Index of papers in Proc. ACL that mention
  • MT system
Toutanova, Kristina and Suzuki, Hisami and Ruopp, Achim
Abstract
We investigate different ways of combining the inflection prediction component with the SMT system by training the base MT system on fully inflected forms or on word stems.
Inflection prediction models
The dependency structure on the Russian side, indicated by solid arcs, is given by a treelet MT system (see Section 4.1), projected from the word dependency struc-
Integration of inflection models with MT systems
The methods differ in the extent to which the factoring of the problem into two subprob-lems — predicting stems and predicting inflections — is reflected in the base MT systems .
Integration of inflection models with MT systems
In the first method, the MT system is trained to produce fully inflected target words and the inflection model can change the inflections.
Introduction
(Finkel et al., 2006), and in some cases, to factor the translation problem so that the baseline MT system can take advantage of the reduction in sparsity by being able to work on word stems.
Machine translation systems and data
This is a syntactically-informed MT system , designed following (Quirk et al., 2005).
Machine translation systems and data
For each language pair, we used a set of parallel sentences (train) for training the MT system sub-models (e.g., phrase tables, language model), a set of parallel sentences (lambda) for training the combination weights with max-BLEU training, a set of parallel sentences (dev) for training a small number of combination parameters for our integration methods (see Section 5), and a set of parallel sentences (test) for final evaluation.
Machine translation systems and data
All MT systems for a given language pair used the same datasets.
Related work
In recent work, Koehn and Hoang (2007) proposed a general framework for including morphological features in a phrase-based SMT system by factoring the representation of words into a vector of morphological features and allowing a phrase-based MT system to work on any of the factored representations, which is implemented in the Moses system.
Related work
This also makes the model portable and applicable to different types of MT systems .
Related work
In contrast, we focus on methods of integration of an inflection prediction model with an MT system , and on evaluation of the model’s impact on translation.
MT system is mentioned in 41 sentences in this paper.
Topics mentioned in this paper:
Haffari, Gholamreza and Sarkar, Anoop
AL-SMT: Multilingual Setting
Our goal is to add a new language to this corpus, and at the same time to construct high quality MT systems from the existing languages (in the multilingual corpus) to the new language.
Abstract
We introduce an active learning task of adding a new language to an existing multilingual set of parallel text and constructing high quality MT systems , from each language in the collection into this new target language.
Introduction
In this paper, we consider how to use active learning (AL) in order to add a new language to such a multilingual parallel corpus and at the same time we construct an MT system from each language in the original corpus into this new target language.
Introduction
In this paper, we explore how multiple MT systems can be used to effectively pick instances that are more likely to improve training quality.
Introduction
When we build multiple MT systems from multiple source languages to the new target language, each MT system can be seen as a different ‘view’ on the desired output translation.
MT system is mentioned in 18 sentences in this paper.
Topics mentioned in this paper:
Salloum, Wael and Elfardy, Heba and Alamir-Salloum, Linda and Habash, Nizar and Diab, Mona
Abstract
Our best result improves over the best single MT system baseline by 1.0% BLEU and over a strong system selection baseline by 0.6% BLEU on a blind test set.
Introduction
In this paper we study the use of sentence-level dialect identification together with various linguistic features in optimizing the selection of outputs of four different MT systems on input text that includes a mix of dialects.
Introduction
Our best system selection approach improves over our best baseline single MT system by 1.0% absolute BLEU point on a blind test set.
Related Work
Sawaf (2010) and Salloum and Habash (2013) used hybrid solutions that combine rule-based algorithms and resources such as leXicons and morphological analyzers with statistical models to map DA to MSA before using MSA-to-English MT systems .
Related Work
In this paper we use four MT systems that translate from DA to English in different ways.
Related Work
Our fourth MT system uses ELISSA, the DA-to-MSA MT tool by Salloum and Habash (2013), to produce an MSA pivot.
MT system is mentioned in 41 sentences in this paper.
