Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents
Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.
To address the first challenge, we propose a novel system that can predict intents from flexible types of inputs: speech, ASR transcripts, or both.
In this work, we propose PolicyIE, a corpus consisting of 5, 250 intent and 11, 788 slot annotations spanning 31 privacy policies of websites and mobile applications.
In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively.
When no translation is performed, mBART's performance is comparable to the current state of the art system (Cross-Lingual BERT by Xu et al. (2020)) for the languages tested, with better average intent classification accuracy (96. 07% versus 95. 50%) but worse average slot F1 (89. 87% versus 90. 81%).
Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.
This is due to the fact that current approaches are built for and trained with clean and complete data, and thus are not able to extract features that can adequately represent incomplete data.