Proceedings of the 2019 Conference of the North 2019
DOI: 10.18653/v1/n19-2013
|View full text |Cite
|
Sign up to set email alerts
|

Scaling Multi-Domain Dialogue State Tracking via Query Reformulation

Abstract: We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant to the conversation across turns. Tracking conversational state is particularly challenging in a multi-domain scenario when there exist multiple spoken language understanding (SLU) sub-systems, and each SLU sub-system operates on its domainspecific meaning repr… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
37
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
3
3
1

Relationship

0
7

Authors

Journals

citations
Cited by 40 publications
(37 citation statements)
references
References 22 publications
0
37
0
Order By: Relevance
“…An interesting avenue for future work is to incorporate deeper context, either from other modalities (Das et al, 2017) or from other dialog comprehension tasks . Parallel to our work, Rastogi et al (2019) and Su et al (2019) introduce utterance rewriting datasets for dialog state tracking. Rastogi et al (2019) covers a narrow set of domains and the rewrites of Su et al (2019) are based on Chinese dialog with two-turn fixed histories.…”
Section: Related Work and Discussionmentioning
confidence: 99%
“…An interesting avenue for future work is to incorporate deeper context, either from other modalities (Das et al, 2017) or from other dialog comprehension tasks . Parallel to our work, Rastogi et al (2019) and Su et al (2019) introduce utterance rewriting datasets for dialog state tracking. Rastogi et al (2019) covers a narrow set of domains and the rewrites of Su et al (2019) are based on Chinese dialog with two-turn fixed histories.…”
Section: Related Work and Discussionmentioning
confidence: 99%
“…Note that high BLEU score does not guarantee high accuracy. NLG metrics like BLEU is common for this kind of reformulation [7,8], where valid queries have significant overlaps with ground truth (more concise and less diverse than translations or answers). However, BLEU is still insufficient since improper reformulations may also resemble ground truth.…”
Section: Additional Analysis On Action Generationmentioning
confidence: 99%
“…Conversation query understanding (CQU) is crucial to multi-turn question answering (QA), but remains challenging owing to diversity of queries (see Table 1). For Conversational AI, much work has been proposed for language understanding (LU) [6] and dialogue state tracking [7] in task-oriented dialogue system (DS). However, in multi-turn QA, user intents and conversational phenomena are more vague and complex (e.g., clarifications, coreferences and ellipsis), but there is no general LU solution as in DS pipelines.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Another topic our method related to is query reformulation. The idea of query reformulation is explored by Ray et al (2018) and Rastogi et al (2019), while they apply this idea in other domains with different scenarios. Our work is also related to semantic parsing, the process of converting natural language utterances into logical forms.…”
Section: Related Workmentioning
confidence: 99%