Topics mentioned in this paper:
Huang, Fei and Xu, Jian-Ming and Ittycheriah, Abraham and Roukos, Salim
Adaptive MT Quality Estimation
The source side of the QE training data Sq is combined with the input document Sd for MT system training data subsampling.
Adaptive MT Quality Estimation
Once the document-specific MT system is trained, we use it to translate both the input document and the source QE training data, obtaining the translation Td and
Adaptive MT Quality Estimation
As the QE model is adaptively retrained for each document-specific MT system , its prediction is more accurate and consistent.
Document-specific MT System
Building a general MT system using all the parallel data not only produces a huge translation model (unless with very aggressive pruning), the performance on the given input document is suboptimal due to the unwanted dominance of out-of-domain data.
Document-specific MT System
The document-specific system is built based on sub-sampling: from the parallel corpora we select sentence pairs that are the most similar to the sentences from the input document, then build the MT system with the sub-sampled sentence pairs.
Introduction
Depending on the difficulty of the input sentences (sentence length, OOV words, complex sentence structures and the coverage of the MT system’s training data), some translation outputs can be perfect, while others are ungrammatical, missing important words or even totally garbled.
Introduction
This shortcoming is one of the main obstacles for the adoption of MT systems , especially in machine assisted human translation: MT post-editing, where human translators have an option to edit MT proposals or translate from scratch.
Introduction
In section 3 we will introduce the document-specific MT system built for post-editing.
Static MT Quality Estimation
However for the post-editing task, we argue that it could also be cast as a classification problem: MT system
Static MT Quality Estimation
We build a document-specific MT system to translate this document, then ask human translator to correct the translation output.
MT system is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Popat, Kashyap and A.R, Balamurali and Bhattacharyya, Pushpak and Haffari, Gholamreza
Clustering for Cross Lingual Sentiment Analysis
If a language is truly resource scarce, it is mostly unlikely to have an MT system .
Clustering for Cross Lingual Sentiment Analysis
Given that sentiment analysis is a less resource intensive task compared to machine translation, the use of an MT system is hard to justify for performing
Conclusion and Future Work
For CLSA, clusters linked together using unlabelled parallel corpora do away with the need of translating labelled corpora from one language to another using an intermediary MT system or bilingual dictionary.
Conclusion and Future Work
Further, this approach was found to be useful in cases where there are no MT systems to perform CLSA and the language of analysis is truly resource scarce.
Conclusion and Future Work
Thus, wider implication of this study is that many widely spoken yet resource scare languages like Pashto, Sundanese, Hausa, Gujarati and Punjabi which do not have an MT system could now be analysed for sentiment.
Discussions
A note on CLSA for truly resource scarce languages: Note that there is no publicly available MT system for English to Marathi.
Introduction
However, many languages which are truly resource scarce, do not have an MT system or existing MT systems are not ripe to be used for CLSA (Balamurali et al., 2013).
Introduction
No MT systems or bilingual dictionaries are used for this study.
Related Work
Given the subtle and different ways the sentiment can be expressed which itself manifested as a result of cultural diversity amongst different languages, an MT system has to be of a superior quality to capture them.
MT system is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Kumar, Shankar and Macherey, Wolfgang and Dyer, Chris and Och, Franz
Abstract
We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Context Free Grammars.
Discussion
On hypergraphs produced by Hierarchical and Syntax Augmented MT systems , our MBR algorithm gives a 7X speedup relative to 1000-best MBR while giving comparable or even better performance.
Discussion
We believe that our efficient algorithms will make them more widely applicable in both SCFG—based and phrase-based MT systems .
Experiments
6.2 MT System Description
Experiments
Our phrase-based statistical MT system is similar to the alignment template system described in (Och and Ney, 2004; Tromble et al., 2008).
Experiments
We also train two SCFG—based MT systems : a hierarchical phrase-based SMT (Chiang, 2007) system and a syntax augmented machine translation (SAMT) system using the approach described in Zollmann and Venugopal (2006).
Introduction
In this paper, we extend MERT and MBR decoding to work on hypergraphs produced by SCFG—based MT systems .
Minimum Bayes-Risk Decoding
MBR decoding for translation can be performed by reranking an N -best list of hypotheses generated by an MT system (Kumar and Byme, 2004).
Minimum Bayes-Risk Decoding
We next extend the Lattice MBR decoding algorithm (Algorithm 3) to rescore hypergraphs produced by a SCFG based MT system .
MT system is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Parton, Kristen and McKeown, Kathleen R. and Coyne, Bob and Diab, Mona T. and Grishman, Ralph and Hakkani-Tür, Dilek and Harper, Mary and Ji, Heng and Ma, Wei Yun and Meyers, Adam and Stolbach, Sara and Sun, Ang and Tur, Gokhan and Xu, Wei and Yaman, Sibel
Methods 5.1 5W Systems
All three annotators were native English speakers who were not system developers for any of the SW systems that were being evaluated (to avoid biased grading, or assigning more blame to the MT system ).
Methods 5.1 5W Systems
If the SW system picked an incorrectly translated argument (e. g., “baked a moon” instead of “baked a cake”), then the error would be assigned to the MT system .
Results
Long-distance phrase movement is a common problem in Chinese-English MT, and many MT systems try to handle it (e. g., Wang et al.
Results
Since MT systems are tuned for word-based overlap measures (such as BLEU), verb deletion is penalized equally as, for example, determiner deletion.
SW System
In this section, we describe the individual systems that we evaluated, the combination strategy, the parsers that we tuned for the task, and the MT systems .
SW System
Finally, Chinese-align used the alignments of three separate MT systems to translate the 5Ws: a phrase-based system, a hierarchical phrase-based system, and a syntax augmented hierarchical phrase-based system.
SW System
Since the predicate is essential, it tried to detect when verbs were deleted in MT, and back-off to a different MT system .
MT system is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Cohn, Trevor and Specia, Lucia
Gaussian Process Regression
In our quality estimation experiments we consider as metadata the MT system which produced the translation, and the identity of the source sentence being translated.
Introduction
In this paper we model the task of predicting the quality of sentence translations using datasets that have been annotated by several judges with different levels of expertise and reliability, containing translations from a variety of MT systems and on a range of different types of sentences.
Multitask Quality Estimation 4.1 Experimental Setup
Partitioning the data by annotator (,uA) gives the best baseline result, while there is less information from the MT system or sentence identity.
Multitask Quality Estimation 4.1 Experimental Setup
The multitask learning methods performed best when using the annotator identity as the task descriptor, and less well for the MT system and sentence pair, where they only slightly improved over the baseline.
Quality Estimation
Examples of applications of QE include improving post-editing efficiency by filtering out low quality segments which would require more effort and time to correct than translating from scratch (Specia et al., 2009), selecting high quality segments to be published as they are, without post-editing (Soricut and Echihabi, 2010), selecting a translation from either an MT system or a translation memory for post-editing (He et al., 2010), selecting the best translation from multiple MT systems (Specia et al., 2010), and highlighting subsegments that need revision (Bach et al., 2011).
Quality Estimation
o It is often desirable to include alternative translations of source sentences produced by multiple MT systems , which requires multiple annotators for unbiased judgements, particularly for labels such as post-editing time (a translation seen a second time will require less editing effort).
Quality Estimation
It contains 299 English sentences translated into Spanish using two or more of eight MT systems randomly selected from all system submissions for WMT11 (Callison-Burch et al., 2011).
MT system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Xiang, Bing and Luo, Xiaoqiang and Zhou, Bowen
Abstract
We show that the recovered empty categories not only improve the word alignment quality, but also lead to significant improvements in a large-scale state-of-the-art syntactic MT system .
Experimental Results
In the Chinese-to-English MT experiments, we test two state-of-the-art MT systems .
Experimental Results
The MT systems are optimized with pairwise ranking optimization (Hopkins and May, 2011) to maximize BLEU (Papineni et al., 2002).
Integrating Empty Categories in Machine Translation
We conducted some initial error analysis on our MT system output and found that most of the errors that are related to ECs are due to the missing *pro* and *PRO*.
