In today's globalized financial market, the ability to accurately interpret and analyze financial documents and communications in multiple languages is crucial. MT systems offer powerful tools to bridge language barriers, ensuring that financial institutions can access and understand information regardless of its original language. This initiative is vital for fostering equitable financial access and understanding worldwide, especially in underserved and non-English-speaking regions.
However, despite the rapid progress of large and multilingual MT models and LLMs, either close or open source, translation cannot be considered a ``solved" task. Among the different factors that impact translation performance are language diversity and domain exposure of the models. Language diversity refers to the wide range of languages and dialects that must be accurately translated, each with its own nuances and complexities. Domain exposure is the extent to which these models have been trained on specific types of language used in finance, which includes unique terminologies, idiomatic expressions, and context-specific meanings. Additionally, the financial domain's inherent need for high accuracy and confidentiality further complicates the application of MT, as even minor errors can lead to significant misinterpretations and financial losses.
This tutorial will focus on increasing awareness about AI applications in finance beyond English, highlighting the importance of multilingual capabilities in servicing a diverse, global customer base. Additionally, we will analyze the specific challenges faced in applying MT in the financial sector and assess the performance of MT models, particularly in the context of recent advancements in LLMs.
Session 1: Introduction and Fundamentals (25 mins):
Overview of basic principles and techniques in NLP and MT, focusing on key algorithms and tools relevant to finance.
Session 2: Challenges in Financial MT (25 mins):
Discussion of specific challenges in applying MT to the financial sector, including terminology, language diversity, and domain-specific issues.
Break (15 mins)
Session 3: Assessing MT Performance in the LLM Era (25 mins):
Evaluation of MT models, including recent advancements with LLMs, and analysis of challenging outputs specific to finance.
Q&A and Discussion (15 mins)
Open floor for questions, discussion of challenges, and future directions for MT and generative AI in finance.