Generative and discriminative algorithms for spoken language understanding

Abstract

Spoken Language Understanding (SLU) for conversational systems (SDS) aims at extracting concept and their relations from spontaneous speech. Previous approaches to SLU have modeled concept relations as stochastic semantic networks ranging from generative approach to discriminative. As spoken dialog systems complexity increases, SLU needs to perform understanding based on a richer set of features ranging from a-priori knowledge, long dependency, dialog history, system belief, etc. This paper studies generative and discriminative approaches to modeling the sentence segmentation and concept labeling. We evaluate algorithms based on Finite State Transducers (FST) as well as discriminative algorithms based on Support Vector Machine sequence classifier based and Conditional Random Fields (CRF). We compare them in terms of concept accuracy, generalization and robustness to annotation ambiguities. We also show how non-local non-lexical features (e.g. a-priori knowledge) can be modeled with CRF which is the best performing algorithm across tasks. The evaluation is carried out on two SLU tasks of different complexity, namely ATIS and MEDIA corpora.

A survey of joint intent detection and slot-filling models in natural language understanding

“…[Yulan He and Young 2003] were the first to use the combined set for language understanding. [Raymond and Riccardi 2007] used the same set but tweaked the annotation to something more closely resembling the ATIS set used today. The set contains 4978 training samples and 893 test samples.…”

“…In more recent years with the advent of neural net models, 500 of the training samples are set aside as a validation set. [Tur et al 2010] work towards formalising the ATIS data set, using the same samples as [Yulan He and Young 2003] and [Raymond and Riccardi 2007]. The intents listed by [Tur et al 2010] are not the current ones as they list 17 intents each of which have non-zero frequency in train and test set.…”

“…They ran a set of different methods from a boosted tree ensemble to a BiLSTM net on ATIS slot tagging with and without named entity tag labelling. They use the same data as [Raymond and Riccardi 2007], which removes issues with position labels (B,I,O) by collapsing semantic spans as single tokens. For example, 'san jose' is a single token not two.…”

Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks

“…Slot filling can be treated as a sequence labeling task. The traditional method based on conditional random fields (CRF) architecture, which has a strong ability on sequence labeling tasks [26]. Another line of popular approaches is CRF-free sequential labeling.…”

“…Intent detection is regarded as an utterance classification problem to predict an intent label, which can be modeled using conventional classifiers, including regression, support vector machine (SVM) [9] or recurrent neural network (RNN) [19]. The slot filling task can be formulated as a sequence labeling problem, and the most popular approaches with good performances are conditional random field (CRF) [26] and long short-term memory (LSTM) networks [35].…”