Conversational AI data collection and platform development

Client Overview

Our client, a leading technology company specializing in conversational AI solutions, sought to expand their existing product to support conversations in several new languages. The goal was to ensure seamless communication for a global user base and enhance the capabilities of their product.

Challenges

The primary challenges faced by our client included the need to:

1. Source and onboard a large team of native language experts proficient in several languages.
2. Create a custom platform to collect audio data tailored to their specific requirements.
3. Generate a substantial and high-quality dataset in each of the new languages for training and improving their conversational AI.

Services Provided

To address these challenges and help our client achieve their objectives, we provided the following services:

1. Native Language Expert Sourcing and Training: We rapidly sourced and onboarded a team of over 300 native language experts. This diverse team was essential to ensuring the authenticity and fluency of the conversations in each language.
2. Custom Audio Data Collection Platform Development: We designed and developed a custom data collection platform tailored to our client's specific needs. This platform allowed for the efficient gathering of audio data, including natural conversations and interactions in the target languages. The platform was designed to be user-friendly, ensuring seamless data collection by the native language experts.
3. Data Collection and Curation: Leveraging our extensive network of language experts and the custom platform, we embarked on a data collection process. This involved collecting a substantial amount of audio data in each of the target languages. The collected data was meticulously curated to ensure its quality and relevance for training the conversational AI system.

Results

Our comprehensive approach to expanding the client's conversational AI to several languages yielded significant results:

• The client successfully integrated support for conversations in the new languages, enabling them to cater to a more diverse user base.
• The custom data collection platform streamlined the process of gathering audio data, saving time and resources while ensuring data quality.
• The curated dataset provided a solid foundation for training and improving the conversational AI system's language capabilities in the new languages.

Overall, our services helped the client achieve their goal of expanding their product's language support and enhancing its global reach. With our assistance, they were able to offer a more inclusive and effective conversational AI solution to users worldwide

Multilingual NLP entity recognition guidelines crafted by expert linguists

Client Overview

Our client, a prominent Fortune 500 tech company specializing in natural language processing (NLP) products, embarked on a significant entity recognition project. The project aimed to advance their NLP capabilities in English and extend entity recognition to 10 additional languages. To achieve this, the client needed assistance in defining entity categories and creating comprehensive annotation guidelines to maintain consistency in data labeling.

Challenges

The challenges faced by our client included:

1. Entity Category Definition: Defining and refining entity categories across multiple languages required linguistic expertise and a principled approach.
2. Annotation Guidelines: The client required precise and principled annotation guidelines that would serve as a reference for annotators across English and 10 additional languages, ensuring consistency in data labeling.

Services Provided

To address these challenges and support our client's entity recognition project, we provided the following services:

1. Linguist Team Assembly: We assembled a team of experienced linguists, each with expertise in multiple languages, to work on this project. This diverse team was crucial in ensuring linguistic accuracy and cultural sensitivity during the entity recognition process.
2. Linguistics-Based Entity Definition: Our linguists used a linguistics-based approach to streamline and solidify entity categories. By leveraging their linguistic expertise, they ensured that entity categories were well-defined, culturally relevant, and consistent across languages.
3. Annotation Guidelines Development: We produced a comprehensive set of annotation guidelines that contained precise instructions and principled principles for entity annotation. These guidelines were crafted to serve as a foundational resource for annotators in English and the 10 additional languages, enabling consistent and high-quality data labeling.

Results

Our services yielded significant results for the client's entity recognition project:

• Linguistically Informed Entity Categories: The client received a set of linguistically informed entity categories that were well-defined and consistent across languages, enhancing the accuracy of their NLP applications.
• Comprehensive Annotation Guidelines: The provided annotation guidelines served as a valuable resource for annotators, ensuring precise and principled data labeling in both English and the 10 additional languages.
• High-Quality Data Annotation: With the support of our linguist team and annotation guidelines, the client achieved high-quality data annotation that was culturally sensitive and linguistically accurate.

Overall, our services empowered the client to establish linguistics-based entity recognition guidelines and maintain data consistency across multiple languages. These guidelines were instrumental in training and testing their NLP applications, enhancing their capabilities and ensuring success in diverse linguistic contexts.

Computational model optimization and language expansion with grammar authoring support.

Client Overview

Our client, a major technology company, requested support for improving two related existing computational models of natural language and expanding them to support input in several world languages. Their goal was to provide a tool for use in automated scheduling and customer service applications.

Challenges

The primary challenges faced by our client included the need to:

• Identify areas to expand existing coverage of the models to include microvariation, ambiguity, and dialectal differences in everyday language usage.
• Improve efficiency of the language representation to optimize model performance.
• Generate comprehensive datasets in a variety of target languages according to specific usage domains and project specifications.

Services Provided

To address these challenges and help our client achieve their objectives, we provided the following services:

1. Native Language Expert Sourcing and Training: Utilizing our multilingual network of linguists and language experts, we were able to rapidly source and identify native speakers in each of the target languages that would be able to collaborate on providing high-quality data for the requested language usage domains. We modified existing onboarding processes from previous similar projects and provided the client with a pilot study.
2. Data Collection and Curation: Leveraging our extensive network of language experts, we embarked on a data collection process. We curated large-scale, comprehensive datasets from the collected native speaker data and passed them to our computational linguists who specialize in mathematical models of language and linguistic theory.
3. Computational Model Authoring and Evaluation: We began by working closely with the client's engineers to familiarize themselves with the underlying logic of the client's existing approach to language modeling, in this case a rule-based approach to recognizing domain-specific language usage. Drawing on our expertise in formal mathematical representations of natural language, we were able to identify numerous improvements to the models’ efficiency and existing coverage. We used the curated cross-linguistic data samples to localize the models into more than 30 languages.

