Benchmarking Natural Language Understanding Services for building Conversational Agents
This provides a comparative evaluation for developers building conversational agents, though it is incremental as it benchmarks existing services without introducing new methods.
The paper benchmarks popular Natural Language Understanding services (Watson, Dialogflow, LUIS, Rasa) on a large multi-domain dataset of 25K utterances, finding that Watson significantly outperforms others in intent classification but performs worse in entity recognition due to low precision.
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer. In this paper, we present the first wide coverage evaluation and comparison of some of the most popular NLU services, on a large, multi-domain (21 domains) dataset of 25K user utterances that we have collected and annotated with Intent and Entity Type specifications and which will be released as part of this submission. The results show that on Intent classification Watson significantly outperforms the other platforms, namely, Dialogflow, LUIS and Rasa; though these also perform well. Interestingly, on Entity Type recognition, Watson performs significantly worse due to its low Precision. Again, Dialogflow, LUIS and Rasa perform well on this task.