Papers with generative models fine-tuned

2 papers
An End-to-End Dialogue Summarization System for Sales Calls (2022.naacl-industry)

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Challenge: Summarizing sales calls is a routine task performed manually by salespeople.
Approach: They propose a production system which combines generative models fine-tuned for customer-agent setting, with a human-in-the-loop user experience for an interactive summary curation process.
Outcome: The proposed system can handle training data scarcity and privacy constraints in an industrial setting.
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2025.naacl-long)

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Challenge: Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area . current code-switching methods suffer from term boundary detection issues and out-of-dictionary problems.
Approach: They propose a test-time code-switching framework which bridges the gap between bilingual training and monolingual test- time prediction.
Outcome: The proposed framework achieves an average improvement of 3.7% on four cross-lingual datasets.

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