Using collocation segmentation to augment the phrase table

This paper describes the 2010 phrase-based statistical machine translation system developed at the TALP Research Center of the UPC1 in cooperation with BMIC2 and VMU3. In phrase-based SMT, the phrase table is the main tool in translation. It is created extracting phrases from an aligned parallel corpus and then computing translation model scores with them. Performing a collocation segmentation over the source and target corpus before the alignment causes that di erent and larger phrases are extracted from the same original documents. We performed this segmentation and used the union of this phrase set with the phrase set extracted from the nonsegmented corpus to compute the phrase table. We present the con gurations considered and also report results obtained with internal and o cial test sets.Postprint (published version

UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, Gravity- Counts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the Frenchto- English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems.Postprint (published version

Implementación de una segmentación estadística complementaria para extraer unidades de traducción en un sistema de traducción estadístico basado en frases

Este artículo evalúa un nuevo método de segmentación en un sistema de traducción automática estadístico basado en frases. La técnica de segmentación se implementa tanto en la parte fuente como en la parte destino y se usa para extraer unidades de traducción. Los resultados mejoran el sistema de referencia en la tarea español-inglés del EuroParl.This report evaluates the impact of using a novel collocation segmentation method for phrase extraction in the standard phrase-based statistical machine translation approach. The collocation segmentation technique is implemented simultaneously in the source and target side. The resulting collocation segmentation is used to extract translation units. Experiments are reported in the Spanish-to-English EuroParl task and promising results are achieved in translation quality.This work has been partially funded by the Spanish Department of Education and Science through the Juan de la Cierva fellowship program. The authors also wants to thank the Barcelona Media Innovation Centre for its support and permission to publish this research

Integration of statistical collocation segmentations in a phrase-based statistical machine translation system

This study evaluates the impact of integrating two different collocation segmentations methods in a standard phrase-based statistical machine translation approach. The collocation segmentation techniques are implemented simultaneously in the source and target side. Each resulting collocation segmentation is used to extract translation units. Experiments are reported in the English-to-Spanish Bible task and promising results (an improvement over 0.7 BLEU absolute) are achieved in translation quality

Integration of statistical collocation segmentations in a phrase-based statistical machine translation system

This study evaluates the impact of integrating two different collocation segmentations methods in a standard phrase-based statistical machine translation approach. The collocation segmentation techniques are implemented simultaneously in the source and target side. Each resulting collocation segmentation is used to extract translation units. Experiments are reported in the English-to-Spanish Bible task and promising results (an improvement over 0.7 BLEU absolute) are achieved in translation quality

Synonym Extraction and Abbreviation Expansion with Ensembles of Semantic Spaces

Background: Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results: A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions: This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models – with different model parameters – and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks

Using collocation segmentation to augment the phrase table

This paper describes the 2010 phrase-based statistical machine translation system developed at the TALP Research Center of the UPC1 in cooperation with BMIC2 and VMU3. In phrase-based SMT, the phrase table is the main tool in translation. It is created extracting phrases from an aligned parallel corpus and then computing translation model scores with them. Performing a collocation segmentation over the source and target corpus before the alignment causes that di erent and larger phrases are extracted from the same original documents. We performed this segmentation and used the union of this phrase set with the phrase set extracted from the nonsegmented corpus to compute the phrase table. We present the con gurations considered and also report results obtained with internal and o cial test sets

UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, Gravity- Counts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the Frenchto- English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems