will make our data and code publicly available for further
research.Comment: Accepted by AAAI-202
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Learning to Ask Conversational Questions by Optimizing Levenshtein Distance
Conversational Question Simplification (CQS) aims to simplify self-contained
questions into conversational ones by incorporating some conversational
characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood
estimation (MLE) based methods often get trapped in easily learned tokens as
all tokens are treated equally during training. In this work, we introduce a
Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the
minimum Levenshtein distance (MLD) through explicit editing actions. RISE is
able to pay attention to tokens that are related to conversational
characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT)
algorithm with a Dynamic Programming based Sampling (DPS) process to improve
exploration. Experimental results on two benchmark datasets show that RISE
significantly outperforms state-of-the-art methods and generalizes well on
unseen data.Comment: 13 pages, 4 figures, Published in ACL 202
Low-Resource Response Generation with Template Prior
We study open domain response generation with limited message-response pairs.
The problem exists in real-world applications but is less explored by the
existing work. Since the paired data now is no longer enough to train a neural
generation model, we consider leveraging the large scale of unpaired data that
are much easier to obtain, and propose response generation with both paired and
unpaired data. The generation model is defined by an encoder-decoder
architecture with templates as prior, where the templates are estimated from
the unpaired data as a neural hidden semi-markov model. By this means, response
generation learned from the small paired data can be aided by the semantic and
syntactic knowledge in the large unpaired data. To balance the effect of the
prior and the input message to response generation, we propose learning the
whole generation model with an adversarial approach. Empirical studies on
question response generation and sentiment response generation indicate that
when only a few pairs are available, our model can significantly outperform
several state-of-the-art response generation models in terms of both automatic
and human evaluation.Comment: Accepted by EMNLP201