Results

Our comprehensive, data-driven approach to improving and expanding upon our client's existing model led to the following key results:

• The client accomplished both facets of their goal for the project, namely the improvement of existing coverage and the expansion of coverage to include new languages and everyday microvariation. The client was further able to improve the efficiency of their product based on input from our linguists.
• The client was able to use our feedback about evaluation methods to better ensure that both related models achieved identical data coverage.
• We worked closely with the client to develop a pipeline for continued improvements to model performance and additions to model coverage across all included languages.

Overall, our services helped the client achieve their goal of expanding their product's efficiency and language support and enhancing its global reach.

Spoken language modeling in speech recognition with grammar authoring support

Client Overview

Our client, a household name in the technology and NLP industries, wanted to develop a new product for natural language recognition using a formalism with which their team was unfamiliar. This project was intended to model spoken language input adhering to a specific format as part of a larger speech recognition project.

Challenges

Our client faced several challenges related to this task, including:

• A lack of experience or knowledge about the specific formalism or input format for the project
• Uncertainty about the extent of the coverage needed for the specific target language usage domain
• A need to capture variation while limiting the complexity of the model

Services Provided

Based on the aforementioned challenges, our team provided the following services:

1. Dataset creation:We leveraged existing datasets for the target language usage domain from previous services provided to the same client to create an appropriately complex coverage set for the project, with additional data points sourced using our existing network of vetted native speaker contacts. This approach saved time and ensured parity with many of the client's other products in terms of coverage.
2. Computational Linguistic Theoretic Expertise and Model Creation: LDue to strong background training in the mathematical and logical underpinnings of computational models of language, our linguists are well-positioned to quickly become proficient with unfamiliar formalisms. In this case, we found that while the syntax of the required model was new to both the client and our team, the foundation of the model was similar to many of the client's existing products. As a result, our team was able to adapt to the new approach and meet the client's demands well ahead of expected timelines. Furthermore, we were able to discuss why the chosen formalism was not appropriate for some features of the client's requested data coverage, helping them to tailor the use-case of the tool and avoid unnecessary complexity.

Results

We were able to use our broad expertise and knowledge base on this project to help the client accomplish their goals:

• Our familiarity with the client’s family of products allowed us to make use of previously curated data, along with native-speaker-generated variation data, to build a comprehensive coverage set.
• Combined with our strong background in mathematical models of language, this familiarity further resulted in our ability to leverage the client’s related tools to build understanding of the new formalism and provide a useful product that aligned with project specifications.
• The client was additionally able to narrow the goals of the project based on our feedback on the scope of the required formalism.

Ultimately, the client succeeded in their stated goal of obtaining a language representation tool that fit seamlessly into the larger speech recognition project.

Enhancing workforce engagement through customized assessment solutions

Client Overview

Our client, a multinational technology firm, recognized the need to boost employee engagement to improve productivity and reduce turnover. To this end, they sought a partner to develop a nuanced understanding of their workforce's engagement levels.

Challenges

The primary challenge was designing a suite of assessments that not only measured general engagement but also aligned with the company’s unique competencies and culture. The client required a tool that could provide deep insights into employee sentiments and identify leverage points to foster a more engaged workforce.

Solution

• Development of Assessment Suite: Our approach began by collaborating closely with the client to understand their specific competencies of interest. This led to the design of an evidence-based suite of assessments that measured engagement across several dimensions, including job satisfaction, alignment with company values, and individual well-being.
• Custom Platform Creation: We created a tailor-made platform that catered to the client’s specific needs. The platform featured an intuitive user interface and robust backend integration capabilities to ensure seamless data collection and analysis.
• Data Collection: The assessment was rolled out across the entire organization, with a completion rate of over 85%. We employed advanced analytic techniques, including predictive analytics, to interpret the data and distill actionable insights.
• Analysis and Reporting: A comprehensive report detailing the findings was presented to the client. It included visual analytics to illustrate engagement patterns and highlighted both strengths and areas for improvement. We provided a set of targeted recommendations for strategic initiatives to enhance engagement.

Results

The assessment revealed several critical insights:

• High Variability in Departmental Engagement: There was a significant disparity in engagement scores between departments, suggesting that engagement drivers could be highly department-specific.
• Alignment with Company Values: While the majority of employees felt aligned with the company's stated values, there was a disconnect in how these values were practiced within certain teams.
• Actionable Recommendations: Our recommendations included the development of mentorship programs, recognition systems, and customized training for managers. These were geared towards strengthening the identified competencies and addressing cultural nuances.
• Measurable Improvements: Following the implementation of our recommendations, the client reported measurable improvements in key engagement metrics, including a reduction in staff turnover and an increase in employee satisfaction scores.

Conclusion

The assessment initiative spearheaded a shift towards a more data-driven approach to employee engagement within the company. The actionable insights led to targeted interventions that are currently being implemented, with early indicators showing improvements in employee satisfaction and retention rates. Our ongoing partnership with the client ensures that the engagement strategies evolve in line with the changing dynamics of the workforce.