Papers with clustering analysis
Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls (2020.coling-main)
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| Challenge: | Despite efforts to adopt digital technologies, the success rate in improving business performance is very low due to the lack of a coherent digital strategy. |
| Approach: | They apply NLP models to earnings calls to understand different clusters of digital strategy patterns that companies are Adopting. |
| Outcome: | The proposed models show that Fortune 500 companies use four distinct strategies which are product-led, customer experience-led and service-led. |
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models (2026.acl-long)
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| Challenge: | Backchannels and fillers are important linguistic expressions in dialogue, but often ignored in modern transformer-based language models. |
| Approach: | They use clustering analysis to learn backchannels and fillers in dialogues in English and Japanese and use natural language generation metrics to confirm this. |
| Outcome: | The proposed models can learn representations of backchannels and fillers using three fine-tuning strategies. |
Dialog Intent Induction with Deep Multi-View Clustering (D19-1)
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| Challenge: | Existing work assumes that dialog intents are expressed in query utterances and captured in the rest of the dialog. |
| Approach: | They propose a dialog intent induction task and propose alternating-view k-means for clustering . they split a conversation into two independent views and exploit multi-view clustering techniques . |
| Outcome: | The proposed approach can induce better dialog intent clusters than state-of-the-art clustering methods. |