Sentence-Alignment in Semi-parallel Datasets (2025)

Abstract: In this paper, we are testing sentence alignment on complex, semi-parallel corpora, i.e., different versions of the same text that have been altered to some extent. We evaluate two hypotheses: To make alignment algorithms more efficient, we test the hypothesis that matching pairs can be found in the immediate vicinity of the source sentence and that it is sufficient to search for paraphrases in a ‘context window’. To improve the alignment quality on complex, semi-parallel texts, we test the implementation of a segmentation into Elementary Discourse Units (EDUs) in order to make more precise alignments at this level. Since EDUs are the smallest possible unit for communicating a full proposition, we assume that aligning at this level can improve the overall quality. Both hypotheses are tested and validated with several embedding models on varying degrees of parallel German datasets. The advantages and disadvantages of the different approaches are presented, and our next steps are outlined.

## introduction the task of sentence alignment originated in the context of machine translation, as the preparatory step for word or phrase alignment, which eventually informed bilingual translation models. in this paper, we address the somewhat different scenario of monolingual semi-parallel text, i.e., different versions of the same text. a well-known case is that of simplified language, where a text in standard language has been mapped to a text that is easier to process for audiences with limited knowledge of the language or people with cognitive or intellectual disabilities. in addition to this, we study two other settings that to our knowledge have not received attention yet. the first are sets of different biographic encyclopedia articles on the same person (authors from the former german democratic republic). the second is a specific use case from the social sciences, viz. the writings of the philosopher hannah arendt, who frequently published second (edited) versions of her works. all our data is in german, but our methods are in principle language-neutral and can be adapted to other target languages, and also to multilingual alignment tasks. these datasets are well-suited for our experiments for several reasons: first, they represent different levels of difficulty in terms of segmentation and alignment. while the plain-language data contains relatively short and concise sentences and the data is relatively parallel, hannah arendt's essays offer significantly greater challenges; they are more heavily altered and the syntactic complexity is greater. the encyclopedia entries represent a special case, as some of the texts are written in terse style, often avoiding full clauses. however, in terms of content they are less parallel than the plainlanguage texts and, therefore, form an interesting complement. in this paper we test two hypotheses: the paper is structured as follows: in section 2, we first describe related work for the most impor-tant concepts of this paper -the notion of semiparallel texts, edu segmentation and sentence alignment. in section 3, we present our datasets in detail. we provide content descriptions in section 3.1 and corpus statistics in section 3.2. in section 3.3 we describe the process and the results of our manual annotation study. in section 4, we explain methods and results of our experimentsseparately for the topics of segmentation, embedding and alignment. section 5 provides a qualitative error analysis, and section 6 summarizes our conclusions and describes next steps. ## data and manual annotation of alignment in this section, we first describe the three sources of data that we are using and how we constructed the corpora; this includes segmenting texts into edus. then we report on our inter-annotator agreement study on the alignment task. ## error analysis the most severe difficulties arise for the arendt essays. when the aligned sentences are examined more closely, it becomes clear that an incorrect assignment may have been made, even though the matched sentences generally fit together well thematically. 'meaning-heavy' terms like names and nouns, which have a great influence on the sentence embeddings, occur repeatedly throughout the dataset and thus make correct assignment more difficult. therefore, part of the problem is that the embeddings of such complex sentences are not finegrained enough to select the actual correct sentence from several potentially-matching sentences. this phenomenon can be observed in the following examples (english translations created by us, not by hannah arendt): the only thing that lures and entices the reader in kafka's work is the truth itself, and kafka succeeded in this enticement with such quiet perfection that his stories cast a spell even if the reader does not at first grasp their actual truthfulness. kafka's real art lies in the fact that the reader endures an indeterminate, vague fascination, which is coupled with the inescapably clear memory of certain images and events that at first appear to make no sense [...] until the true meaning of the story is revealed. as already mentioned in the last section, the alignment of the arendt texts is made more difficult by the fact that the texts were also heavily altered at paragraph level. parts were added or omitted and the sentence order was changed considerably. these characteristics make it very difficult (in particular for the context window) to find the correct correspondences, as the window size would have to be increased significantly and the efficiency advantages of this approach would be lost as a result. ## conclusion the context window shows a superior performance compared to the paraphrase mining function both for alignment quality and alignment speed. however, there are still problems to be solved: if text versions are altered heavily, the window size has to be extended to find the best match. to mitigate this, a previous paragraph alignment could be implemented and the sentence alignment could be performed in a second step. the role of edu segmentation is difficult to assess. the use of edus in the alignment process can only make sense if the sentences are so long on average that several edus are created. however, even the experiments on the hannah arendt data showed that the models achieve slightly worse values on average with edus than with whole sentences. several aspects should be considered here: of all the data we worked with, hannah arendt's essays are by far the least parallel and therefore the most difficult to align. this can already be seen from the proportion of unaligned items in the test data: while less than 25% of the data in the plain language dataset was not aligned, this proportion is more than 35% in the edus of the arendt essays. in addition, alignment is made very difficult by the length of the texts. both the news reports of the plain language dataset (46 segments on average) and the encyclopedia entries (14 segments on average) are short, and correspondences are to be expected in the immediate vicinity. arendt's essays, on the other hand, consist of more than 500 segments. they were restructured on paragraph level and are of different lengths. in contrast, both models achieve very good values on the encyclopedia data, with kappa scores ranging from 0.65 to 0.75. this also shows that the generally poorer scores of the arendt essays are due more to the difficulty of the dataset than to the problems caused by the use of edus in the alignment process. in order to solve the problems described here, several tasks must be tackled in the next steps: to conclusively evaluate the usability of edus for the alignment of complex, semi-parallel texts, further data should be included, which to some extent form a compromise of the datasets available here: they should be longer and more complex than the plain language and encyclopedia data, but more similarly structured on the textual level than the arendt essays. in addition, further models for edu segmentation should be considered, which may also be fine-tuned on the data available. also, it is necessary to thoroughly check the quality of the sentence embeddings. it has been discovered as part of the problem that the embeddings cannot clearly distinguish similar but non-identical phrases. a study that specifically measures the similarity of exchanged words, sentence structures and paraphrases could help to develop more precise embeddings for this use case. finally, a previous paragraph alignment should be tested to mitigate the fact that increased window sizes are necessary to combat alterations on paragraph level. with these additions, it should be possible to further improve sentence alignment on semi-parallel datasets. viewers for their helpful feedback. data from gdr literature encyclopedias was provided by the research group "forschungsplattform literarisches feld ddr". 2 our work is supported by the deutsche forschungsgemeinschaft (dfg), project (524057241) "semi-automatische kollationierung verschiedensprachiger fassungen eines textes".