SKOPE: A connectionist/symbolic architecture of spoken Korean processing

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for unique processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, which emphasizes that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) the linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. The phoneme recognition, morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be presented at IJCAI95 workshops on new approaches to learning for natural language processin

Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean

A new tightly coupled speech and natural language integration model is presented for a TDNN-based continuous possibly large vocabulary speech recognition system for Korean. Unlike popular n-best techniques developed for integrating mainly HMM-based speech recognition and natural language processing in a {\em word level}, which is obviously inadequate for morphologically complex agglutinative languages, our model constructs a spoken language system based on a {\em morpheme-level} speech and language integration. With this integration scheme, the spoken Korean processing engine (SKOPE) is designed and implemented using a TDNN-based diphone recognition module integrated with a Viterbi-based lexical decoding and symbolic phonological/morphological co-analysis. Our experiment results show that the speaker-dependent continuous {\em eojeol} (Korean word) recognition and integrated morphological analysis can be achieved with over 80.6% success rate directly from speech inputs for the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer processing of oriental language journa

Chart-driven Connectionist Categorial Parsing of Spoken Korean

While most of the speech and natural language systems which were developed for English and other Indo-European languages neglect the morphological processing and integrate speech and natural language at the word level, for the agglutinative languages such as Korean and Japanese, the morphological processing plays a major role in the language processing since these languages have very complex morphological phenomena and relatively simple syntactic functionality. Obviously degenerated morphological processing limits the usable vocabulary size for the system and word-level dictionary results in exponential explosion in the number of dictionary entries. For the agglutinative languages, we need sub-word level integration which leaves rooms for general morphological processing. In this paper, we developed a phoneme-level integration model of speech and linguistic processings through general morphological analysis for agglutinative languages and a efficient parsing scheme for that integration. Korean is modeled lexically based on the categorial grammar formalism with unordered argument and suppressed category extensions, and chart-driven connectionist parsing method is introduced.Comment: 6 pages, Postscript file, Proceedings of ICCPOL'9

SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task

Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief state input. The self-feeding belief state input increases the accuracy in multi-turn dialogue by summarizing previous dialogue. Also, we newly developed a slot-gate auxiliary task. This new auxiliary task helps classify whether a slot is mentioned in the dialogue. Our model achieved the best score in a few-shot setting for four domains on multiWOZ 2.0.Comment: Accepted in INTERSPEECH 202

DORA: Toward Policy Optimization for Task-oriented Dialogue System with Efficient Context

Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, we propose a multi-domain task-oriented dialogue system, called Dialogue System with Optimizing a Recurrent Action Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy. This dialogue policy recurrently generates explicit system actions as a both word-level and high-level policy. As a result, DORA is clearly optimized during both SL and RL steps by using an explicit system action policy that considers an efficient context instead of the entire dialogue history. The system actions are both interpretable and controllable, whereas the latent actions are not. DORA improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on MultiWOZ 2.1.Comment: 23 pages, 9 figures, submitted to Computer Speech ans Language journa