40 research outputs found
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally
guide a dialogue agent or human to achieve specific goals, topics or keywords
during open-ended conversations. Existing methods mainly rely on single-turn
datadriven learning and simple target-guided strategy without considering
semantic or factual knowledge relations among candidate topics/keywords. This
results in poor transition smoothness and low success rate. In this work, we
adopt a structured approach that controls the intended content of system
responses by introducing coarse-grained keywords, attains smooth conversation
transition through turn-level supervised learning and knowledge relations
between candidate keywords, and drives an conversation towards an specified
target with discourse-level guiding strategy. Specially, we propose a novel
dynamic knowledge routing network (DKRN) which considers semantic knowledge
relations among candidate keywords for accurate next topic prediction of next
discourse. With the help of more accurate keyword prediction, our
keyword-augmented response retrieval module can achieve better retrieval
performance and more meaningful conversations. Besides, we also propose a novel
dual discourse-level target-guided strategy to guide conversations to reach
their goals smoothly with higher success rate. Furthermore, to push the
research boundary of target-guided open-domain conversation to match real-world
scenarios better, we introduce a new large-scale Chinese target-guided
open-domain conversation dataset (more than 900K conversations) crawled from
Sina Weibo. Quantitative and human evaluations show our method can produce
meaningful and effective target-guided conversations, significantly improving
over other state-of-the-art methods by more than 20% in success rate and more
than 0.6 in average smoothness score.Comment: 8 pages, 2 figues, 6tables, AAAI2020, fix our model's abbreviatio
Outlier-Robust Gromov-Wasserstein for Graph Data
Gromov-Wasserstein (GW) distance is a powerful tool for comparing and
aligning probability distributions supported on different metric spaces.
Recently, GW has become the main modeling technique for aligning heterogeneous
data for a wide range of graph learning tasks. However, the GW distance is
known to be highly sensitive to outliers, which can result in large
inaccuracies if the outliers are given the same weight as other samples in the
objective function. To mitigate this issue, we introduce a new and robust
version of the GW distance called RGW. RGW features optimistically perturbed
marginal constraints within a Kullback-Leibler divergence-based ambiguity set.
To make the benefits of RGW more accessible in practice, we develop a
computationally efficient and theoretically provable procedure using Bregman
proximal alternating linearized minimization algorithm. Through extensive
experimentation, we validate our theoretical results and demonstrate the
effectiveness of RGW on real-world graph learning tasks, such as subgraph
matching and partial shape correspondence
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness
of missing data. Many efforts try to first draft a completed data and second
refine to derive the imputation results, or "draft-then-refine" for short. In
this work, we analyze this widespread practice from the perspective of
Dirichlet energy. We find that a rudimentary "draft" imputation will decrease
the Dirichlet energy, thus an energy-maintenance "refine" step is in need to
recover the overall energy. Since existing "refine" methods such as Graph
Convolutional Network (GCN) tend to cause further energy decline, in this work,
we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to
preserve Dirichlet energy and improve imputation performance. GLPN consists of
a U-shaped autoencoder and residual networks to capture global and local
detailed information respectively. By extensive experiments on several
real-world datasets, GLPN shows superior performance over state-of-the-art
methods under three different missing mechanisms. Our source code is available
at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Developing conversational agents to interact with patients and provide
primary clinical advice has attracted increasing attention due to its huge
application potential, especially in the time of COVID-19 Pandemic. However,
the training of end-to-end neural-based medical dialogue system is restricted
by an insufficient quantity of medical dialogue corpus. In this work, we make
the first attempt to build and release a large-scale high-quality Medical
Dialogue dataset related to 12 types of common Gastrointestinal diseases named
MedDG, with more than 17K conversations collected from the online health
consultation community. Five different categories of entities, including
diseases, symptoms, attributes, tests, and medicines, are annotated in each
conversation of MedDG as additional labels. To push forward the future research
on building expert-sensitive medical dialogue system, we proposes two kinds of
medical dialogue tasks based on MedDG dataset. One is the next entity
prediction and the other is the doctor response generation. To acquire a clear
comprehension on these two medical dialogue tasks, we implement several
state-of-the-art benchmarks, as well as design two dialogue models with a
further consideration on the predicted entities. Experimental results show that
the pre-train language models and other baselines struggle on both tasks with
poor performance in our dataset, and the response quality can be enhanced with
the help of auxiliary entity information. From human evaluation, the simple
retrieval model outperforms several state-of-the-art generative models,
indicating that there still remains a large room for improvement on generating
medically meaningful responses.Comment: Data and code are available at https://github.com/lwgkzl/MedD
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
In this paper, we study the design and analysis of a class of efficient
algorithms for computing the Gromov-Wasserstein (GW) distance tailored to
large-scale graph learning tasks. Armed with the Luo-Tseng error bound
condition~\citep{luo1992error}, two proposed algorithms, called Bregman
Alternating Projected Gradient (BAPG) and hybrid Bregman Proximal Gradient
(hBPG) enjoy the convergence guarantees. Upon task-specific properties, our
analysis further provides novel theoretical insights to guide how to select the
best-fit method. As a result, we are able to provide comprehensive experiments
to validate the effectiveness of our methods on a host of tasks, including
graph alignment, graph partition, and shape matching. In terms of both
wall-clock time and modeling performance, the proposed methods achieve
state-of-the-art results
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure