Domain Adaptation of Statistical Machine Translation Models with Monolingual Data for Cross Lingual Information Retrieval

The data-driven Bulgarian WordNet: BTBWNThe paper presents our work towards the simultaneous creation of a data-driven WordNet for Bulgarian and a manually annotated treebank with semantic information. Such an approach requires synchronization of the word senses in both - syntactic and lexical resources, without limiting the WordNet senses to the corpus or vice versa. Our strategy focuses on the identification of senses used in BulTreeBank, but the missing senses of a lemma also have been covered through exploration of bigger corpora. The identified senses have been organized in synsets for the Bulgarian WordNet. Then they have been aligned to the Princeton WordNet synsets. Various types of mappings are considered between both resources in a cross-lingual aspect and with respect to ensuring maximum connectivity and potential for incorporating the language specific concepts. The mapping between the two WordNets (English and Bulgarian) is a basis for applications such as machine translation and multilingual information retrieval. Oparty na danych WordNet bułgarski: BTBWNW artykule przedstawiono naszą pracę na rzecz jednoczesnej budowy opartego na danych wordnetu dla języka bułgarskiego oraz ręcznie oznaczonego informacjami semantycznymi banku drzew. Takie podejście wymaga uzgodnienia znaczeń słów zarówno w zasobach składniowych, jak i leksykalnych, bez ograniczania znaczeń umieszczanych w wordnecie do tych obecnych w korpusie, jak i odwrotnie. Nasza strategia koncentruje się na identyfikacji znaczeń stosowanych w BulTreeBank, przy czym brakujące znaczenia lematu zostały również zbadane przez zgłębienie większych korpusów. Zidentyfikowane znaczenia zostały zorganizowane w synsety bułgarskiego wordnetu, a następnie powiązane z synsetami Princeton WordNet. Rozmaite rodzaje rzutowań są rozpatrywane pomiędzy obydwoma zasobami w kontekście międzyjęzykowym, a także w odniesieniu do zapewnienia maksymalnej łączności i możliwości uwzględnienia pojęć specyficznych dla języka bułgarskiego. Rzutowanie między dwoma wordnetami (angielskim i bułgarskim) jest podstawą dla aplikacji, takich jak tłumaczenie maszynowe i wielojęzyczne wyszukiwanie informacji.

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Domain adaptation for statistical machine translation with monolingual resources

10.3115/1626431.1626468 ◽

2009 ◽

Cited By ~ 23

Author(s):

Nicola Bertoldi ◽

Marcello Federico

Keyword(s):

Machine Translation ◽

Domain Adaptation ◽

Statistical Machine Translation

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An overview of the phrase-based statistical machine translation techniques

The Knowledge Engineering Review ◽

10.1017/s026988891200029x ◽

2012 ◽

Vol 27 (4) ◽

pp. 413-431 ◽

Cited By ~ 1

Author(s):

Marta Ruiz Costa-jussà

Keyword(s):

Machine Translation ◽

Domain Adaptation ◽

Statistical Machine Translation ◽

Scientific Field ◽

Quality Improvements ◽

General Overview ◽

Translation Techniques ◽

Statistical Criteria

AbstractThis work provides a general overview of the statistical machine translation (SMT) scientific field, which is a subfield of machine translation (MT). Specifically, this paper focuses on one of the most popular SMT approaches, that is, the phrase-based system.The phrase-based translation units are typically extracted using statistical criteria, and they are weighted using different models. These models are log-linearly combined in the decoding, which is in charge of choosing the most probable translation. Significant quality improvements have been produced from original phrase-based SMT systems. Among others, the main challenges are reordering, domain adaptation and evaluation.

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Neural Networks Classifier for Data Selection in Statistical Machine Translation

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0027 ◽

2017 ◽

Vol 108 (1) ◽

pp. 283-294 ◽

Cited By ~ 1

Author(s):

Álvaro Peris ◽

Mara Chinea-Ríos ◽

Francisco Casacuberta

Keyword(s):

Neural Networks ◽

Machine Translation ◽

Domain Adaptation ◽

Statistical Machine Translation ◽

Data Selection ◽

Target Domain ◽

Translation Quality ◽

Bilingual Corpora ◽

Proper Estimation ◽

Adaptation Field

AbstractCorpora are precious resources, as they allow for a proper estimation of statistical machine translation models. Data selection is a variant of the domain adaptation field, aimed to extract those sentences from an out-of-domain corpus that are the most useful to translate a different target domain. We address the data selection problem in statistical machine translation as a classification task. We present a new method, based on neural networks, able to deal with monolingual and bilingual corpora. Empirical results show that our data selection method provides slightly better translation quality, compared to a state-of-the-art method (cross-entropy), requiring substantially less data. Moreover, the results obtained are coherent across different language pairs, demonstrating the robustness of our proposal.

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