| Title | Natural Spoken Instructions Understanding for Robot with Dependency Parsing |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Chai, Yadeng, Liu, Yong |
| Conference Name | 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) |
| Keywords | benchmark ATIS task, compositionality, control engineering computing, Cyber Dependencies, deep learning algorithms, dependency features, dependency parsing tree, FBM3, Filling, grammars, Hidden Markov models, human factors, human-robot interaction, intent determination, Labeling, learning (artificial intelligence), machine learning, Metrics, natural language processing, natural spoken instruction understanding, neural nets, Neural Network, performance level, pubcrawl, Resiliency, Robot, robots, Scalability, slot filling tasks, speech processing, spoken instruction understanding module, spoken language understanding system, syntactic information, syntactic-based model, Syntactics, Task Analysis, trees (mathematics), window vector |
| Abstract | This paper presents a method based on syntactic information, which can be used for intent determination and slot filling tasks in a spoken language understanding system including the spoken instructions understanding module for robot. Some studies in recent years attempt to solve the problem of spoken language understanding via syntactic information. This research is a further extension of these approaches which is based on dependency parsing. In this model, the input for neural network are vectors generated by a dependency parsing tree, which we called window vector. This vector contains dependency features that improves performance of the syntactic-based model. The model has been evaluated on the benchmark ATIS task, and the results show that it outperforms many other syntactic-based approaches, especially in terms of slot filling, it has a performance level on par with some state of the art deep learning algorithms in recent years. Also, the model has been evaluated on FBM3, a dataset of the RoCKIn@Home competition. The overall rate of correctly understanding the instructions for robot is quite good but still not acceptable in practical use, which is caused by the small scale of FBM3. |
| DOI | 10.1109/CYBER46603.2019.9066566 |
| Citation Key | chai_natural_2019 |
Natural Spoken Instructions Understanding for Robot with Dependency Parsing