The document proposes a replicated Siamese LSTM model for semantic textual similarity (STS) and information retrieval (IR) in an industrial diagnostic ticketing system. The system aims to retrieve relevant solutions from a knowledge base of tickets given a query. However, the text pairs in the system are often asymmetric in length and content. The proposed model addresses this by learning complementary representations of text pairs in a highly structured latent space using a replicated Siamese LSTM architecture and multi-channel Manhattan metric. It aims to capture similarity at both coarse-grained topic and fine-grained semantic levels to better handle asymmetric texts. The model is evaluated on STS and IR tasks for the industrial ticketing system.

1. Intern © Siemens AG 2017
Replicated Siamese LSTM in Ticketing System
for Similarity Learning and Retrieval
in Asymmetric Texts
Author(s): Pankaj Gupta1,2, Bernt Andrassy2, Hinrich Schütze1
Presenter: Pankaj Gupta @COLING2018 Santa Fe, NM USA. 20th August 2018
1CIS, University of Munich (LMU) | 2Machine Intelligence, Siemens AG | August 2018

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Contributors
PANKAJ GUPTA
PhD Candidate @LMU
Research Scientist @Siemens
Munich Germany
Dr. Bernt Andrassy
Senior Key Expert @Siemens
Munich Germany
Prof. Hinrich Schütze
@University of Munich (LMU)
(PhD advisor)

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Outline
➢ Problem Statement
- Industrial Diagnostic Ticketing System
- Asymmetric Textual Similarity
- Complementary Semantics in Similarity
➢ Background
- Siamese Neural Networks (Siamese-LSTM) in NLP
- Neural Autoregressive Topic Model (DocNADE)
➢ Proposal
- The Replicated Siamese LSTM for STS and IR
- Multi-Channel Manhattan Metric for Complementary Semantics
➢ Evaluations: STS and IR

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Problem Statement: STS and IR
Semantic Textual Similarity (STS):
➢ Task to find out if the text pairs mean the same thing
➢ Information Retrieval (IR) and text understanding may be improved by
modeling the underlying semantic similarity between texts.

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB DESC SOL
…… …… ……
…… …… ……
…… …… ……
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected. Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should not run at load over 28 MW until resolved.
KB of Industrial Tickets
Intelligent Ticketing System (ITS)#TICKET

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB DESC SOL
…… …… ……
…… …… ……
…… …… ……
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected. Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should not run at load over 28 MW until resolved.
KB of Industrial Tickets
Intelligent Ticketing System (ITS)#TICKET
SUBJECT text
DESCRIPTION text
QUERY
Solution:
Recommended
Service Action text
Recommendation(s)

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
Retrieval
#TICKET
IN OUT
KB
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
#TICKET
IN OUT
KB
ITS
Retrieval

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
Retrieval
#TICKET
IN OUT
KB
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
#TICKET
IN OUT
KB
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
#TICKET
IN OUT
KB
cosine similarity
(low, narrow) = 0.26
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar.
SOL: The recommended action is that the machine should
not run at load over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
QUERY
similarity
#TICKET
IN OUT
KB
cosine similarity
(frequency, load) = 0.14
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
cosine similarity
(frequency, mbar) = 0.25
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
ITS

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Problem Statement: Industrial Diagnostic Ticketing System
Goal: Retrieve a relevant solution for an input query
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KBcosine similarity
(frequency, MW) = 0.08
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
ITS

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Problem Statement: Significant Term Mismatch
Goal: Retrieve a relevant solution for an input query
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB(Significant Term Mismatch)
SUB:
GT Trip - Low Frequency Pulsations
QUERY
ITS

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Problem Statement: Symmetric Textual Similarity
Goal: Retrieve a relevant solution for an input query
SUB: Narrow Frequency Pulsations
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Symmetric Text pairs
(short Vs short texts)
SUB:
GT Trip - Low Frequency Pulsations
QUERY
ITS

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Problem Statement: Symmetric Textual Similarity
Goal: Retrieve a relevant solution for an input query
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Symmetric Text pairs
(long Vs long texts)
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
ITS

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Problem Statement: Asymmetric Textual Similarity
Goal: Retrieve a relevant solution for an input query
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Asymmetric Text pairs
(short Vs multi-sentence texts)
SUB:
GT Trip - Low Frequency Pulsations
QUERY
ITS

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Problem Statement: Asymmetric Textual Similarity
Goal: Retrieve a relevant solution for an input query
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Asymmetric Text pairs
(short Vs multi-sentence texts)
SUB:
GT Trip - Low Frequency Pulsations
QUERY
ITS

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Complementary Semantic Learning and Similarity
Goal: Retrieve a relevant solution for an input query
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Complementary Semantics
(Coarse Vs fine Granularity)
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
ITS

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Complementary Semantic Learning and Similarity
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Complementary Semantics
(Coarse Vs fine Granularity)
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
similarity in latent topic semantics
ITS

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Complementary Semantic Learning and Similarity
SUB: Narrow Frequency Pulsations
DESC: Low and Narrow frequency pulsations were detected.
Peak value for the Low Frequency Pulsations is 60 mbar .
SOL: The recommended action is that the machine should
not run at loads over 28 MW until resolved.
The recommended action is that the
machine should not run at loads over
28 MW until resolved.
Recommendation(s)
similarity
#TICKET
IN OUT
KB
Learn Complementary
Text Pair Representations
in a Highly Structured Space
SUB:
GT Trip - Low Frequency Pulsations
DESC:
GT Tripped due to a sudden increase
in Low Frequency Pulsations.
Machine is restarted and now operating
normally. Alarm received was: GT ESD
Low Frequency Pulsation.
QUERY
similarity in latent topic semantics
ITS

