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Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

Cristina Bosco
Sara Tonelli
Fabio Massimo Zanzotto

Experimenting the use of catenae in Phrase-Based SMT

Manuela Sanguinetti


Following recent trends on hybridization of machine translation architectures, this paper presents an experiment on the integration of a phrase-based system with syntactically-motivated bilingual pairs, namely the so-called catenae, extracted from a dependency-based parallel treebank. The experiment consisted in combining in different ways a phrasebased translation model, as typically conceived in Phrase-Based Statistical Machine Translation, with a small set of bilingual pairs of such catenae. The main goal is to study, though still in a preliminary fashion, how such units can be of any use in improving automatic translation quality.

L’integrazione di conoscenza linguistica all’interno di sistemi di traduzione automatica statistica è un trend diffuso e motivato dal tentativo di combinare le migliori caratteristiche dei sistemi basati su regole con approcci puramente statistici e basati su corpora. Il presente lavoro si inserisce all’interno di queste ricerche e costituisce uno studio preliminare sull’applicazione di una nozione sintattica basata su dipendenze, quella delle cosiddette ”catenae”, all’interno di una tipica architettura di traduzione statistica.

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1. Introduction

1The hybridization of machine translation systems in order to benefit from both statistical-based and linguistically-motivated approaches is becoming a popular trend in translation field. Such trend is well described in a number of surveys (Costa-Jussá and Farrús, 2014; Costa-Jussá and Fonollosa, 2015) and witnessed by recent initiatives in NLP community, such as the HyTra workshop series1. The motivations to this choice can be manifold, but essentially lie in the need to either reduce the costs - both in terms of time and resources - of building a fully rule-based system, or to integrate statistical models or SMT outputs with linguistic knowledge, as this could be useful to capture complex translation phenomena that data-driven approaches cannot handle properly.

2Such phenomena are often called translational divergences, or even shifts (Catford, 1965), and usually involve a large number of linguistic and extralinguistic factors.

3Our main research interest is the study of such shifts, in particular from a syntactic point of view, and of how such linguistic knowledge could be of any use to overcome the current shortcomings in machine translation.

4The preliminary experiment presented here is therefore guided by the second motivation mentioned above: our basic assumption is that supplementing translation models in classical PhraseBased Statistical Machine Translation (PBSMT) with syntactically-motivated units extracted from parallel treebanks can lead to improvements in machine translation accuracy. This was already demonstrated, for example, in Tinsley (2009), where syntactic contituents were used to improve the translation quality of a PBSMT system. However, instead of a constituency paradigm, we focused on a more dependency-oriented syntactic unit, namely the one of catena. The choice of a dependency-paradigm in general is mainly dictated by the acknowledged fact that dependencies can better represent linguistic phenomena typical of morphologically rich and free-word order languages (see e.g. (Covington, 1990; Goldberg et al., 2013)). On the other hand, to capture translation shifts of various nature, it is necessary to consider a syntactic unit that goes beyond the single node, as also recently pointed out, e.g., in Deng et al. (2015); hence the introduction of the notion of catena in our study.

5In order to verify our assumption, we carried out a preliminary experiment performing several translation tasks, with Italian and English as language pair. For this purpose, a typical phrase-based SMT system was built, using for training the translation model various combinations of baseline SMT configurations and pairs of catenae automatically extracted from a parallel treebank, i.e. ParTUT, and then automatically aligned.

6The remainder of this paper is thus organized as follows: Section 2 introduces the notion of catena, in Section 3 we describe our use of catenae in this experiment, while in Section 4 we describe the training configurations chosen and discuss the results.

2. Catenae: a brief introduction

7A large number of contributions, in MT, provided some hints on the need to infer complex translational patterns - often encoded by one-to-many or many-to-many alignments - by including a more extensive hierarchical notion that goes beyond the mere word level. In constituency frameworks, such notion is fully covered by syntactic phrases, or constituents, while in dependency contexts where this is not explicitly defined - a number of different approaches have been proposed to tackle the problem; Ding and Palmer (2004) (and follow-up works) proposed the extraction and learning of the so-called treelets, which refer to any arbitrary dependency subgraph that does not necessarily goes down to some leaf. Recently though, a new unit type has been defined in dependency framework, which, to a certain extent, linguistically justifies and formalizes the abovementioned notion of treelet (originally conceived for computational purposes only). This is the notion of catena (Latin for ”chain”, pl. catenae). In Kiss (2015), a catena is defined as:

a single w(ord) or a set of w(ords) C such that for all w in C, there is another w’ in C that either immediately dominates or is dominated by w. According to this definition, any given tree or any given subtree of a tree qualifies as a catena.

