FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
This work addresses the problem of limited resources for open-domain chatbot development in non-English languages like Finnish, though it is incremental as it adapts existing evaluation setups to a new language.
The authors tackled the lack of conversational data and benchmarks for Finnish chatbots by creating FinChat, a publicly available corpus of unscripted Finnish chat conversations on seven topics, and observed that off-the-shelf models perform poorly (no better than chance on automatic metrics and predominantly incoherent in human evaluation) compared to humans who perform almost perfectly.
Creating open-domain chatbots requires large amounts of conversational data and related benchmark tasks to evaluate them. Standardized evaluation tasks are crucial for creating automatic evaluation metrics for model development; otherwise, comparing the models would require resource-expensive human evaluation. While chatbot challenges have recently managed to provide a plethora of such resources for English, resources in other languages are not yet available. In this work, we provide a starting point for Finnish open-domain chatbot research. We describe our collection efforts to create the Finnish chat conversation corpus FinChat, which is made available publicly. FinChat includes unscripted conversations on seven topics from people of different ages. Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development. We observe that off-the-shelf chatbot models trained on conversational corpora do not perform better than chance at choosing the right answer based on automatic metrics, while humans can do the same task almost perfectly. Similarly, in a human evaluation, responses to questions from the evaluation set generated by the chatbots are predominantly marked as incoherent. Thus, FinChat provides a challenging evaluation set, meant to encourage chatbot development in Finnish.