Using Storyboards to Improve Translations in Low-Resource Languages

Low-resource Languages struggle to get good quality language data. This often happens because data collection relies on translating from languages that have more resources, like English. However, this method can lead to translations that don't sound natural or fluent in the target language. This issue is known as Translationese.

Translationese is when translated sentences seem awkward, unnatural, or overly formal. Words and sentence structures often mirror the source language in ways that don’t feel right to native speakers of the target language. Even skilled translators face difficulties in capturing the subtle meanings and nuances of the original text, which can make the final translation sound stiff or awkward.

While past research has tried to fix translationese issues after translations are done, most methods focus on treating these awkward translations as a separate problem rather than preventing them right from the start. In this work, a new method is introduced using Storyboards as a way to collect data that minimizes translationese. By using visuals rather than text, the goal is to encourage native speakers to provide more natural translations.

#Overview of the Storyboard Approach

The storyboard approach uses images to help native speakers create translations that feel more fluent. To do this, the process involves showing participants a series of images representing a story, without giving them access to the text in another language. Then, participants describe what they see, using their own words in their native language.

This method is a shift from traditional methods, which usually require speakers to translate provided sentences directly. By focusing on visual stimuli, it aims to gather translations that are more natural and fluent. The big question is whether using storyboards can really reduce the awkwardness that comes with typical translations.

#Challenges in Low-Resource Languages

When working with low-resource languages, it’s often hard to find enough quality data. Many of these languages lack extensive resources like dictionaries or databases. Therefore, researchers often resort to translating content from languages that are more widely spoken. This can lead to a cycle where these languages never get a chance to develop their own unique linguistic resources.

Translationese often becomes a significant challenge. When translating from a resource-rich language, the translated sentences can sound too formal or stilted. Native speakers might find these translations awkward, and this can create a barrier in effective communication, impeding things like language learning or usage in technology.

#The Effects of Translationese

Translationese can introduce many issues for both machine translation and human communication. It can create biases, make sentences sound unnatural, and affect the overall quality of communication. If the translations don’t flow well, it can confuse the audience and may even distort the intended message.

Researchers have been working on various strategies to deal with translationese. These strategies often involve adjusting the data after it has been translated, which can require extra steps and resources. The goal has usually been to fix translations rather than prevent issues from happening in the first place.

#Storyboards as a Solution

The goal of this new approach is to collect translations in a way that aims to reduce translationese right from the start. By using storyboards, researchers can present images that encourage native speakers to describe scenes without being influenced by source language text.

The storyboard method works by presenting participants with images showing different scenes, alongside English sentences, an hour before they begin the actual translation task. This allows participants to get a sense of the context. When the time comes to describe what they see, they can do so without direct access to the English sentences. It is thought that removing this exposure will help produce more fluent and natural descriptions.

#Key Contributions

The research makes three major contributions:

1. It collects data in four different low-resource African languages (Hausa, Ibibio, Swahili, and another) while aiming to reduce translationese.
2. It evaluates how effective the storyboard approach is in generating more fluent sentences.
3. It creates the first parallel resource for Ibibio that’s not focused on religious content.

#Data Collection Process

To gather data, images and their respective English descriptions are acquired. Native speakers form two groups: one group translates the sentences, while the other is shown the storyboard images. This dual approach aims to see how reliance on English influences the resulting translations.

To control for variables, a traditional text translation group is also included as a baseline to compare against the storyboard translations. This group translates the same English sentences directly.

#The Role of Control Groups

Having a control group is essential for understanding the effectiveness of the new storyboard method. This group helps researchers gauge how traditional translation methods stack up against newer methods. Participants in the control group translate sentences directly from the English text, which helps identify how much translationese appears in each method.

#Annotator Preparation

Before the actual translation process, participants in the storyboard group gather for a brief session where they familiarize themselves with the images and sentences. After this session, there’s a one-hour break before they begin translating. This gap helps absorb the visual information while minimizing direct influence from the English text.

The primary goal is to focus purely on the visual content during translation. By doing so, the hope is to allow for a more authentic representation of the language being translated.

#Evaluation of the Method

To see if the storyboard method works, researchers will evaluate the Accuracy and Fluency of translations produced through both the storyboard and text methods. Native speakers, who are also fluent in English, will be asked to assess the translations.

Fluency assessment focuses on how smooth and natural the sentences sound, while accuracy looks at how well the meaning of the original text is captured. Comparing the results from both methods will provide insights into what works better in reducing translationese.

#Results and Observations

Initial evaluations suggest that while traditional text translations score better for accuracy, the storyboard method has the edge when it comes to fluency. This aligns with the expectation that translations based solely on visual stimuli result in more natural-sounding sentences.

While the text translations capture more of the semantic content, the storyboard translations showcase improvements in the overall flow and readability in the target languages. This highlights a critical trade-off between accuracy and fluency.

#Reflection on the Findings

The storyboard approach highlights both strengths and weaknesses. The method’s more natural translations come at the cost of some accuracy. The absence of direct exposure to the source text means some nuances may be missed, affecting precision.

However, by refining the storyboards to provide clearer context, translators can better capture essential details during the annotation phase. Additionally, using post-processing techniques could further align translations with the original content while preserving their naturalness.

#Future Directions

The storyboard method, while innovative, has its challenges, particularly in creating detailed storyboards. A possible solution may lie in using generative AI models to help automate the creation of storyboards.

By integrating AI technology, researchers could streamline the storyboard design process and focus more on data collection and analysis. This could lead to more effective storyboard preparation and improve the quality of translations generated.

Looking ahead, the plan is to expand the complexity of messages captured in the storyboard. Further research can explore how to improve the overall accuracy of the storyboard translations while keeping the fluency benefits.

In conclusion, this new method offers a promising avenue for gathering translation data in low-resource languages while tackling the issue of translationese head-on. The balance between accuracy and fluency achieved through visual-based translation methods could pave the way for enhanced language resources and better communication in various fields.

The implications of this work show the potential for improvements in machine translation tasks and other areas that require robust cross-lingual data. By fostering a better understanding of how to effectively collect data, the field can work towards diminishing the negative effects of translationese while enriching the languages involved.