TY - JOUR
T1 - Speech understanding for spoken dialogue systems
T2 - From corpus harvesting to grammar rule induction
AU - Iosif, Elias
AU - Klasinas, Ioannis
AU - Athanasopoulou, Georgia
AU - Palogiannidi, Elisavet
AU - Georgiladakis, Spiros
AU - Louka, Katerina
AU - Potamianos, Alexandros
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/1
Y1 - 2018/1
N2 - We investigate algorithms and tools for the semi-automatic authoring of grammars for spoken dialogue systems (SDS) proposing a framework that spans from corpora creation to grammar induction algorithms. A realistic human-in-the-loop approach is followed balancing automation and human intervention to optimize cost to performance ratio for grammar development. Web harvesting is the main approach investigated for eliciting spoken dialogue textual data, while crowdsourcing is also proposed as an alternative method. Several techniques are presented for constructing web queries and filtering the acquired corpora. We also investigate how the harvested corpora can be used for the automatic and semi-automatic (human-in-the-loop) induction of grammar rules. SDS grammar rules and induction algorithms are grouped into two types, namely, low- and high-level. Two families of algorithms are investigated for rule induction: one based on semantic similarity and distributional semantic models, and the other using more traditional statistical modeling approaches (e.g., slot-filling algorithms using Conditional Random Fields). Evaluation results are presented for two domains and languages. High-level induction precision scores up to 60% are obtained. Results advocate the portability of the proposed features and algorithms across languages and domains.
AB - We investigate algorithms and tools for the semi-automatic authoring of grammars for spoken dialogue systems (SDS) proposing a framework that spans from corpora creation to grammar induction algorithms. A realistic human-in-the-loop approach is followed balancing automation and human intervention to optimize cost to performance ratio for grammar development. Web harvesting is the main approach investigated for eliciting spoken dialogue textual data, while crowdsourcing is also proposed as an alternative method. Several techniques are presented for constructing web queries and filtering the acquired corpora. We also investigate how the harvested corpora can be used for the automatic and semi-automatic (human-in-the-loop) induction of grammar rules. SDS grammar rules and induction algorithms are grouped into two types, namely, low- and high-level. Two families of algorithms are investigated for rule induction: one based on semantic similarity and distributional semantic models, and the other using more traditional statistical modeling approaches (e.g., slot-filling algorithms using Conditional Random Fields). Evaluation results are presented for two domains and languages. High-level induction precision scores up to 60% are obtained. Results advocate the portability of the proposed features and algorithms across languages and domains.
KW - Corpora creation
KW - Crowdsourcing
KW - Grammar induction
KW - Semantic similarity
KW - Spoken dialogue systems
KW - Web mining
UR - http://www.scopus.com/inward/record.url?scp=85029429337&partnerID=8YFLogxK
U2 - 10.1016/j.csl.2017.08.002
DO - 10.1016/j.csl.2017.08.002
M3 - Article
AN - SCOPUS:85029429337
SN - 0885-2308
VL - 47
SP - 272
EP - 297
JO - Computer Speech and Language
JF - Computer Speech and Language
ER -