Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts

Pankaj Gupta, Bernt Andrassy, Hinrich Schütze


Abstract
The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.
Anthology ID:
W18-4001
Volume:
Proceedings of the Third Workshop on Semantic Deep Learning
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editors:
Luis Espinosa Anke, Dagmar Gromann, Thierry Declerck
Venue:
SemDeep
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/W18-4001
DOI:
Bibkey:
Cite (ACL):
Pankaj Gupta, Bernt Andrassy, and Hinrich Schütze. 2018. Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts. In Proceedings of the Third Workshop on Semantic Deep Learning, pages 1–11, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts (Gupta et al., SemDeep 2018)
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PDF:
https://aclanthology.org/W18-4001.pdf