3238818
Descripción
Mapa Mental por Ivan Zapreev, actualizado hace más de 1 año
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Creado por Ivan Zapreev
hace más de 9 años
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|
Pairwise Neural Machine
Translation Evaluation
- Introduction
- Automated
Machine
Translation (MT)
- Evaluation
- Needed
- Developing
a new MT
- Comparing
two MT
- Developing
a new MT
- Reference based MT
- Comparing the
system output to
one or more
human reference
tranlations
- Most Common
- Compute
- Absolute quality score
- Computing similarity
between the machine and
human translation
- Computing similarity
between the machine and
human translation
- Simplest case
- Computing word N-gram
matches between the
translation and the
reference
- BLUE
- BLUE
- Computing word N-gram
matches between the
translation and the
reference
- More advanced
- Take into
account
various
aspects of
linguistic
similarity
- Better
correlation
with
human
judjment
- Better
correlation
with
human
judjment
- Take into
account
various
aspects of
linguistic
similarity
- Absolute quality score
- Comparing the
system output to
one or more
human reference
tranlations
- Human ranking
- Can be used
to train
automatic
metrics
- Can be
oriented to
predict
absolute scores
- Using
- Regression
- ?
- ?
- Ranking
- Special case
- Compare two
hypotheses
and referenec
- Decide
which
hypothesis
is better
- Recent result
- Guzman
- Learning framework
- Using
- Preference kernel
- ?
- ?
- Vector
machines
(SVM)
- Syntactic structures
- Discourage-based
structures
- Preference kernel
- High computational costs
- Training
- Testing
- Due to
- Using
convolution
kernels
- ?
- ?
- Over
complex
structures
- Using
convolution
kernels
- Simplification is needed!
- Training
- Using
- Learning framework
- Guzman
- Recent result
- Decide
which
hypothesis
is better
- Compare two
hypotheses
and referenec
- Special case
- Regression
- Using
- Can be
oriented to
predict
absolute scores
- Can be used
to train
automatic
metrics
- Needed
- Evaluation
- Automated
Machine
Translation (MT)
- Research
- Framework
for machine
translation
evaluation
- Novel!
- Goal
- Select a better
translation from a
pair of hypothesis,
given the reference
translation
- Select a better
translation from a
pair of hypothesis,
given the reference
translation
- Using
- Neural networks
- Multi-layer
- Input layer
- Semantic info
- Syntactic info
- Lexical info
- Semantic info
- Hidden layer
- Captures the
interactions
between the
relevant input
components
- Captures the
interactions
between the
relevant input
components
- Input layer
- Models
the
interaction
between
- The two
hypothesis
- Reference and
Hypothesis
- The two
hypothesis
- Multi-layer
- Distributed vector
representations
- Used for storing
- The Two
hypothesis
- The Two
hypothesis
- Based on
- Word embedding
- Sentence embedding
- Word embedding
- Learned from
- Neural Networks
- Novel!
- Can be trained to
optimize
task-specific cost
function
- Novel!
- Neural Networks
- Efficient
- Vector-based compression
- ?
- ?
- Vector-based compression
- Used for storing
- Neural networks
- Experiments
- WMT12 metrics task
- Better results
than by
Guzman
- High
correlation with
human judjment
- Comparable
with the best!
- DiscoTK
- Metric
- Combination based
- Much heavier
- Much heavier
- Combination based
- Metric
- DiscoTK
- Embeddings
- Syntactically oriented
- Semantically oriented
- Cumulative performance gains
- Over
- BLUE
- NIST
- METEOR
- TER
- BLUE
- Over
- Syntactically oriented
- WMT12 metrics task
- Novel!
- Simplification is needed!
- Framework
for machine
translation
evaluation
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