|
translation ( MT ) systems
</term>
. We believe
|
that
|
these
<term>
evaluation techniques
</term>
|
#583
We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. |
|
language learning experiment
</term>
showed
|
that
|
<term>
assessors
</term>
can differentiate
<term>
|
#633
A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. |
|
with
<term>
intelligent mobile agents
</term>
|
that
|
mediate between
<term>
users
</term>
and
<term>
|
#806
We integrate a spoken language understanding system with intelligent mobile agentsthat mediate between users and information sources. |
|
<term>
language models ( LMs )
</term>
. We find
|
that
|
simple
<term>
interpolation methods
</term>
|
#1046
We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
|
</term>
. We provide experimental results
|
that
|
clearly show the need for a
<term>
dynamic
|
#1135
We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. |
|
performance
</term>
further . We suggest a method
|
that
|
mimics the behavior of the
<term>
oracle
</term>
|
#1156
We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. |
|
from
<term>
training data
</term>
. We show
|
that
|
the trained
<term>
SPR
</term>
learns to select
|
#1435
We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. |
|
two distinct
<term>
datasets
</term>
, we find
|
that
|
<term>
indexing
</term>
according to simple
|
#1538
Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. |
|
but much faster . We also provide evidence
|
that
|
our findings are scalable . The theoretical
|
#1591
We also provide evidence that our findings are scalable. |
|
<term>
queries
</term>
containing them . I show
|
that
|
the
<term>
performance
</term>
of a
<term>
search
|
#1873
I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. |
|
approximation of the
<term>
formal analysis
</term>
|
that
|
is compatible with the
<term>
search engine
|
#1892
I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysisthat is compatible with the search engine's operational semantics. |
|
semantics
</term>
. The value of this approach is
|
that
|
as the
<term>
operational semantics
</term>
|
#1909
The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. |
|
</term>
of
<term>
Minimalist grammars
</term>
,
|
that
|
are
<term>
Stabler 's formalization
</term>
|
#1935
We provide a logical definition of Minimalist grammars, that are Stabler's formalization of Chomsky's minimalist program. |
|
baseline sentence planners
</term>
. We show
|
that
|
the
<term>
trainable sentence planner
</term>
|
#2101
We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system. |
|
for
<term>
utterance classification
</term>
|
that
|
does not require
<term>
manual transcription
|
#2213
This paper describes a method for utterance classificationthat does not require manual transcription of training data. |
|
utterance classification performance
</term>
|
that
|
is surprisingly close to what can be achieved
|
#2238
The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performancethat is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. |
|
multi-level answer resolution algorithm
</term>
|
that
|
combines results from the
<term>
answering
|
#2379
We present our multi-level answer resolution algorithmthat combines results from the answering agents at the question, passage, and/or answer levels. |
|
<term>
annotation experiment
</term>
and showed
|
that
|
<term>
human annotators
</term>
can reliably
|
#2486
We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. |
|
against the
<term>
annotated data
</term>
shows
|
that
|
, it successfully classifies 73.2 % in
|
#2511
An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%). |
|
</term>
and
<term>
decoding algorithm
</term>
|
that
|
enables us to evaluate and compare several
|
#2549
We propose a new phrase-based translation model and decoding algorithmthat enables us to evaluate and compare several, previously proposed phrase-based translation models. |