In this paper, we report our semantic tagging tool for spoken dialogue corpus. This tagging tool can acquire analysis rules using Transformation-based Learning (TBL) from small scale training corpus. It can learn dialogue act tagging rules and semantic frame tagging rules. The precisions are 72% in dialogue act tagging and 58% of semantic frame tagging in open test.

The development of conversational multidomain spoken dialogue systems poses new challenges for the reliable processing of less restricted user utterances. Unlike in controlled and restricted dialogue systems a simple oneto-one mapping from words to meanings is no longer feasible here. In this paper two different approaches to the resolution of lexical ambiguities are applied to a multi-domain c...

There is a strong relationship between evaluation and methods for automatically training language processing systems, where generally the same resource and metrics are used both to train system components and to evaluate them. To date, in dialogue systems research, this general methodology is not typically applied to the dialogue manager and spoken language generator. I will argue that any metr...

Spoken Dialogue Systems are man-machine interfaces which use speech as the medium of interaction. In recent years, dialogue optimization using reinforcement learning has evolved to be a state of the art technique. The primary focus of research in the dialogue domain is to learn some optimal policy with regard to the task description (reward function) and the user simulation being employed. Howe...

Given the state of the art of current language and speech technology, errors are unavoidable in present-day spoken dialogue systems. Therefore, one of the main concerns in dialogue design is how to decide whether or not the system has understood the user correctly. In human-human communication, dialogue participants are continuously sending and receiving signals on the status of the information...

The dialogue strategies used by a spoken dialogue system strongly influence performance and user satisfaction. An ideal system would not use a single fixed strategy, but would adapt to the circumstances at hand. To do so, a system must be able to identify dialogue properties that suggest adaptation. This paper focuses on identifying situations where the speech recognizer is performing poorly. W...

Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding er...

A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into derivation rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an ...

Given the state of the art of current language and speech technology , errors are unavoidable in present-day spoken dialogue systems. Therefore, one of the main concerns in dialogue design is how to decide whether or not the system has understood the user correctly. In human-human communication, dialogue participants are continuously sending and receiving signals on the status of the informatio...

We present a new two-tier user simulation model for learning adaptive referring expression generation (REG) policies for spoken dialogue systems using reinforcement learning. Current user simulation models that are used for dialogue policy learning do not simulate users with different levels of domain expertise and are not responsive to referring expressions used by the system. The twotier mode...