Challenges in Translating West African Greetings

This article looks at the challenges of translating greetings from a West African language into English. Greetings are an important part of communication and culture, but many Translation Systems struggle to handle them properly, especially for less common languages. This paper talks about a new translation dataset called Ikini, which focuses on greetings and how they are used in different Contexts.

#The Importance of Greetings

Greetings are more than just polite words; they hold cultural value and help shape identities. In many cultures, how people greet one another can express respect, friendship, or social status. For instance, in the West African language being discussed, greetings contain specific phrases that carry deep meaning, such as "E ku," which is an essential part of many greetings.

Without these culturally significant phrases, the true meaning behind a greeting could be lost. The way greetings are structured in language also matters. Often, the greetings change based on the time of day, celebrations, or seasons, which adds layers to their meanings.

#Current State of Machine Translation

Over the years, machine translation systems have improved significantly. They can translate many languages, including those that are considered low-resource languages, which have less available data for training systems. However, despite these advancements, translating idiomatic expressions and cultural phrases remains a challenge.

Many popular translation systems, like Google Translate, work well for straightforward sentences but can falter with expressions rich in cultural context. This paper looks specifically at how these systems perform when translating greetings.

#The Ikini Dataset

To better evaluate how translation systems handle greetings, the authors introduced a new dataset called Ikini. This dataset includes many commonly used greetings, along with examples showing how these greetings are used in conversations. The creation process involved three main steps:

1. Gathering Greetings: Collecting greetings that people commonly use in various situations resulted in a wide range of greetings.
2. Creating Example Sentences: For each greeting, sentences were created to show how they are used in context. This was done by native speakers of the language, resulting in a rich set of examples.
3. Translating the Data: After gathering the greetings and examples, a professional translator translated them into English to ensure accuracy.

The result was a dataset with a variety of greetings and their contexts that could be used to test translation systems.

#Experimental Setup

In the experiments, the authors used several existing translation models. These models were selected because they had been trained on many languages. The systems tested were Google Translate, Meta's NLLB, and M2M-100. The goal was to see how well these models could translate greetings compared to translating regular sentences, like those found in movie transcripts.

To evaluate performance, the authors compared the translations generated by these systems using a scoring method called BLEU, which measures how close the translations are to human translations. They also conducted human evaluations, asking native speakers to rate the quality of the translations on different criteria, including how well the meanings were preserved and whether the cultural content was maintained.

#Results of the Experiments

Results showed that while the translation systems performed quite well on movie transcripts, they struggled with greetings. For instance, one model, M2M-100, received high scores when translating regular sentences but scored poorly on translating greetings. This was significant as it highlighted that although these systems were well-trained, they still lacked the ability to handle culturally rich content like greetings accurately.

This mismatch in performance indicates a need for more focused research and data collection. Even when the authors fine-tuned the M2M-100 model using the Ikini dataset, improvements were seen, but the results were still not sufficient for capturing the nuances of greetings.

#Challenges in Translation

One major reason for the shortcomings of translation systems is the ambiguity present in greetings. For example, a single word in a greeting can have multiple meanings depending on context. In the language being studied, the word "ku" could mean either "death" or be a term of endearment, depending on how it's used. This ambiguity can confuse translation models that don't have enough context to draw from.

Furthermore, the systems often generate translations that are too literal or miss cultural references entirely. For instance, the phrase “E ku” is integral to greetings, but translation models often fail to convey its true significance, leading to loss of cultural identity in the translation.

#Analysis of Translation Outputs

The authors analyzed several translations from different models and found mixed results. In some examples, Google Translate and NLLB produced translations that were contextually appropriate. However, in many instances, their outputs did not capture the intended meaning or cultural nuances of the greetings.

For instance, certain expressions related to celebrations were not translated correctly, leading to misinterpretations. The authors noted that even though some models performed well in specific cases, an overall pattern of inadequate translations persisted.

Human evaluations confirmed the findings of the automatic evaluations. Native speakers consistently rated the translations poorly, indicating a struggle with cultural content preservation by the machine translation systems.

#Conclusion and Future Work

This research highlights the challenges of translating greetings in a low-resource language. The introduction of the Ikini dataset provides a valuable resource for evaluating machine translation systems but also showcases the need for more targeted efforts in this area.

While the existing models can translate regular texts well, they fall short with culturally rich content like greetings. Future research will aim to expand the Ikini dataset by adding more examples, particularly those related to different professions. The authors also plan to explore more advanced methods to enhance translation accuracy, such as using verb disambiguation techniques or integrating external knowledge sources.

This study serves as an important step in understanding the limitations of machine translation in relation to cultural expressions and calls for continued efforts to bridge this gap.