This simple example just illustrates the point that if a reader is combining noisy visual information with a language model , then confidence in previous regions will sometimes fall.

Unfortunately, however, the Mr. Chips model simplifies the problem of reading in a number of ways: First, it uses a unigram model as its language model , and thus fails to use any information in the linguistic context to help with word identification.

Specifically, our model identifies the words in a sentence by performing Bayesian inference combining noisy input from a realistic visual model with a language model that takes context into account.

Specifically, the model begins reading with a prior distribution over possible identities of a sentence given by its language model .

model’s prior distribution over the identity of the sentence given the language model is updated to a posterior distribution taking into account both the language model and the visual input obtained thus far.

Given the visual input and a language model, inferences about the identity of the sentence w can be made by standard Bayesian inference, where the prior is given by the language model and the likelihood is a function of the total visual input obtained from the first to the ith timestep Ii ,

5.1.2 Language model

Our reader’s language model was an unsmoothed bigram model created using a vocabulary set con-

Specifically, we constructed the model’s initial belief state (i.e., the distribution over sentences given by its language model ) by directly translating the bigram model into a wFSA in the log semiring.

6.1.3 Language model

In the event that a trigram or bigram would be found in the plaintext that was not counted in the language model , add one smoothing was used.

Our character-level language model used was developed from the first 1.5 million characters of the Wall Street Journal section of the Penn Tree-bank corpus.

If the text from which a language model is trained is of a different genre than the plaintext of a cipher, the unigraph letter frequencies may differ substantially from those of the language model , and so frequency counting will be misleading.

Such inefficiency indicates that integer programming may simply be the wrong tool for the job, possibly because language model probabilities computed from empirical data are not smoothly distributed enough over the space in which a cutting-plane method would attempt to compute a linear relaxation of this problem.

This difference in difficulty, while real, is not inherent, but rather an artefact of the character-level n-gram language models that they (and we) use, in which preponderant evidence of differences in short character sequences is necessary for the model to clearly favour one letter-substitution mapping over another.

Every possible full solution to a cipher C will produce a plaintext string with some associated language model probability, and we will consider the best possible solution to be the one that gives the highest probability.

For the sake of concreteness, we will assume here that the language model is a character-level trigram model.

Backpointers are necessary to reference one of the two language model probabilities.

Cells that would produce inconsistencies are left at zero, and these as well as cells that the language model assigns zero to can only produce zero entries in later columns.

The n p x n p cells of every column 2' do not depend on each other —only on the cells of the previous two columns 2' — 1 and i— 2, as well as the language model .

Removing the power of higher order language model and longer max phrase length, which are inherent in pseudo-words, shows that pseudo-words still improve translational performance significantly over unary words.

The pipeline uses GIZA++ model 4 (Brown et al., 1993; Och and Ney, 2003) for pseudo-word alignment, uses Moses (Koehn et al., 2007) as phrase-based decoder, uses the SRI Language Modeling Toolkit to train language model with modified Kneser-Ney smoothing (Kneser and Ney 1995; Chen and Goodman 1998).

A 5-gram language model is trained on English side of parallel corpus.

Xinhua portion of the English Gigaword3 corpus is used together with English side of large corpus to train a 4-gram language model .

Further experiments of removing the power of higher order language model and longer max phrase length, which are inherent in pseudo-words, show that pseudo-words still improve translational performance significantly over unary words.

(1) 3.1.1 Language Model

The language model (LM) pm?)

The parameters of the language model are learned from a monolingual Urdu corpus.

3.1 Word choice: language models

Language models (LM) are a way of computing how familiar a text is to readers using the distribution of words from a large background corpus.

We built unigram, bigram, and trigram language models with Good-Turing smoothing over the New York Times (NYT) section of the English Gigaword corpus (over 900 million words).

Coh-Metrix, which has been proposed as a comprehensive characterization of text, does not perform as well as the language model and the entity coherence classes, which contain considerably fewer features related to only one aspect of text.

