1,191 research outputs found
Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Neural dialog models often lack robustness to anomalous user input and
produce inappropriate responses which leads to frustrating user experience.
Although there are a set of prior approaches to out-of-domain (OOD) utterance
detection, they share a few restrictions: they rely on OOD data or multiple
sub-domains, and their OOD detection is context-independent which leads to
suboptimal performance in a dialog. The goal of this paper is to propose a
novel OOD detection method that does not require OOD data by utilizing
counterfeit OOD turns in the context of a dialog. For the sake of fostering
further research, we also release new dialog datasets which are 3 publicly
available dialog corpora augmented with OOD turns in a controllable way. Our
method outperforms state-of-the-art dialog models equipped with a conventional
OOD detection mechanism by a large margin in the presence of OOD utterances.Comment: ICASSP 201
Better Unrelated Machine Scheduling for Weighted Completion Time via Random Offsets from Non-Uniform Distributions
In this paper we consider the classic scheduling problem of minimizing total
weighted completion time on unrelated machines when jobs have release times,
i.e, using the three-field notation. For this
problem, a 2-approximation is known based on a novel convex programming (J. ACM
2001 by Skutella). It has been a long standing open problem if one can improve
upon this 2-approximation (Open Problem 8 in J. of Sched. 1999 by Schuurman and
Woeginger). We answer this question in the affirmative by giving a
1.8786-approximation. We achieve this via a surprisingly simple linear
programming, but a novel rounding algorithm and analysis. A key ingredient of
our algorithm is the use of random offsets sampled from non-uniform
distributions.
We also consider the preemptive version of the problem, i.e, . We again use the idea of sampling offsets from non-uniform
distributions to give the first better than 2-approximation for this problem.
This improvement also requires use of a configuration LP with variables for
each job's complete schedules along with more careful analysis. For both
non-preemptive and preemptive versions, we break the approximation barrier of 2
for the first time.Comment: 24 pages. To apper in FOCS 201
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