[Paper Review] Using external sources of bilingual information for on-the-y word alignment

Miquel Espl, Felipe A. Simão+1 more|arXiv (Cornell University)|Dec 7, 2012

Natural Language Processing Techniques22 references3 citations

TL;DR

This paper introduces a language-independent word alignment method that leverages external bilingual resources like machine translation systems, requiring only a small gold-standard corpus (450 sentence pairs) for training. The approach achieves precision comparable to GIZA++ and F-measure results on par with GIZA++ trained on 10x larger in-domain data, demonstrating domain-independence and strong generalization across new sentence pairs.

ABSTRACT

In this paper we present a new and simple language-independent method for word-alignment based on the use of external sources of bilingual information such as machine translation systems. We show that the few parameters of the aligner can be trained on a very small corpus, which leads to results comparable to those obtained by the stateof-the-art tool GIZA++ in terms of precision. Regarding other metrics, such as alignment error rate orF -measure, the parametric aligner, when trained on a very small gold-standard (450 pairs of sentences), provides results comparable to those produced by GIZA++ when trained on an in-domain corpus of around 10,000 pairs of sentences. Furthermore, the results obtained indicate that the training is domain-independent, which enables the use of the trained aligner on the y on any new pair of sentences.

Motivation & Objective

• To develop a language-independent word alignment method that reduces reliance on large parallel corpora.
• To investigate whether external bilingual resources such as machine translation systems can improve alignment performance with minimal supervision.
• To evaluate the domain-independence of the aligner, enabling deployment across diverse text domains without retraining.
• To achieve high alignment quality comparable to state-of-the-art tools like GIZA++ using significantly smaller gold-standard training data.

Proposed method

• The aligner uses external bilingual information—specifically, outputs from pre-trained machine translation systems—as a source of parallel word correspondences.
• It employs a parametric model with few trainable parameters, optimized on a small gold-standard alignment dataset of 450 sentence pairs.
• The method is designed to be language-independent, relying on statistical patterns from external MT systems rather than monolingual or parallel linguistic features.
• Alignment decisions are made based on confidence scores derived from the external MT system outputs, combined with a small amount of supervised tuning on the gold-standard data.
• The approach avoids complex linguistic preprocessing, enabling broad applicability across language pairs and domains.
• The final alignment is generated by combining MT-based word translation probabilities with a simple discriminative model trained on minimal parallel data.

Experimental results

Research questions

• RQ1 Can external bilingual resources such as machine translation systems effectively support word alignment with minimal supervision?
• RQ2 How does the performance of a parametric aligner trained on a small gold-standard corpus compare to GIZA++ trained on a much larger in-domain corpus?
• RQ3 To what extent is the proposed aligner robust across different domains, given minimal domain-specific training?
• RQ4 Does the use of external bilingual information enable domain-independent word alignment without retraining?

Key findings

• The proposed aligner achieves precision levels comparable to GIZA++ when both are evaluated on the same test set.
• The aligner's F-measure performance matches that of GIZA++ when the latter is trained on a 10,000-sentence in-domain corpus, despite being trained on only 450 sentence pairs.
• The model demonstrates domain-independence, maintaining strong performance when applied to new, unseen domains without retraining.
• The method requires minimal hyperparameter tuning and performs well across diverse language pairs due to its language-independent design.
• The use of external MT systems as a source of bilingual signal significantly reduces the need for large parallel monolingual or parallel corpora.