Integrating Empty Categories in Machine Translation
For example, for a hierarchical MT system , some phrase pairs and Hiero (Chiang, 2005) rules can be extracted with recovered *pro* and *PRO* at the Chinese side.
Integrating Empty Categories in Machine Translation
In this work we also take advantages of the augmented Chinese parse trees (with ECs projected to the surface) and extract tree-to-string grammar (Liu et al., 2006) for a tree-to-string MT system .
Related Work
We directly take advantage of the augmented parse trees in the tree-to-string grammar, which could have larger impact on the MT system performance.
MT system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Li, Haibo and Zheng, Jing and Ji, Heng and Li, Qi and Wang, Wen
Baseline MT
As our baseline, we apply a high-performing Chinese-English MT system (Zheng, 2008; Zheng et al., 2009) based on hierarchical phrase-based translation framework (Chiang, 2005).
Experiments
For example, the baseline MT system mistakenly translated a person name “3% 21%?
Experiments
For example, the following sentence: “§K%%Efifi§i$fi%§§ infill, EEWHWZ’Q (Gao Meimei’s strength really is formidable, I really admire her)” was mistakenly translated into “Gao the strength of the America and the America also really strong , ah , really admire her” by the baseline MT system because the person name “33%;: (Gaomeimei)” was mistakenly segmented into three words “$3 (Gao)”, “% (the America)” and “% (the America)”.
Experiments
Furthermore, we calculated three Pearson product-moment correlation coefficients between human judgment scores and name-aware BLEU scores of these two MT systems .
Introduction
A typical statistical MT system can only translate 60% person names correctly (Ji et al., 2009).
Related Work
Two types of humble strategies were previously attempted to build name translation components which operate in tandem and loosely integrate into conventional statistical MT systems:
Related Work
Preprocessing: identify names in the source texts and propose name translations to the MT system ; the name translation results can be simply but aggressively transferred from the source to the target side using word alignment, or added into phrase table in order to
Related Work
Some statistical MT systems (e.g.
MT system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Andreas, Jacob and Vlachos, Andreas and Clark, Stephen
Discussion
Our results validate the hypothesis that it is possible to adapt an ordinary MT system into a working semantic parser.
Introduction
Indeed, successful semantic parsers often resemble MT systems in several important respects, including the use of word alignment models as a starting point for rule extraction (Wong and Mooney, 2006; Kwiatkowski et al., 2010) and the use of automata such as tree transducers (Jones et al., 2012) to encode the relationship between NL and MRL.
MT—based semantic parsing
Language modeling In addition to translation rules learned from a parallel corpus, MT systems also rely on an n-gram language model for the target language, estimated from a (typically larger) monolingual corpus.
Related Work
UBL, like an MT system (and unlike most of the other systems discussed in this section), extracts rules at multiple levels of granularity by means of this splitting and unification procedure.
Related Work
multilevel rules composed from smaller rules, a process similar to the one used for creating phrase tables in a phrase-based MT system .
Results
In the results shown in Table 1 we observe that on English GeoQuery data, the hierarchical translation model achieves scores competitive with the state of the art, and in every language one of the MT systems achieves accuracy at least as good as a purpose-built semantic parser.
Results
While differences in implementation and factors like programming language choice make a direct comparison of times necessarily imprecise, we note that the MT system takes less than three minutes to train on the GeoQuery corpus, while the publicly-available implementations of tsVB and UBL require roughly twenty minutes and five hours respectively on a 2.1 GHz CPU.
MT system is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Liu, Shujie and Li, Chi-Ho and Li, Mu and Zhou, Ming
Features and Training
Before elaborating the details of how the actual graph is constructed, we would like to first introduce how the graph-based translation consensus can be used in an MT system .
Features and Training
When graph-based consensus is applied to an MT system , the graph will have nodes for training data, development (dev) data, and test data (details in Section 5).
Graph-based Translation Consensus
Our MT system with graph-based translation consensus adopts the conventional log-linear model.
Introduction
The principle of consensus can be sketched as “a translation candidate is deemed more plausible if it is supported by other translation candidates.” The actual formulation of the principle depends on whether the translation candidate is a complete sentence or just a span of it, whether the candidate is the same as or similar to the supporting candidates, and whether the supporting candidates come from the same or different MT system .
Introduction
Others extend consensus among translations from the same MT system to those from different MT systems .
Introduction
For the source (Chinese) span “fig 73 H T 57 3x91 the MT system produced the correct translation for the second sentence, but it failed to do so for the first one.
MT system is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Braslavski, Pavel and Beloborodov, Alexander and Khalilov, Maxim and Sharoff, Serge
Conclusions and future plans
This was the first attempt at making proper quantitative and qualitative evaluation of the English—>Russian MT systems .
Conclusions and future plans
We have made the corpus comprising the source sentences, their human translations, translations by participating MT systems and the human evaluation data publicly available.8
Corpus preparation
We chose to retain the entire texts in the corpus rather than individual sentences, since some MT systems may use information beyond isolated sentences.
Evaluation methodology
In our case the assessors were asked to make a pairwise comparison of two sentences translated by two different MT systems against a gold standard translation.
Introduction
One of the main challenges in developing MT systems for Russian and for evaluating them is the need to deal with its free word order and complex morphology.
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
He, Wei and Wu, Hua and Wang, Haifeng and Liu, Ting
Experiments
A Chinese-English and an English-Chinese MT system are trained on (C0, E0).
Forward-Translation vs. Back-Translation
The only requirement is that the MT system needs to be bidirectional.
Forward-Translation vs. Back-Translation
The procedure includes translating a text into certain foreign language with the MT system (Forward-Translation), and translating it back into the original language with the same system (Back-Translation).
Forward-Translation vs. Back-Translation
Two possible reasons may explain this phenomenon: (l) in the first round of translation T 0 9 S1, some target word orders are reserved due to the reordering failure, and these reserved orders lead to a better result in the second round of translation; (2) the text generated by an MT system is more likely to be matched by the reversed but homologous MT system .
Related Work
(2010) captures the structures implicitly by training an MT system on (SO, $1) and “translates” the SMT input to an MT-favored expression.
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Chen, Boxing and Kuhn, Roland and Larkin, Samuel
Abstract
This paper presents PORTl, a new MT evaluation metric which combines precision, recall and an ordering metric and which is primarily designed for tuning MT systems .
Abstract
We compare PORT-tuned MT systems to BLEU-tuned baselines in five experimental conditions involving four language pairs.
Conclusions
Most important, our results show that PORT-tuned MT systems yield better translations than BLEU-tuned systems on several language pairs, according both to automatic metrics and human evaluations.
Introduction
First, there is no evidence that any other tuning metric yields better MT systems .
Introduction
In this work, our goal is to devise a metric that, like BLEU, is computationally cheap and language-independent, but that yields better MT systems than BLEU when used for tuning.
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Ravi, Sujith and Knight, Kevin
Machine Translation as a Decipherment Task
MT Systems: We build and compare different MT systems under two training scenarios:
Machine Translation as a Decipherment Task
Evaluation: All the MT systems are run on the Spanish test data and the quality of the resulting English translations are evaluated using two different measures—(1) Normalized edit distance score (Navarro, 2001),6 and (2) BLEU (Papineni et
Machine Translation as a Decipherment Task
Results: Figure 3 compares the results of various MT systems (using parallel versus decipherment training) on the two test corpora in terms of edit distance scores (a lower score indicates closer match to the gold translation).
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Clifton, Ann and Sarkar, Anoop
Models 2.1 Baseline Models
Table 1 shows how morphemes are being used in the MT system .
Models 2.1 Baseline Models
The first phase of our morphology prediction model is to train a MT system that produces morphologically simplified word forms in the target language.
Models 2.1 Baseline Models
MT System Alignment:
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Li, Zhifei and Eisner, Jason and Khudanpur, Sanjeev
Background 2.1 Terminology
It can be used to encode exponentially many hypotheses generated by a phrase-based MT system (e.g., Koehn et al.
Background 2.1 Terminology
(2003)) or a syntax-based MT system (e.g., Chiang (2007)).