8As a result, catena is claimed to be more inclusive than constituents, as it does not require the unit to include all the nodes that are dominated. Because of the dominance constraint, however, it cannot be compared to a string either.

Figure 1: Example of catena.

Image 1000000000000278000001183EB500E7.jpg

  • 2 In accordance with the convention used in (Osborne et al., 2012), the words that form a catena are (...)

9Figure 1 shows an example of a sentence represented in an unlabelled dependency graph where each word is assigned an identifier (1, 2, 3, 4, 5). In the sentence, 15 distinct catenae can be identified (including single nodes)2: [1], [2], [3], [4], [5], [1 2], [1 3], [3 5], [4 5], [1 2 3], [1 3 5], [3 4 5], [1 2 3 5], [1 3 4 5], and [1 2 3 4 5] (i.e. the whole dependency graph).

10A catena may thus include both contiguous and non-contiguous sequences of words, such as Let get or Let get updates; however, this is not the case for the string ”Let anyone get public”, since there is no direct path to the word ”public”.

11The usefulness of catenae in theoretic accounts of complex linguistic phenomena has already been widely shown in literature (Osborne, 2005; Osborne et al., 2011; Osborne and Putnam, 2012; Simov and Osenova, 2015). And to our knowledge, only a few NLP studies (even beyond the bare MT field) exploited this syntactic unit for some practical purpose. The only study we are aware of so far is that of Maxwell et al. (2013), who present an approach based on catenae to ad hoc Information Retrieval. It is our opinion, however, that even translation issues can be tackled by integrating such inclusive notion; catenae can be used, for example, to explain and properly identify those cases of one-to-many or many-to-many correspondences, typical of several translation shifts, such as different underlying syntactic structures, MWEs or idioms. For this reason we attempted to exploit them in this experimental study, among other purposes.

3. Catenae extraction and alignment

12The first preprocessing step in this experiment consists in the extraction of the possible catenae from parse trees of a parallel treebank. The resource we used for this purpose is ParTUT, a recently developed parallel treebank for Italian, English and French3 (Sanguinetti and Bosco, 2014). The whole treebank currently comprises an overall amount of 148,000 tokens, with approximately 2,200 sentences in the Italian and English sections respectively, and 1,050 sentences for French.

13For this experiment, we used the Europarl section of the treebank, retaining only the sentence pairs that have a direct correspondence (1:1), hence using a set of 376 pairs with an average of 10K tokens per language. To each monolingual file, formatted in CoNLL, of this parallel set we then applied the script for the extraction of catenae.

14The script basically performs a depth-first search into the dependency tree, and for each node w recursively detects all the possible catenae starting from w to the nodes that, directly or indirectly, it dominates. The output file thus provides for each sentence a sequence of such catenae (one per line).

15Although the parallel sentences perfectly match with each other, this is not obviously the case for catenae as well. For this reason we carried out a further preprocessing step that entailed the automatic alignment of the output English and Italian files containing such catenae. The alignment was performed considering catenae as if they were sentences, thus using the Microsoft Sentence Bilingual Aligner4 (Moore, 2002) as alignment tool, and setting a high-probability threshold (0.99) in order to have a more accurate - though far more reduced5 - pairs of parallel catenae. The set obtained in this step consists of about 1,700 pairs (set A), which was further filtered to obtain a separate subset of pairs - 778 in total - where each catena has a 7-token maximum length (set B). Such subset was created so as to be used in a different training configuration during the translation step (see next section).

16Once extraction and alignment steps were completed, we proceeded with the translation tasks, as detailed in the next section.

4. Using catenae in PBSMT

17To perform the task, we used Moses (Koehn et al., 2007) as translation toolkit, and set up the system so as to train multiple models, that correspond to the baseline model and to the baseline model augmented with catenae in two different ways.

4.1 Data

18Because of its size and availability, the Europarlv7 parallel corpus (Koehn, 2005) was used for training and testing the system.

19To train the baseline translation model, we used a set of 100K parallel sentences, that, however, reduced to an amount of approximately 85K after cleaning up the corpus (we just retained the sentence pairs of up to a 50-tokens length), while we retrieved a far smaller set for tuning (850 sentences) and a set of 1000 sentences for testing.

