We investigate native language identification (LangID) for Brazilian Indigenous Languages (BILs), using the Bible as training data. Our research extends from previous work, by presenting two analyses on the generalization of Bible-based LangID in non-biblical data. First, with newly collected non-biblical datasets, we show that such a LangID can still provide quite reasonable accuracy in languages for which there are more established writing standards, such as Guarani Mbya and Kaigang, but there can be a quite drastic drop in accuracy depending on the language. Then, we applied the LangID on a large set of texts, about 13M sentences from the Portuguese Wikipedia, towards understanding the difficulty factors may come out of such task in practice. The main outcome is that the lack of handling other American indigenous languages can affect considerably the precision for BILs, suggesting the need of a joint effort with related languages from the Americas.

In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.