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Homepage2Vec: Language-Agnostic Website Embedding and Classification ...
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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Cognitive Network Topology and Optimization of the Mental Lexicon ...
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| 6 |
Linguistic effects on news headline success: Evidence from thousands of online field experiments (Registered Report Protocol)
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In: PLoS One (2021)
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On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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| 10 |
Crosslingual Document Embedding as Reduced-Rank Ridge Regression ...
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| 14 |
Causal Effects of Brevity on Style and Success in Social Media ...
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| 15 |
Message Distortion in Information Cascades
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In: http://infoscience.epfl.ch/record/270657 (2019)
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| 17 |
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted despite Making Serious Advances"
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In: http://infoscience.epfl.ch/record/271147 (2019)
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| 18 |
Why the World Reads Wikipedia: Beyond English Speakers
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In: http://infoscience.epfl.ch/record/270302 (2019)
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| 19 |
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
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In: http://infoscience.epfl.ch/record/263893 (2019)
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| 20 |
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media ...
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Abstract:
We propose a new method to detect when users express the intent to leave a service, also known as churn. While previous work focuses solely on social media, we show that this intent can be detected in chatbot conversations. As companies increasingly rely on chatbots they need an overview of potentially churny users. To this end, we crowdsource and publish a dataset of churn intent expressions in chatbot interactions in German and English. We show that classifiers trained on social media data can detect the same intent in the context of chatbots. We introduce a classification architecture that outperforms existing work on churn intent detection in social media. Moreover, we show that, using bilingual word embeddings, a system trained on combined English and German data outperforms monolingual approaches. As the only existing dataset is in English, we crowdsource and publish a novel dataset of German tweets. We thus underline the universal aspect of the problem, as examples of churn intent in English help us ... : 10 pages ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1808.08432
https://arxiv.org/abs/1808.08432
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