Background 2.1 Terminology
To approximate the intractable decoding problem of (2), most MT systems (Koehn et al., 2003; Chiang, 2007) use a simple Viterbi approximation,
Introduction
They recover additional latent variables—so-called nuisance variables—that are not of interest to the user.1 For example, though machine translation (MT) seeks to output a string, typical MT systems (Koehn et al., 2003; Chiang, 2007)
Variational Approximate Decoding
For each input sentence c, we assume that a baseline MT system generates a hypergraph HG(cc) that compactly encodes the derivation set D(cc) along with a score for each d E D(9c),5 which we interpret as p(y, d | c) (or proportional to it).
MT system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Devlin, Jacob and Zbib, Rabih and Huang, Zhongqiang and Lamar, Thomas and Schwartz, Richard and Makhoul, John
Abstract
Recent work has shown success in using neural network language models (NNLMs) as features in MT systems .
Model Variations
> 5 MT System
Model Variations
In this section, we describe the MT system used in our experiments.
Model Variations
Because of this, the baseline BLEU scores are much higher than a typical MT system — especially a real-time, production engine which must support many language pairs.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Experiments
MT System We develop a machine translation baseline as follows.
Experiments
MT System ADD single
Experiments
As expected, the MT system slightly outperforms our models on most language pairs.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Green, Spence and DeNero, John
A Class-based Model of Agreement
However, we conduct CRF inference in tandem with the translation decoding procedure (§3), creating an environment in which subsequent words of the observation are not available; the MT system has yet to generate the rest of the translation when the tagging features for a position are scored.
Introduction
The MT system selects the correct verb stem, but with masculine inflection.
Introduction
Agreement relations that cross statistical phrase boundaries are not explicitly modeled in most phrase-based MT systems (Avramidis and Koehn, 2008).
Related Work
To our knowledge, Uszkoreit and Brants (2008) are the only recent authors to show an improvement in a state-of-the-art MT system using class-based LMs.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Razmara, Majid and Foster, George and Sankaran, Baskaran and Sarkar, Anoop
Conclusion & Future Work
In this approach a number of MT systems are combined at decoding time in order to form an ensemble model.
Conclusion & Future Work
We will also add capability of supporting syntax-based ensemble decoding and experiment how a phrase-based system can benefit from syntax information present in a syntax-aware MT system .
Experiments & Results 4.1 Experimental Setup
The first group are the baseline results on the phrase-based system discussed in Section 2 and the second group are those of our hierarchical MT system .
Experiments & Results 4.1 Experimental Setup
Since the Hiero baselines results were substantially better than those of the phrase-based model, we also implemented the best-performing baseline, linear mixture, in our Hiero-style MT system and in fact it achieves the hights BLEU score among all the baselines as shown in Table 2.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Duh, Kevin and Sudoh, Katsuhito and Wu, Xianchao and Tsukada, Hajime and Nagata, Masaaki
Introduction
Discrimi-native optimization methods such as MERT (Och, 2003), MIRA (Crammer et al., 2006), PRO (Hopkins and May, 2011), and Downhill-Simplex (Nelder and Mead, 1965) have been influential in improving MT systems in recent years.
Introduction
We want to build a MT system that does well with respect to many aspects of translation quality.
Opportunities and Limitations
We introduce a new approach (PMO) for training MT systems on multiple metrics.
Theory of Pareto Optimality 2.1 Definitions and Concepts
Here, the MT system’s Decode function, parameterized by weight vector w, takes in a foreign sentence f and returns a translated hypothesis h. The argmax operates in vector space and our goal is to find to leading to hypotheses on the Pareto Frontier.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Lo, Chi-kiu and Wu, Dekai
Abstract
As MT systems improve, the shortcomings of the n-gram based evaluation metrics are becoming more apparent.
Abstract
State-of-the-art MT systems are often able to output fluent translations that are nearly grammatical and contain roughly the correct words, but still fail to eXpress meaning that is close to the input.