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Background: Siamese Networks in NLP
Reference: Mueller and Thyagarajan, 2016
- dual-branch networks with tied weights
- non-linear metric learning with similarity information
- Invariant and selective representation directly through the
use of similarity and dissimilarity information

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Background: Siamese Networks in NLP
Reference: Mueller and Thyagarajan, 2016
Limited to
sentence pairs
- dual-branch networks with tied weights
- non-linear metric learning with similarity information
- Invariant and selective representation directly through the
use of similarity and dissimilarity information

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Background: Document Neural Autoregressive Topic Model (DocNADE)
Reference: Gupta et al. Document Informed Autoregressive Topic Models. Preprint: https://arxiv.org/pdf/1808.03793.pdf
- Learn word co-occureances across documents
- Extract latent topics (coarse grained representation)
- Optimise via reconstruction loss where, input should
be reproduced by the decoding part of the network.

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Query Component(s)
subscripted
by 1 e.g., X1
Ticket Component(s)
subscripted
by 2 e.g., X2

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Multi-channel for
Complementary Semantics !!!
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Multi-channel for
Complementary Semantics !!!
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Replicated !!!
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Multi-level Similarity !!!
e.g. SUB1-SUB2, DESC1-DESC2
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADEi.e. symmetric textual similarities

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Cross-level Similarity !!!
e.g. SUB1-DESC2, DESC1-SOL2, etc.
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE
i.e. asymmetric textual similarities

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Cross-level Similarity !!!
e.g. SUB1-DESC2, SUB1-SOL2, etc.
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE
i.e. asymmetric textual similarities

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Cross-level Similarity !!!
e.g. SUB1-DESC2, DESC1-SOL2, etc.
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Cross-level Similarity !!!
e.g. SUB1-DESC2, DESC1-SUB2, etc.
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE
i.e. asymmetric textual similarities

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Proposal: Multi-Channel, Replicated and Multi-Cross Level Siamese
Overall Learning !!!
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE

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Proposal: Multi-Channel Manhattan Metric with Complementary Semantics
Multi-Channel Manhattan Metric

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Proposal: Multi-Channel Manhattan Metric with Complementary Semantics
Weighted semantic distances
+ Weighted ‘level’ similarity
Multi-Channel Manhattan Metric

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Evaluation

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Evaluation
➢ Industrial Data Set
- 949 historical tickets in the KB
- 421 pairs labeled
- relatedness labels: YES (similar that provides correct solution): 5.0
REL (does not provide correct solution, but close to a solution): 3.0 and
NO (not related, not relevant and provides no correct solution): 1.0
- average length (#words) of SUB: (4.6); DESC: (65.0); SOL: (74.2).
➢ Baselines
- Unsupervised (topic and word embedding representations)
- Supervised (Siamese LSTM)
➢ Evaluation and Analysis the proposed Replicated Siamese LSTM

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Evaluation: LDA Vs DocNADE

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Evaluation: Unsupervised Baselines for STS and IR
SIMILARITY: Pearson correlation (r), Spearmans correlation coefficient (p), Mean Squared Error (MSE),
IR: Mean Average Precision@k (MAP@k) and Accuracy@k (Acc@k)
Topic (T) semantics from Query and Ticket components i.e. T(SUB1-SUB2), etc.

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Evaluation: Unsupervised Baselines for STS and IR
SIMILARITY: Pearson correlation (r), Spearmans correlation coefficient (p), Mean Squared Error (MSE),
IR: Mean Average Precision@k (MAP@k) and Accuracy@k (Acc@k)
Topic (T) semantics from Query and Ticket components i.e. T(SUB1-SUB2), etc.

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Evaluation: Unsupervised Baselines for STS and IR
Topic (T) semantics from Query and Ticket components i.e. T(SUB1-SUB2), etc.
SIMILARITY: Pearson correlation (r), Spearmans correlation coefficient (p), Mean Squared Error (MSE),
IR: Mean Average Precision@k (MAP@k) and Accuracy@k (Acc@k)

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Evaluation: Unsupervised Baselines for STS and IR
Topic (T) semantics from Query and Ticket components i.e. T(SUB1-SUB2), etc.
Distributional Semantics (SumEmbedding) (E) for Query and Ticket components i.e.
E(SUB1-SUB2), . E(SUB1+DESC1-SUB2+DESC2+SOL2), etc.

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Evaluation: Supervised Baselines for STS and IR
S-LSTM: Compute Similarity using Standard Siamese LSTM on a query and ticket components

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Evaluation: Supervised Baselines for STS and IR
ML-LSTM: Multi-level similarity

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Evaluation: Supervised Baselines for STS and IR
CL-LSTM: Cross-level similarity

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Evaluation: Supervised Baselines for STS and IR
Multi-channel semantic similarity

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Evaluation: Supervised Baselines for STS and IR
Multi-channel semantic similarity
7%

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Qualitative Inspections for STS and IR
topics

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Qualitative Inspections for STS and IR
topics

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Conclusion
➢ Advancement of Siamese architecture for similarity learning in
asymmetric text pairs, instead of sentence pairs only
➢ Proposed Replicated Siamese LSTM with multi/cross level similarity
➢ Gain of 22% and 7% (Acc@10), respectively over unsupervised and supervised baselines in IR
➢ Demonstrated complementary semantics improved similarity/retrieval performance
➢ Address/Improve the real-world industrial application of ticketing system

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Thanks !!!
W
W
W
W
W
DocNADE
DocNADE
DocNADE
DocNADE
DocNADE