We propose a practical approach by integrating Large Language Models (LLMs) with a framework designed to navigate the complexities of Airbnb customer support operations. In this paper, our methodology employs a novel reformatting technique, the Intent, Context, and Action (ICA) format, which transforms policies and workflows into a structure more comprehensible to LLMs. Additionally, we develop a synthetic data generation strategy to create training data with minimal human intervention, enabling cost-effective fine-tuning of our model. Our internal experiments (not applied to Airbnb products) demonstrate that our approach of restructuring workflows and fine-tuning LLMs with synthetic data significantly enhances their performance, setting a new benchmark for their application in customer support. Our solution is not only cost-effective but also improves customer support, as evidenced by both accuracy and manual processing time evaluation metrics.

We introduce two methods to collect additional training data for statistical machine translation systems from public social network content. The first method identifies multilingual content where the author self-translated their own post to reach additional friends, fans or customers. Once identified, we can split the post in the language segments and extract translation pairs from this content. The second methods considers web links (URLs) that users add as part of their post to point the reader to a video, article or website. If the same URL is shared from different language users, there is a chance they might give the same comment in their respective language. We use a support vector machine (SVM) as a classifier to identify true translations from all candidate pairs. We collected additional translation pairs using both methods for the language pairs Spanish-English and Portuguese-English. Testing the collected data as additional training data for statistical machine translations on in-domain test sets resulted in very significant improvements of up to 5 BLEU.