28,115 research outputs found
Discourse relations and conjoined VPs: automated sense recognition
Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (inter-sentential) and within sentences (intra-sentential), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification system for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
The key idea behind the unsupervised learning of disentangled representations
is that real-world data is generated by a few explanatory factors of variation
which can be recovered by unsupervised learning algorithms. In this paper, we
provide a sober look at recent progress in the field and challenge some common
assumptions. We first theoretically show that the unsupervised learning of
disentangled representations is fundamentally impossible without inductive
biases on both the models and the data. Then, we train more than 12000 models
covering most prominent methods and evaluation metrics in a reproducible
large-scale experimental study on seven different data sets. We observe that
while the different methods successfully enforce properties ``encouraged'' by
the corresponding losses, well-disentangled models seemingly cannot be
identified without supervision. Furthermore, increased disentanglement does not
seem to lead to a decreased sample complexity of learning for downstream tasks.
Our results suggest that future work on disentanglement learning should be
explicit about the role of inductive biases and (implicit) supervision,
investigate concrete benefits of enforcing disentanglement of the learned
representations, and consider a reproducible experimental setup covering
several data sets
Committee-Based Sample Selection for Probabilistic Classifiers
In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger
The Economic Costs and Benefits of Self-Managed Teams Among Skilled Technicians
This paper estimates the economic costs and benefits of implementing teams among highly-skilled technicians in a large regional telecommunications company. It matches individual survey and objective performance data for 230 employees in matched pairs of traditionally-supervised and self-managed groups. Multivariate regressions with appropriate controls show that teams do the work of supervisors in 60-70% less time, reducing indirect labor costs by 75 percent per team. Objective measures of quality and labor productivity are unaffected. Team members receive additional overtime pay that represents a 4-5 percent annual wage premium, which may be viewed alternatively as a share in the productivity gains associated with innovation or as a premium for learning skills
- …