Linguistically Motivated Statistical Machine Translation: Models and Algorithms

Statistical machine translation
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We only add new alignment points that exist in the union of two word alignments. We also always require that a new alignment point connects at least one previously unaligned word. First, we expand to only directly adjacent alignment points. We check for potential points starting from the top right corner of the alignment matrix, checking for alignment points for the first English word, then continue with alignment points for the second English word, and so on. This is done iteratively until no alignment point can be added anymore. In a final step, we add non-adjacent alignment points, with otherwise the same requirements. We collect all aligned phrase pairs that are consistent with the word alignment: The words in a legal phrase pair are only aligned to each other, and not to words outside. See the figure below for some examples what this means. All alignment points for words that are part of the phrase pair have to be in the phrase alignment box. It is fine to have unaligned words in a phrase alignment, even at the boundary.

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The figure below displays all the phrase pairs that are collected according to this definition for the alignment from our running example. Maria, Mary , no, did not , slap, daba una bofetada , a la, the , bruja, witch , verde, green , Maria no, Mary did not , no daba una bofetada, did not slap , daba una bofetada a la, slap the , bruja verde, green witch , Maria no daba una bofetada, Mary did not slap , no daba una bofetada a la, did not slap the , a la bruja verde, the green witch Maria no daba una bofetada a la, Mary did not slap the , daba una bofetada a la bruja verde, slap the green witch , no daba una bofetada a la bruja verde, did not slap the green witch , Maria no daba una bofetada a la bruja verde, Mary did not slap the green witch.

No smoothing is performed, although lexical weighting addresses the problem of sparse data. See his presentation for details. Venugopal, Zhang, and Vogel Venugopal et al. ACL allows also for the collection of phrase pairs that are violated by the word alignment. They introduce a number of scoring methods take consistency with the word alignment, lexical translation probabilities, phrase length, etc. Zhang et al.

This enables them to estimate joint probability distributions, which can be marginalized into conditional probability distributions. Vogel et al. Decoder This section describes the Moses decoder from a more theoretical perspective. The decoder was originally developed for the phrase model proposed by Marcu and Wong. At that time, only a greedy hill-climbing decoder was available, which was unsufficent for our work on noun phrase translation Koehn, PhD, In fact, by reframing Och's alignment template model as a phrase translation model, the decoder is also suitable for his model, as well as other recently proposed phrase models.

We start this section with defining the concept of translation options, describe the basic mechanism of beam search, and its neccessary components: pruning, future cost estimates. We conclude with background on n-best list generation. Translation Options Given an input string of words, a number of phrase translations could be applied.

We call each such applicable phrase translation a translation option. This is illustrated in the figure below, where a number of phrase translations for the Spanish input sentence Maria no daba uma bofetada a la bruja verde are given. These translation options are collected before any decoding takes place. This allows a quicker lookup than consulting the whole phrase translation table during decoding. The translation options are stored with the information. Note that only the translation options that can be applied to a given input text are necessary for decoding.

Since the entire phrase translation table may be too big to fit into memory, we can restrict ourselves to these translation options to overcome such computational concerns. We may even generate a phrase translation table on demand that only includes valid translation options for a given input text. This way, a full phrase translation table that may be computationally too expensive to produce may never have to be built.

Core Algorithm The phrase-based decoder we developed employs a beam search algorithm, similar to the one used by Jelinek book "Statistical Methods for Speech Recognition", for speech recognition.

Statistical machine translation

The English output sentence is generated left to right in form of hypotheses. This process illustrated in the figure below. Starting from the initial hypothesis, the first expansion is the foreign word Maria , which is translated as Mary. The foreign word is marked as translated marked by an asterisk. We may also expand the initial hypothesis by translating the foreign word bruja as witch. We can generate new hypotheses from these expanded hypotheses.

Given the first expanded hypothesis we generate a new hypothesis by translating no with did not. Now the first two foreign words Maria and no are marked as being covered. Following the back pointers of the hypotheses we can read of the partial translations of the sentence. Let us now describe the beam search more formally.

We begin the search in an initial state where no foreign input words are translated and no English output words have been generated. New states are created by extending the English output with a phrasal translation of that covers some of the foreign input words not yet translated.

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The current cost of the new state is the cost of the original state multiplied with the translation, distortion and language model costs of the added phrasal translation. Note that we use the informal concept cost analogous to probability: A high cost is a low probability. Final states in the search are hypotheses that cover all foreign words. Among these the hypothesis with the lowest cost highest probability is selected as best translation.

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The algorithm described so far can be used for exhaustively searching through all possible translations. In the next sections we will describe how to optimize the search by discarding hypotheses that cannot be part of the path to the best translation. We then introduce the concept of comparable states that allow us to define a beam of good hypotheses and prune out hypotheses that fall out of this beam. In a later section, we will describe how to generate an approximate n-best list. Recombining Hypotheses Recombining hypothesis is a risk-free way to reduce the search space.

Two hypotheses can be recombined if they agree in. If there are two paths that lead to two hypotheses that agree in these properties, we keep only the cheaper hypothesis, e. The other hypothesis cannot be part of the path to the best translation, and we can safely discard it. Note that the inferior hypothesis can be part of the path to the second best translation. This is important for generating n-best lists.

Beam Search While the recombination of hypotheses as described above reduces the size of the search space, this is not enough for all but the shortest sentences. Let us estimate how many hypotheses or, states are generated during an exhaustive search. In practice, the number of possible English words for the last two words generated is much smaller than V e 2.

The main concern is the exponential explosion from the 2 n f possible configurations of foreign words covered by a hypothesis. I do recognize that all of these areas of study have value to different people.

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An introduction to each field is important to give people an opportunity to find their personal passion. There was no translation. I used to explain my passion for programming in those terms, that unlike people, code did exactly what you told it to do. But today under every line of code there are many tiers of transpiling, compiling, and execution of deeply nested referenced code, from people with different skills and levels of interest in the quality and ongoing development of their code. I hope that the future of coding includes more standards, not limiting or restricting development, but holding all code modules to some level of compliance with all others.

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This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance. Editorial Reviews. Review. “Linguistically Motivated Statistical Machine Translation, written by Linguistically Motivated Statistical Machine Translation: Models and Algorithms - Kindle edition by Deyi Xiong, Min Zhang. Download it once and.

Could you imagine this in a StarTrek universe? Captain : Engineering! I need warp speed Now! We just updated the software that manages the dilithium chamber temperature to v4. It has dependencies on HeatMon v0. Captain : Just give me the bottom line! CE: Well sir, we need to update the communications software because the nearest starbase just updated theirs and there were breaking changes.

Then we need to download an unmerged pull request for HeatMon that patches v0. If that works we may be able to get the temperature under control. Captain : My God man, what does that all mean? Romulans will be attacking in 20 seconds. The End. There were more women in programming decades ago, because other fields were more closed.

When medicine and law opened up to women, they women left programming for fields they were more interested in and more lucrative, prestigious, etc. Doctors make a lot more than programmers on average for both fields. Why would women stick with tech when medicine has more to offer?

Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content. What follows is a transcript of their conversation, lightly edited for length and clarity. Clive: What!