Abstract
, 2006) is more adequacy-oriented, it is only employed in very large scale MT system evaluation instead of day-to-day research activities.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Pado, Sebastian and Galley, Michel and Jurafsky, Dan and Manning, Christopher D.
Conclusion and Outlook
2) and may find use in uncovering systematic shortcomings of MT systems .
Conclusion and Outlook
To some extent, of course, this problem holds as well for state-of—the-art MT systems .
EXpt. 1: Predicting Absolute Scores
Each language consists of 1500—2800 sentence pairs produced by 7—15 MT systems .
Introduction
Figure l: Entailment status between an MT system hypothesis and a reference translation for equivalent (top) and nonequivalent (bottom) translations.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Ganchev, Kuzman and Graça, João V. and Taskar, Ben
Abstract
We propose and extensively evaluate a simple method for using alignment models to produce alignments better-suited for phrase-based MT systems , and show significant gains (as measured by BLEU score) in end-to-end translation systems for six languages pairs used in recent MT competitions.
Adding agreement constraints
Most MT systems train an alignment model in each direction and then heuristically combine their predictions.
Conclusions
The nature of the complicated relationship between word alignments, the corresponding extracted phrases and the effects on the final MT system still begs for better explanations and metrics.
Introduction
used, we can get not only improvements in alignment performance, but also in the performance of the MT system that uses those alignments.
MT system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Xiao, Tong and Zhu, Jingbo and Zhang, Chunliang
Evaluation
We see, first of all, that the MT system benefits from our approach in most cases.
Evaluation
Apart from showing the effects of the skeleton-based model, we also studied the behavior of the MT system under the different settings of search space.
Related Work
More importantly, we develop a complete approach to this issue and show its effectiveness in a state-of-the-art MT system .
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
van Gompel, Maarten and van den Bosch, Antal
Discussion and conclusion
An application of our idea outside the area of translation assistance is post-correction of the output of some MT systems that, as a last-resort heuristic, copy source words or phrases into their output, producing precisely the kind of input our system is trained on.
Discussion and conclusion
Our classification-based approach may be able to resolve some of these cases operating as an add-on to a regular MT system —or as a independent post-correction system.
Evaluation
These scores should generally be much better than the typical MT system performances as only local changes are made to otherwise “perfect” L2 sentences.
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Green, Spence and Wang, Sida and Cer, Daniel and Manning, Christopher D.
Adaptive Online Algorithms
SGD is sensitive to the learning rate 77, which is difiicult to set in an MT system that mixes frequent “dense” features (like the language model) with sparse features (e. g., for translation rules).
Experiments
We built Arabic-English and Chinese-English MT systems with Phrasal (Cer et al., 2010), a phrase-based system based on alignment templates (Och and Ney, 2004).
Introduction
We introduce a new method for training feature-rich MT systems that is effective yet comparatively easy to implement.
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Liu, Chang and Ng, Hwee Tou
Experiments
The test set was translated by seven MT systems , and each translation has been manually judged for adequacy and fluency.
Experiments
In addition, the translation outputs of the MT systems are also manually ranked according to their translation quality.
Experiments
The NIST 2008 English-Chinese MT task consists of 127 documents with 1,830 segments, each with four reference translations and eleven automatic MT system translations.
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Riesa, Jason and Marcu, Daniel
Conclusion
We treat word alignment as a parsing problem, and by taking advantage of English syntax and the hypergraph structure of our search algorithm, we report significant increases in both F-measure and BLEU score over standard baselines in use by most state-of-the-art MT systems today.
Experiments
We tune the the parameters of the MT system on a held-out development corpus of 1,172 parallel sentences, and test on a held-out parallel corpus of 746 parallel sentences.
Related Work
(2009) confirm and extend these results, showing BLEU improvement for a hierarchical phrase-based MT system on a small Chinese corpus.
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Shen, Libin and Xu, Jinxi and Weischedel, Ralph
Discussion
This dependency LM can also be used in hierarchical MT systems using lexical-ized CFG trees.
Experiments
0 filtered: a string-to-string MT system as in baseline.
Introduction
In section 4, we describe the implementation details of our MT system .
MT system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: