.png){kind=icon}
.png){kind=icon}
Return
End-to-end response selection based on multi-level context response matching
.png){kind=icon}
OA
AI
DOI：10.1016/j.csl.2020.101080.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents our work on the Dialog System Technology Challenges 7 (DSTC7). We took part in Track 1 on sentence selection which evaluates response retrieving in dialog systems on more realistic test scenarios compared to the state-of-the-art evaluations. Our proposed dialog system matches the context with the best response by computing their semantic similarity on word and sequence levels. Evaluation results on the datasets provided show the effectiveness of our system by achieving higher performance compared to the provided baseline system. Our system enjoys the advantages of its simple and end-to-end architecture making its training and adaptation to other domains easier. (c) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Retrieval systems
Chatbots
Neural networks
Goal-oriented dialogue systems
DSTC
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.