Improving J apanese- E nglish Patent Claim Translation with Clause Segmentation Models based on Word Alignment (2025)

Abstract: In patent documents, patent claims represent a particularly important section as they define the scope of the claims. However, due to the length and unique formatting of these sentences, neural machine translation (NMT) systems are prone to translation errors, such as omissions and repetitions. To address these challenges, this study proposes a translation method that first segments the source sentences into multiple shorter clauses using a clause segmentation model tailored to facilitate translation. These segmented clauses are then translated using a clause translation model specialized for clause-level translation. Finally, the translated clauses are rearranged and edited into the final translation using a reordering and editing model. In addition, this study proposes a method for constructing clause-level parallel corpora required for training the clause segmentation and clause translation models. This method leverages word alignment tools to create clause-level data from sentence-level parallel corpora. Experimental results demonstrate that the proposed method achieves statistically significant improvements in BLEU scores compared to conventional NMT models. Furthermore, for sentences where conventional NMT models exhibit omissions and repetitions, the proposed method effectively suppresses these errors, enabling more accurate translations.

## introduction the claims in patent documents are critically important for defining the scope of patent rights. however, due to the length and unique descriptive style of these sentences, neural machine translation (nmt) models often encounter issues such as omissions and repetitions in translation. figure 1 shows the distribution of subword token lengths for © 2025 the authors. this article is licensed under a creative commons 4.0 licence, no derivative works, attribution, cc-by-nd. japanese patent claims included in the japanese-english patent parallel corpus japarapat @xcite and for japanese sentences commonly used in the aspec @xcite japanese-english parallel corpus. comparing the two reveals that the patent parallel corpus used in this study has a higher proportion of long sentences compared to scientific paper's abstract. the divideand-conquer translation approach is known to be an effective method for addressing challenges in long sentences translation. @xcite proposed a method in statistical machine translation (smt) that segments input sentences into clause units based on syntactic parsing, translates each clause separately, and then reorders them according to their hierarchical structure. this approach was shown to improve translation accuracy. applying this divide-and-conquer approach to neural machine translation (nmt), @xcite proposed a "divide-and-conquer neural machine translation" method for english-japanese translation, which divides input sentences into clauses based on syntactic parsing and reassembles them after translation. while this method demonstrated the potential to improve translation accuracy, challenges remained in selecting appropriate clause units and ensuring accurate reassembly after clause translation. in response, @xcite addressed two challenges highlighted in the document @xcite : the selection of clause segmentation units and the translation accuracy of clauses after segmentation. they sought to improve translation accuracy by adopting clause segmentation based on conjunctions and utilizing mbart @xcite , a pretrained model, for both the clause translation model and the reordering/editing model. additionally, they attempted to enhance clause translation accuracy by fine-tuning the clause translation model with pseudo-parallel data at the clause level. experiments showed a significant reduction in excessively long translations, as well as suppression of hallucinations and repetitions. these findings suggest that the divide-and-conquer translation method has the potential to mitigate translation errors commonly caused by conventional nmt models in long sentences translation. however, the study @xcite has some limitations. one issue is that clause segmentation based on conjunctions sometimes fails to divide long sentences into appropriately short clauses. another issue is that the clause translation model was trained using pseudo-parallel data rather than real parallel data collected from actual sources. based on the above, this study proposes a novel approach to the divide-and-conquer translation method, specifically targeting japanese-english translation of patent claims, which differs from previous studies @xcite . in this method, we introduce a clause segmentation model that divides the source patent claim sentences into clauses optimized for translation by the model. in particular, this study ensures that the clause units, determined based on word alignments in parallel texts, are consistent between the two languages. by doing so, the proposed method suppresses errors such as omissions and repetitions in the final translations, enabling the generation of more accurate translations. specifically, we propose a method to generate high-quality clause-level parallel data from the original parallel corpus using a word alignment tool. furthermore, we propose a method to construct the following three models using the generated clause-level parallel corpus: ## method figure 2 illustrates the overall structure of the proposed method. the source japanese patent claim is first divided into multiple clauses using the clause segmentation model. each clause is then translated by the clause translation model, and finally, the translated clauses are integrated by the reordering and editing model to generate the english patent claim. this method aims to suppress omissions and repetitions that are commonly encountered in conventional nmt models. it is worth noting that the term clause used in this study does not refer to syntactic clauses in the traditional linguistic sense. as shown in figure 2 , in our approach, japanese sentences are first segmented at punctuation marks, and adjacent segments are then grouped based on word alignments to ensure semantic correspondence with the english side. thus, we define clauses as semantically coherent segments that preserve consistency between source and target languages. this operational definition aims to support alignment quality rather than adhere strictly to syntactic boundaries. ## conclusion in this study, we proposed a translation method to address the translation errors of "omissions" and "repetitions" which are common challenges in japanese-english translation of patent claims. the method focuses on the fact that patent claims are often long and have unique structures, utilizing a clause segmentation model to divide patent claims into more translatable units. the experimental results demonstrated that the proposed method achieved statistically significant improvements over the baseline model in bleu scores. notably, it showed remarkable improvements even for sentences prone to omissions and repetitions. these results confirm the effectiveness of the proposed method in resolving issues of omissions and repetitions in the translation of patent claims. an important direction for future work is to extend the evaluation to include state-of-the-art large language models (llms), which we plan to pursue in our subsequent research.