16,720 research outputs found
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus
In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries.Comment: Accepted to Information Retrieval Journa
Reified Context Models
A classic tension exists between exact inference in a simple model and
approximate inference in a complex model. The latter offers expressivity and
thus accuracy, but the former provides coverage of the space, an important
property for confidence estimation and learning with indirect supervision. In
this work, we introduce a new approach, reified context models, to reconcile
this tension. Specifically, we let the amount of context (the arity of the
factors in a graphical model) be chosen "at run-time" by reifying it---that is,
letting this choice itself be a random variable inside the model. Empirically,
we show that our approach obtains expressivity and coverage on three natural
language tasks
Learning Semantic Correspondences in Technical Documentation
We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.Comment: accepted to ACL-201
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