... Vogel.
2006. Distributed Language Modeling for N-best List
Re-ranking. In Proc. of EMNLP 2006, pages 216-223.
Bing Zhao, Matthias Eck and Stephan Vogel. 2004.
Language ModelAdaptationfor Statistical ... translation mod-
el adaptation and languagemodel adaptation. Here
we focus on how to adapt a translation model, which
is trained from the large-scale out-of-domain bilin-
gual corpus, for domain-specific ... enforces one-to-one topic corre-
spondence and enables latent topic distributions to
be efficiently transferred across languages, to cross-
lingual language modeling and translation lexicon
adaptation. ...
... USA. Association for Computational Linguistics.
Bing Zhao, Matthias Eck, and Stephan Vogel. 2004.
Language modeladaptationfor statistical machine
translation with structured query models. In Proceed-
ings ... Association for Computational Linguistics:shortpapers, pages 445–449,
Portland, Oregon, June 19-24, 2011.
c
2011 Association for Computational Linguistics
On-line LanguageModel Biasing for Statistical ... translation: parameter
estimation. Computational Linguistics, 19:263–311.
Woosung Kim. 2005. LanguageModelAdaptation for
Automatic Speech Recognition and Statistical Machine
Translation. Ph.D. thesis,...
... was 0.001
for almost all training runs. The language model
weight µ of the reduced model was about 60%
smaller than the respective value for the full model,
which confirms that the full model provides ... level of < 0.1% for both tests. The improve-
ment of the full model compared to the reduced
model is weakly significant on a level of 2.6% for
the MAPSSWE test.
For both models, the optimal ... |
(1)
The languagemodel weight λ and the word inser-
tion penalty ip lead to a better performance in prac-
tice, but they have no theoretical justification. Our
grammar-based languagemodel is...
... 115–119,
Jeju, Republic of Korea, 8-14 July 2012.
c
2012 Association for Computational Linguistics
Topic Models for Dynamic Translation Model Adaptation
Vladimir Eidelman
Computer Science
and UMIACS
University ... for the source it
came from, many word pairs will be unobserved for
a given table. This sparsity requires smoothing. Sec-
ond, we may not know the (sub)corpora our training
1
Language modeladaptation ... topic-specific contexts, where
topics are induced in an unsupervised way
using topic models; this can be thought of
as inducing subcorpora foradaptation with-
out any human annotation. We use these...
... Syntax-
based language models for statistical machine transla-
tion. MT Summit IX., Intl. Assoc. for Machine Trans-
lation.
C. Chelba and F. Jelinek. 1998. Exploiting syntactic
structure forlanguage modeling. ... Dis-
tributed language modeling for N-best list re-ranking.
The 2006 Conference on Empirical Methods in Natu-
ral Language Processing (EMNLP), 216-223.
Y. Zhang, 2008. Structured language models for statisti-
cal ... n-gram/m-
SLM/PLSA language model.
The composite n-gram/m-SLM/PLSA lan-
guage model can be formulated as a directed
MRF model (Wang et al., 2006) with lo-
cal normalization constraints for the param-
eters...
... (CCN)
Adaptation
Corpus
Lexicon Adaptation
for Improved
Character Accurac
y
Add/Delete words
Lexicon
(
Lex
i
)
Language
Model (
LM
i
)
y
(LAICA)
Word
Segmentation
LM
Trainin
g
(
Lex
i
)
Model
... can be
amended by involving the discriminative language
modeladaptation in the iteration, which results in
a unified languagemodel and lexicon adaptation
framework. This can be our future work. ... beginning we are given an adaptation spoken
corpus and manual transcriptions. Based on a base-
line lexicon (Lex
0
) and a languagemodel (LM
0
)
we perform ASR on the adaptation corpus and con-
struct...
... June 2005.
c
2005 Association for Computational Linguistics
A Phonotactic LanguageModelfor Spoken Language Identification
Haizhou Li and Bin Ma
Institute for Infocomm Research
Singapore ... Chen of Institute for Info-
comm Research for insightful discussions.
References
Jerome R. Bellegarda. 2000. Exploiting latent semantic
information in statistical language modeling
, In Proc. ... in the formalism mentioned
above: tokenization, statistical language modeling,
and language identification. A typical LID system
is illustrated in Figure 1 (Zissman, 1996), where
language...
... Markov language model, and a simple
set of unification grammar rules for the Chinese
language, although the present model is in fact
language independent. The system is written in C
language ... signal
preprocessor is included to form a complete speech
recognition system. The language processor consists
of a languagemodel and a parser. The languagemodel
properly integrates the unification ... summarized.
The Laneua~e Model
The goal of the languagemodel is to participate
in the selection of candidate constituents for a
sentence to be identified. The proposed language
model is composed...
... use
21
6.1 The language model: probabilities and
smoothing
For our language model, we need a list of French
lemmas with their frequencies of occurrence. Get-
ting robust estimates for a large number ... a
statistical languagemodel and a measure of tense
difficulty.
4.1 The language model
The lexical difficulty of a text is quite an elaborate
phenomenon to parameterise. The logistic regres-
sion models ... also ensures that
the language resembles present-day spoken
French.
• The target population for our formula is
young people and adults. Therefore, only
textbooks intended for this public were...
... 2006. Unsupervised language
modeladaptation using latent semantic marginals. In
Proc. of Interspeech.
Y. C. Tam and T.Schultz. 2007. Correlated latent seman-
tic modelforunsupervisedlanguagemodel ... propose a bilingual
LSA model (bLSA) for crosslingual LM adaptation
that can be applied before translation. The bLSA
model consists of two LSA models: one for each
side of the language trained on ... marginal
adaptation (Kneser et al., 1997)
In this paper, we propose a framework to per-
form LM adaptation across languages, enabling the
adaptation of a LM from one language based on the
adaptation...