Improving Multilingual Data Synchronization on Wikipedia

Information synchronization of semi-structured data across different languages can be tough. A good example of this is Wikipedia, where tables for the same topic in different languages need to match. We create a new Dataset to help tackle this issue and introduce a two-step approach.

#The Challenge

When you're looking at Wikipedia tables from various languages, you often find that some tables have information missing or outdated. For instance, one language version of a table might list the birthplace of a person, while another version might not. This inconsistency can lead to confusion and misinformation.

We have built a dataset of 100,000 entity-centric tables, known as Wikipedia Infoboxes, in 14 different languages. Out of these, 3,500 pairs have been manually checked to ensure that they synchronize correctly.

#Solution Outline

Our proposed method contains two main parts:

1. Information Alignment - This step is all about mapping the rows from one table to the corresponding rows in another table.
2. Information Update - This step focuses on making sure all aligned tables have the latest information and fill in any missing pieces.

Our method achieved an impressive F1 score of 87.91 when checked for the alignment task, indicating a high level of accuracy. Additionally, when we tested updating tables, our method achieved a 77.28% acceptance rate from Wikipedia editors, validating its effectiveness.

#Why Synchronization is Necessary

Information found in English articles tends to be more timely than in articles from other languages. Cultural differences and variations in editing can lead to mismatched information. For example, someone might write that a specific event happened in English, but that might not be reflected in the same way in Hindi or Spanish.

Wikipedia consists of millions of articles, and maintaining them can be a big job. Many editors focus on English, which means information in other languages might lag. This can create gaps in essential data about global topics.

#Example of Mismatched Information

Take, for example, the Infobox for Janaki Ammal. The English version includes cultural context about the "British Rule of India." However, the Hindi version omits this, creating a gap in understanding. The two tables also differ in how they present information about her thesis, awards, and early education.

To address this issue, we have set out to improve information synchronization across multilingual content. While the task is big and complicated, our focus on semi-structured data, specifically table synchronization, is a good start.

#Dataset and Methodology

The first step in our approach was to create a large-scale dataset of entity-centric Wikipedia Infoboxes. To do this, we extracted tables from Wikipedia pages that appeared in multiple languages. We ensured that these pages matched in terms of dates to maintain the original information.

#Language Selection

We chose 14 different languages for our dataset, which include English, French, German, Korean, Russian, Arabic, Chinese, Hindi, Cebuano, Spanish, Swedish, Dutch, Turkish, and Afrikaans. These selections allow us to cover a wide audience while ensuring a mix of language resources.

#Categories of Information

The dataset covers 21 different categories, ranging from airports to musicians and everything in between. Each category reflects popular topics that people are likely to search for in multiple languages. We found that certain categories, such as airports and movies, had more available tables compared to others.

#Analyzing Missing Information

As we analyzed the extracted tables, we noticed the number of available tables differed across languages. For instance, languages like Afrikaans and Hindi had fewer tables compared to English. This inconsistency in available information presents a significant challenge when trying to synchronize data.

#Proposed Method

Our two-step method consists of information alignment followed by information update.

#Information Alignment

The objective here is to align the rows from two different tables that should be referencing the same information. We developed a method that uses five modules to accomplish this. Each module helps to improve alignment by relaxing specific conditions to create more connections between tables.

1. Corpus-Based: This module matches rows based on word embeddings to see if they represent the same information.

2. Key-Only Module: This module will align rows based purely on their keys, improving the process by focusing on the main identifier of the row.

3. Key-Value Bidirectional: By looking at both the key and value together, this module improves accuracy even further.

4. Key-Value Unidirectional: This module allows for one-way matching, ensuring that we still get useful information even when the alignment is not perfect.

5. Multi-Key Module: This module allows for multiple keys to be aligned, which can help when there are different ways to mention the same thing.

By tuning the modules, we can ensure a reliable connection between tables, addressing the mismatches we observed.

#Information Update

Once we've aligned the rows, the next step is to check for any missing or outdated information. We use a set of rules to manage updates.

1. Row Transfer: This rule helps to bring over missing rows from one table to another.

2. Multi-Match: In cases where multiple keys may be involved, we merge information appropriately.

3. Time-Based Updates: When new information is available, we prioritize updating based on the latest timestamps.

4. Trends: For data that follows a trend, such as statistics, we can intelligently update based on previous values.

5. Appending Values: This rule can add more information from up-to-date rows to outdated ones.

6. Resource Transfer: Information can also flow from high-resource languages to low-resource ones.

7. Row Addition: We facilitate the addition of new rows from larger tables to smaller ones.

By following these rules, we look to maintain accuracy and consistency in the data across languages.

#Evaluating Effectiveness

We carefully assessed the performance of our method through various tests. By comparing the results with existing methods, we clearly see that our approach to information alignment and update is superior.

#Acceptance Rates

When we submitted changes based on our updates, we achieved a 77.28% acceptance rate. This indicates that editors found our suggestions credible and useful, which is crucial for sustaining the quality of content on Wikipedia.

#Future Improvements

Looking ahead, we have several ideas for improving our approach:

1. Expand Beyond Infoboxes: While our method works well for Infoboxes, we are interested in seeing if it can also apply to other types of data.

2. Multi-Language Updates: Instead of just looking at pairs of languages, we want to explore how to perform updates across multiple languages at once.

3. Joint Alignment and Update: Currently, our method processes alignment and updates in two steps. We want to see if it's possible to streamline this process into one unified step.

4. Broader Language and Category Coverage: Our current dataset includes 14 languages and 21 categories. We aim to expand this for better inclusivity.

5. Automation: While manual updates are sometimes necessary, we are exploring how to automate processes using advanced models.

6. Addressing Other Page Elements: Beyond tables, we want to consider how to improve updates for images and article text.

#Ethics in Editing

As we work on bringing better synchronization to multilingual content, we are committed to ethical editing. We acknowledge that Wikipedia relies on human contributions, and our methods are designed to assist rather than replace human effort. We follow strict guidelines to ensure that our updates adhere to Wikipedia's rules.

#Conclusion

Information synchronization across different languages is vital for a global knowledge base like Wikipedia. By developing a comprehensive method for aligning and updating data, we hope to enhance the quality and reliability of multilingual information. With ongoing improvements and a commitment to ethical practices, we aim to contribute significantly to this important task.