It is apparent from the results that continuity, entity coherence, sentence fluency and language models are the most powerful classes of features that should be used in automation of evaluation and against which novel predictors of text quality should be compared.

For example, the language model features, which are the second best class for the system-level, do not fare as well at the input-level.

Our results show that summaries biased by dependency pattern models lead to significantly higher ROUGE scores than both n-gram language models reported in previous work and also Wikipedia baseline summaries.

They also experimented with representing such conceptual models using n- gram language models derived from corpora consisting of collections of descriptions of instances of specific object types (e.g.

a corpus of descriptions of churches, a corpus of bridge descriptions, and so on) and reported results showing that incorporating such n-gram language models as a feature in a feature-based extractive summarizer improves the quality of automatically generated summaries.

The main weakness of n-gram language models is that they only capture very local information aboutshofitennsequencesandcannotnnxkfllong distance dependencies between terms.

2.2 N-gram language models

Aker and Gaizauskas (2009) experimented with uni-gram and bi-gram language models to capture the features commonly used when describing an object type and used these to bias the sentence selection of the summarizer towards the sentences that contain these features.

As in Song and Croft (1999) they used their language models in a gener-

We thus propose to combine the advantages of both, and present a novel constituency-to-dependency translation model, which uses constituency forests on the source side to direct the translation, and dependency trees on the target side (as a language model ) to ensure grammaticality.

where the first two terms are translation and language model probabilities, 6(0) is the target string (English sentence) for derivation 0, the third and forth items are the dependency language model probabilities on the target side computed with words and POS tags separately, De (0) is the target dependency tree of 0, the fifth one is the parsing probability of the source side tree TC(0) 6 FC, the ill(0) is the penalty for the number of ill-formed dependency structures in 0, and the last two terms are derivation and translation length penalties, respectively.

For each node, we use the cube pruning technique (Chiang, 2007; Huang and Chiang, 2007) to produce partial hypotheses and compute all the feature scores including the dependency language model score (Section 4.1).

4.1 Dependency Language Model Computing

We also store the POS tag information for each word in dependency trees, and compute two different dependency language models for words and POS tags in dependency tree separately.

We use SRI Language Modeling Toolkit (Stolcke, 2002) to train a 4-gram language model with Kneser-Ney smoothing on the first 1/3 of the Xinhua portion of Giga-word corpus.

This suggests that using dependency language model really improves the translation quality by less than 1 BLEU point.

We add an English syntax language model £ to the cascade of transducers just described to better simulate an actual machine translation decoding task.

The language model is cast as an identity WTT and thus fits naturally into the experimental framework.

In our experiments we try several different language models to demonstrate varying performance of the application algorithms.

It would also be interesting to test the impact of another lexical language model , learned on non-SMS sentences.

The language model of the evaluation is a 3-gram.

(2008a), who showed on a French corpus comparable to ours that, if using a larger language model is always rewarded, the improvement quickly decreases with every higher level and is already quite small between 2-gram and 3-gram.

In our system, all lexicons, language models and sets of rules are compiled into finite-state machines (FSMs) and combined with the input text by composition (0).

Third, a combination of the lattice of solutions with a language model , and the choice of the best sequence of lexical units.

A language model is then applied on the word lattice, and the most probable word sequence is finally chosen by applying a best-path algorithm on the lattice.

All tokens Tj of S are concatenated together and composed with the lexical language model LM.

4.6 The language model

Our language model is an n-gram of lexical forms, smoothed by linear interpolation (Chen and Goodman, 1998), estimated on the normalized part of our training corpus and compiled into a weighted FST LMw.

This is particularly true when the sentence structure is defined in a language model that is psycholinguistically plausible (here, bounded-memory right-corner form).

This accords with an understated result of Boston et al.’s eye-tracking study (2008a): a richer language model predicts eye movements during reading better than an oversimplified one.

