One of the main challenges in Spoken Language Understanding (SLU) is dealing with ‘open-vocabulary’ slots. Recently, SLU models based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. This paper proposes data noising, which reflects the characteristics of the ‘open-vocabulary’ slots, for data augmentation. We applied it to an attention based bi-directional recurrent neural network (Liu and Lane, 2016) and experimented with three datasets: Airline Travel Information System (ATIS), Snips, and MIT-Restaurant. We achieved performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score), respectively. Our method is advantageous because it does not require additional memory and it can be applied simultaneously with the training process of the model.

This paper describes a sentence pattern-based English-Korean machine translation system backed up by a rule-based module as a solution to the translation of long sentences. A rule-based English-Korean MT system typically suffers from low translation accuracy for long sentences due to poor parsing performance. In the proposed method we only use chunking information on the phrase-level of the parse result (i.e. NP, PP, and AP). By applying a sentence pattern directly to a chunking result, the high performance of analysis and a good quality of translation are expected. The parsing efficiency problem in the traditional RBMT approach is resolved by sentence partitioning, which is generally assumed to have many problems. However, we will show that the sentence partitioning has little side effect, if any, in our approach, because we use only the chunking results for the transfer. The coverage problem of a pattern-based method is overcome by applying sentence pattern matching recursively to the sub-sentences of the input sentence, in case there is no exact matching pattern to the input sentence.