20As we built a system for both translation directions, the language model was computed for both languages using the entire monolingual sets on the English and Italian sides of the corpus (around 1.9M sentences each).

4.2 Experimental setup

21The baseline system was built using the basic phrase-based model, which typically does not make any explicit use of linguistic information. For language modeling, we opted for the trigram option using the IRSTLM toolkit (Federico et al., 2008).

22The translation model was computed using the default settings provided by the system guidelines. Word alignment was performed with GIZA++ (Och and Ney, 2003) and ’grow-diag-final-and’ as symmetrization heuristic, while a default length of 7 was kept for phrases.

23This model, however, was also adapted so as to be configured with three different options:

  • to be trained with phrase pairs only (BASE-LINE)

  • to be trained by adding to the baseline train-ing corpus the set A of aligned catenae described in Section 3 (BASELINE+TRAIN)

  • to be trained with a combination of multiplesources, i.e. extending Moses’ phrase table with the set B of aligned catenae mentioned in Section 3 (BASELINE+CAT)

Table 1: Translation example.

source sentence

sia l’ Islam che il mondo cristiano sostengono i diritti delle donne


for Islam and Christianity both uphold the rights of women


both Islam that the Christian world are the rights of women


both Islam and Christianity support the rights of women

24The second and third configurations were obtained using a simple approach, i.e. concatenating the bilingual catenae a) to the training files (BASELINE+TRAIN), and b) to the list of the corpus-extracted phrase pairs (BASELINE+CAT); as also suggested in (Bouamor et al., 2012).

25The final translation outputs were then evaluated with BLEU (Papineni et al., 2002) and NIST (Doddington, 2002) scores, and results are discussed in the next section.

4.3 Results

26The findings emerged from the final evaluation, as also reported in Table 2, show very different results both according to the type of configuration used and to the translation direction. However, from such diversified outputs, relevant data can be highlighted.

27Such relevance mainly consists in the improvement of translation quality when simply augmenting the training corpus with other external data (BASELINE+TRAIN). As a matter of fact, although such improvement is far from significant in terms of BLEU score in Italian-to-English translation, its NIST counterpart, together with the overall quality of English-to-Italian translation show more encouraging results, with an increase from 6.2410 to 6.2599 in NIST score for the first case, and a 0.02 and 0.03 points in BLEU and NIST scores respectively, for the the second one. Table 1 shows an example translation of an Italian sentence comparing BASELINE and BASELINE+TRAIN outputs.

28A small improvement is also reported in the NIST score of the Italian-to-English model when adding a set of bilingual catenae into the phrase table (BASELINE+CAT). This case as well may not be particularly significant in itself, though however encouraging, considering the small amount of data that was added with respect to the baseline system. On the other hand, such set does not seem to affect at all the English-to-Italian model. As a matter of fact, it produces the same hypothesis translation than the one produced with the baseline configuration, and both translations are reported to have a lower translation quality with respect to the first system pair, despite the same amount of training data was used in both directions, even for the language modeling. Such result can be probably explained with some error in the tuning process, while the overall lower quality may be explained, we hypothesize, as an effect of translating into a morphologically richer language - though more indepth studies should be carried out to support this hypothesis.

Table 2: Experimental evaluation of Italian-toEnglish and English-to-Italian translation quality under a baseline PBSMT system, and other two PBSMT systems integrated with catenae.























5. Conclusions

29The paper presented a small experiment on the combined use of linguistic knowledge - in the form of syntactically-motivated translation units - and statistical model provided by state-of-the-art machine translation techniques. The results reported here are to be considered preliminary, as they suffer from the absence of systematic procedures and data that could not have been applied so far due to lack of time and proper resources. Still, considering these shortcomings, translation evaluation, at least in one direction, produced promising results. There is however a lot of work to do to under this respect in order to effectively improve translation quality with the help of such linguistic information; for example by scaling up this experiment using a larger set of external data, or using different training configurations, so as to have multiple sources of comparison for final assessments and considerations.


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2 In accordance with the convention used in (Osborne et al., 2012), the words that form a catena are listed in a left-toright order, following their linear order in the sentence.


4 Downloadable here:

5 The extremely smaller amount of aligned catenae may also be explained by the fact that the order in which the source and target sentences (and catenae, in our case) are listed impacts on the amount and quality of the final alignments.


Dipartimento di Informatica, Università degli Studi di Torino -


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