220 research outputs found
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding
Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the anno-tation cost, a key challenge for design-ing such an ontology is to define a coher-ent slot set while considering their com-plex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utter-ances and semantic elements without the need of corpus annotations. Specifically, our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic pars-ing using latent MF techniques. To fur-ther consider the global semantic struc-ture, such as inter-word and inter-slot re-lations, we augment the latent MF-based model with a knowledge graph propaga-tion model based on a slot-based seman-tic graph and a word-based lexical graph. Our experiments show that the proposed MF approaches produce better SLU mod-els that are able to predict semantic slots and word patterns taking into account their relations and domain-specificity in a joint manner.
SKATE: A Natural Language Interface for Encoding Structured Knowledge
In Natural Language (NL) applications, there is often a mismatch between what
the NL interface is capable of interpreting and what a lay user knows how to
express. This work describes a novel natural language interface that reduces
this mismatch by refining natural language input through successive,
automatically generated semi-structured templates. In this paper we describe
how our approach, called SKATE, uses a neural semantic parser to parse NL input
and suggest semi-structured templates, which are recursively filled to produce
fully structured interpretations. We also show how SKATE integrates with a
neural rule-generation model to interactively suggest and acquire commonsense
knowledge. We provide a preliminary coverage analysis of SKATE for the task of
story understanding, and then describe a current business use-case of the tool
in a specific domain: COVID-19 policy design.Comment: Accepted at IAAI-2
A usage-based model for the acquisition of syntactic constructions and its application in spoken language understanding
Gaspers J. A usage-based model for the acquisition of syntactic constructions and its application in spoken language understanding. Bielefeld: Universitätsbibliothek Bielefeld; 2014
ZERO-SHOT LEARNING OF INTENT EMBEDDINGS FOR EXPANSION BY CONVOLUTIONAL DEEP STRUCTURED SEMANTIC MODELS
ABSTRACT The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A convolutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances. Then it can flexibly generate new intent embeddings without the need of training samples and model-retraining, which bridges the semantic relation between seen and unseen intents and further performs more robust results. Experiments show that CDSSM is capable of performing zero-shot learning effectively, e.g. generating embeddings of previously unseen intents, and therefore expand to new intents without re-training, and outperforms other semantic embeddings. The discussion and analysis of experiments provide a future direction for reducing human effort about annotating data and removing the domain constraint in spoken dialogue systems. Index Terms-zero-shot learning, spoken language understanding (SLU), spoken dialogue system (SDS), convolutional deep structured semantic model (CDSSM), embeddings, expansion
Dialogue State Induction Using Neural Latent Variable Models
Dialogue state modules are a useful component in a task-oriented dialogue
system. Traditional methods find dialogue states by manually labeling training
corpora, upon which neural models are trained. However, the labeling process
can be costly, slow, error-prone, and more importantly, cannot cover the vast
range of domains in real-world dialogues for customer service. We propose the
task of dialogue state induction, building two neural latent variable models
that mine dialogue states automatically from unlabeled customer service
dialogue records. Results show that the models can effectively find meaningful
slots. In addition, equipped with induced dialogue states, a state-of-the-art
dialogue system gives better performance compared with not using a dialogue
state module.Comment: IJCAI 202
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