Frank (2009) similarly reports improvements in the reading-time predictiveness of unlexi-calized surprisal when using a language model that is more plausible than PCFGs.

Ideally, a psychologically-plausible language model would produce a surprisal that would correlate better with linguistic complexity.

Therefore, the specification of how to encode a syntactic language model is of utmost importance to the quality of the metric.

The purpose of this paper is to determine whether the language model defined by the HHMM parser can also predict reading times —it would be strange if a psychologically plausible model did not also produce Viable complexity metrics.

Both of these metrics fall out naturally from the time-series representation of the language model .

With the understanding of what operations need to occur, a formal definition of the language model is in order.

In this paper we analyze reading times in terms of a single predictive measure which integrates a model of semantic composition with an incremental parser and a language model .

While surprisal is a theoretically well-motivated measure, formalizing the idea of linguistic processing being highly predictive in terms of probabilistic language models , the measurement of semantic constraint in terms of vector similarities lacks a clear motivation.

This can be achieved by turning a vector model of semantic similarity into a probabilistic language model .

There are in fact a number of approaches to deriving language models from distributional models of semantics (e.g., Bellegarda 2000; Coccaro and Jurafsky 1998; Gildea and Hofmann 1999).

The basic idea is that the processing costs relating to the expectations of the language processor can be expressed in terms of the probabilities assigned by some form of language model to the input.

Surprisal could be also defined using a vanilla language model that does not take any structural or grammatical information into account (Frank 2009).

So it is a class-based bigram language model .

Deschacht and Moens (2009) use a latent-variable language model to improve semantic role labeling.

Word embeddings are typically induced using neural language models , which use neural networks as the underlying predictive model (Bengio, 2008).

Historically, training and testing of neural language models has been slow, scaling as the size of the vocabulary for each model computation (Bengio et al., 2001; Bengio et al., 2003).

Collobert and Weston (2008) presented a neural language model that could be trained over billions of words, because the gradient of the loss was computed stochastically over a small sample of possible outputs, in a spirit similar to Bengio and Sénecal (2003).

Neural language models (Bengio et al., 2001; Schwenk & Gauvain, 2002; Mnih & Hinton, 2007; Collobert & Weston, 2008), on the other hand, induce dense real-valued low-dimensional

(See Bengio (2008) for a more complete list of references on neural language models .)

These auxiliary tasks are sometimes specific to the supervised task, and sometimes general language modeling tasks like “predict the missing word”.

We leverage recently-developed techniques for learning representations of text using latent-variable language models , and extend these techniques to ones that provide the kinds of features that are useful for semantic role labeling.

Using latent-variable language models , we learn representations of texts that provide novel kinds of features to our supervised learning algorithms.

The next section provides background information on learning representations for NLP tasks using latent-variable language models .

2 Open-Domain Representations Using Latent-Variable Language Models

The problem is essentially one of generating multiple candidate sentences with the unattached function words ambiguously positioned (say in a lattice) and then use a second language model to rerank these sentences to select the target sentence.

Furthermore, in factored models, we can employ different language models for different factors.

We believe that the use of multiple language models (some much less sparse than the surface LM) in the factored baseline is the main reason for the improvement.

3.2.3 Experiments with higher-order language models

The main reason given for these problems was that the same statistical translation, reordering and language modeling mechanisms were being employed to both determine the morphological structure of the words and, at the same time, get the global order of the words correct.

First, a pre-def1ned confusion set is used to generate candidate corrections, then a scoring model, such as a trigram language model or na'1've Bayes classifier, is used to rank the candidates according to their context (e.g., Golding and Roth, 1996; Mangu and Brill, 1997; Church et al., 2007).

(2009) present a query speller system in which both the error model and the language model are trained using Web data.

Typically, a language model (source model) is used to capture contextual information, while an error model (channel model) is considered to be context free in that it does not take into account any contextual information in modeling word transformation probabilities.

where the error model P(QIC) models the transformation probability from C to Q, and the language model P(C) models how likely C is a correctly spelled query.

