Workshop on Hybrid Approaches to Machine Translation (2016)
fast align is a simple and fast word alignment tool which is widely used in state-of-the-art machine translation systems. It yields comparable results in the end-to-end translation experiments of various language pairs. However, fast align does not perform as well as GIZA++ when applied to language pairs with distinct word orders, like English and Japanese. In this paper, given the lexical translation table output by fast align, we propose to realign words using the hierarchical sub-sentential alignment approach. Experimental results show that simple additional processing improves the performance of word alignment, which is measured by counting alignment matches in comparison with fast align. We also report the result of final machine translation in both English-Japanese and Japanese-English. We show our best system provided significant improvements over the baseline as measured by BLEU and RIBES.
This paper presents the comparison of how using different neural network based language modeling tools for selecting the best candidate fragments affects the final output translation quality in a hybrid multi-system machine translation setup. Experiments were conducted by comparing perplexity and BLEU scores on common test cases using the same training data set. A 12-gram statistical language model was selected as a baseline to oppose three neural network based models of different characteristics. The models were integrated in a hybrid system that depends on the perplexity score of a sentence fragment to produce the best fitting translations. The results show a correlation between language model perplexity and BLEU scores as well as overall improvements in BLEU.
We present a novel method of comparable corpora construction. Unlike the traditional methods which heavily rely on linguistic features, our method only takes image similarity into consid-eration. We use an image-image search engine to obtain similar images, together with the cap-tions in source language and target language. On the basis, we utilize captions of similar imag-es to construct sentence-level bilingual corpora. Experiments on 10,371 target captions show that our method achieves a precision of 0.85 in the top search results.
We present an algorithm for predicting translation equivalents between two languages, based on the corresponding WordNets. The assumption is that all synsets of one of the languages are linked to the corresponding synsets in the other language. In theory, given the exact sense of a word in a context it must be possible to translate it as any of the words in the linked synset. In practice, however, this does not work well since automatic and accurate sense disambiguation is difficult. Instead it is possible to define a more robust translation relation between the lexemes of the two languages. As far as we know the Finnish WordNet is the only one that includes that relation. Our algorithm can be used to predict the relation for other languages as well. This is useful for instance in hybrid machine translation systems which are usually more dependent on high-quality translation dictionaries.
Traditional machine translation evaluation metrics such as BLEU and WER have been widely used, but these metrics have poor correlations with human judgements because they badly represent word similarity and impose strict identity matching. In this paper, we propose some modifications to the traditional measures based on word embeddings for these two metrics. The evaluation results show that our modifications significantly improve their correlation with human judgements.
We describe experiments in Machine Translation using word sense disambiguation (WSD) information. This work focuses on WSD in verbs, based on two different approaches – verbal patterns based on corpus pattern analysis and verbal word senses from valency frames. We evaluate several options of using verb senses in the source-language sentences as an additional factor for the Moses statistical machine translation system. Our results show a statistically significant translation quality improvement in terms of the BLEU metric for the valency frames approach, but in manual evaluation, both WSD methods bring improvements.
We introduce a new statistical machine translation approach specifically geared to learning translation from low resource languages, that exploits monolingual English semantic parsing to bias inversion transduction grammar (ITG) induction. We show that in contrast to conventional statistical machine translation (SMT) training methods, which rely heavily on phrase memorization, our approach focuses on learning bilingual correlations that help translating low resource languages, by using the output language semantic structure to further narrow down ITG constraints. This approach is motivated by previous research which has shown that injecting a semantic frame based objective function while training SMT models improves the translation quality. We show that including a monolingual semantic objective function during the learning of the translation model leads towards a semantically driven alignment which is more efficient than simply tuning loglinear mixture weights against a semantic frame based evaluation metric in the final stage of statistical machine translation training. We test our approach with three different language pairs and demonstrate that our model biases the learning towards more semantically correct alignments. Both GIZA++ and ITG based techniques fail to capture meaningful bilingual constituents, which is required when trying to learn translation models for low resource languages. In contrast, our proposed model not only improve translation by injecting a monolingual objective function to learn bilingual correlations during early training of the translation model, but also helps to learn more meaningful correlations with a relatively small data set, leading to a better alignment compared to either conventional ITG or traditional GIZA++ based approaches.
We defend that bilingual lexicons automatically extracted from parallel corpora, whose entries have been meanwhile validated by linguists and classified as correct or incorrect, should constitute a specific parallel corpora. And, in this paper, we propose to use word-to-word translations to learn morph-units (comprising of bilingual stems and suffixes) from those bilingual lexicons for two language pairs L1-L2 and L1-L3 to induce a bilingual lexicon for the language pair L2-L3, apart from also learning morph-units for this other language pair. The applicability of bilingual morph-units in L1-L2 and L1-L3 is examined from the perspective of pivot-based lexicon induction for language pair L2-L3 with L1 as bridge. While the lexicon is derived by transitivity, the correspondences are identified based on previously learnt bilingual stems and suffixes rather than surface translation forms. The induced pairs are validated using a binary classifier trained on morphological and similarity-based features using an existing, automatically acquired, manually validated bilingual translation lexicon for language pair L2-L3. In this paper, we discuss the use of English (EN)-French (FR) and English (EN)-Portuguese (PT) lexicon of word-to-word translations in generating word-to-word translations for the language pair FR-PT with EN as pivot language. Generated translations are filtered out first using an SVM-based FR-PT classifier and then are manually validated.