Dependency Parsing for Spoken Dialog Systems

Abstract

Dependency parsing of conversational input can play an important role in language understanding for dialog systems by identifying the relationships between entities extracted from user utterances. Additionally, effective dependency parsing can elucidate differences in language structure and usage for discourse analysis of human-human versus human-machine dialogs. However, models trained on datasets based on news articles and web data do not perform well on spoken human-machine dialog, and currently available annotation schemes do not adapt well to dialog data. Therefore, we propose the Spoken Conversation Universal Dependencies (SCUD) annotation scheme that extends the Universal Dependencies (UD) (Nivre et al., 2016) guidelines to spoken human-machine dialogs. We also provide ConvBank, a conversation dataset between humans and an opendomain conversational dialog system with SCUD annotation. Finally, to demonstrate the utility of the dataset, we train a dependency parser on the ConvBank dataset. We demonstrate that by pre-training a dependency parser on a set of larger public datasets and finetuning on ConvBank data, we achieved the best result, 85.05% unlabeled and 77.82% labeled attachment accuracy.

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

“…We are not the first to annotate spoken data. Previous work has annotated English for conversation agents (Davidson et al, 2019), Slovenian data (Dobrovoljc and Martinc, 2018), Komi-Zyrian (Partanen et al, 2018) and Turkish-German (C ¸etinoglu and C ¸öltekin, 2019).…”

“…Commonly mentioned problems are disfluencies and sentence segmentation (Dobrovoljc and Martinc, 2018). Two main types of solutions can be identified; adapting the existing guidelines (C ¸etinoglu and C ¸öltekin, 2019) versus extending them (Davidson et al, 2019).…”

A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions

“…From theoretical aspects, various dialogue structures have been studied, including discourse structure (Stent, 2000;Asher et al, 2003), speech act (Austin, 1962;Searle, 1969) and common grounding (Clark, 1996;Lascarides and Asher, 2009). In dialogue system engineering, various linguistic structures have been considered and applied, including syntactic dependency (Davidson et al, 2019), predicate-argument structure (PAS) (Yoshino et al, 2011), ellipsis (Quan et al, 2019;Hansen and Søgaard, 2020), intent recognition (Silva et al, 2011;Shi et al, 2016), semantic representation/parsing (Mesnil et al, 2013;Gupta et al, 2018) and frame-based dialogue state tracking (Williams et al, 2016;El Asri et al, 2017). However, most prior work focus on dialogues where information is not grounded in external, perceptual modality such as vision.…”

A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions

“…From theoretical aspects, various dialogue structures have been studied, including discourse structure (Stent, 2000;Asher et al, 2003), speech act (Austin, 1962;Searle, 1969) and common grounding (Clark, 1996;Lascarides and Asher, 2009). In dialogue system engineering, various linguistic structures have been considered and applied, including syntactic dependency (Davidson et al, 2019), predicate-argument structure (PAS) (Yoshino et al, 2011), ellipsis (Quan et al, 2019;Hansen and Søgaard, 2020), intent recognition (Silva et al, 2011;Shi et al, 2016), semantic representation/parsing (Mesnil et al, 2013;Gupta et al, 2018) and frame-based dialogue state tracking (Williams et al, 2016;El Asri et al, 2017). However, most prior work focus on dialogues where information is not grounded in external, perceptual modality such as vision.…”