The language model (the second factor) is a backoff bigram model trained on the tokenized form of one year of query logs, using maximum likelihood estimation with absolute discounting smoothing.

Since we define the logarithm of the probabilities of the language model and the error model (i.e., the edit distance function) as features, the ranker can be viewed as a more general framework, subsuming the source channel model as a special case.

A language model is not used in this case, as the system is constrained to the given target sentence and thus the language model score has no effect on the alignment.

To deal with this problem, instead of simple phrase length restriction, we propose to apply the leaving-one-out method, which is also used for language modeling techniques (Kneser and Ney, 1995).

The baseline system is a standard phrase-based SMT system with eight features: phrase translation and word lexicon probabilities in both translation directions, phrase penalty, word penalty, language model score and a simple distance-based reordering model.

We used a 4-gram language model with modified Kneser-Ney discounting for all experiments.

The phrase model is combined with a language model , word lexicon models, word and phrase penalty, and many oth: ers.

They report improvements over a phrase-based model that uses an inverse phrase model and a language model .

Since all the member systems share the same data resources, such as language model and translation table, we only need to keep one copy of the required resources in memory.

Another method to speed up the system is to accelerate n-gram language model with n-gram caching techniques.

If the required n-gram hits the cache, the corresponding n-gram probability is returned by the cached copy rather than re-fetching the original data in language model .

In language modeling practice, one finds the likelihood P ( w | (9 of a word sequence w of length under a model (9, to be an inconvenient measure for comparison.

This makes it suitable for comparison of conversational genres, in much the same way as are general language models of words.

Accordingly, as for language models , density estimation in future turn-taking models may be im-

The current work attempts to address this problem by proposing a simple framework, which, at least conceptually, borrows quite heavily from the standard language modeling paradigm.

Specifically, we use an adaptive language model (Kneser et al., 1997) that modifies an

where P(wi E C |wi E D) is the probability of W, appearing in the caption given that it appears in the document D, and Padap(wi|wi_1,wi_2) the language model adapted with probabilities from our image annotation model:

The scaling parameter [3 for the adaptive language model was also tuned on the development set using a range of [05,09].

To produce baseline cognate identification predictions, we calculate the probability of each latent Hebrew letter sequence predicted by the HMM, and compare it to a uniform character-level Ugaritic language model (as done by our model, to avoid automatically assigning higher cognate probability to shorter Ugaritic words).

We also calculate P = 0) using a uniform uni-gram character-level language model (and thus depends only on the number of characters in ui).

Otherwise, a lone word it is generated, according a uniform character-level language model .

By combining such information with traditional statistical language models , it is capable of suggesting relevant articles that meet the dynamic nature of a discussion in social media.

The second one, LM, is based on statistical language models for relevant information retrieval (Ponte and Croft, 1998).

bilistic language model for each article, and ranks them on query likelihood, i.e.

We trained two language models : the first one is a 4-gram LM which is estimated on the target side of the texts used in the large data condition.

Both language models are used for both tasks.

Only the target-language half of the parallel training data are used to train the language model in this task.

Here, the first item is the language model (LM) probability where 7'(d) is the target string of derivation d; the second item is the translation length penalty; and the third item is the translation score, which is decomposed into a product of feature values of rules:

SRI Language Modeling Toolkit (Stolcke, 2002) was employed to train 5-gram English and Japanese LMs on the training set.

By introducing supertags into the target language side, i.e., the target language model and the target side of the phrase table, significant improvement was achieved for Arabic-to-English translation.

To some extent, these two features have similar function to a target language model or pos-based target language model .

(2009) study several confidence features based on mutual information between words and n-gram and backward n-gram language model for word-level and sentence-level CE.

We build a four-gram language model using the SRILM toolkit (Stolcke, 2002), which is trained