Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
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Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
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Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
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Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization (2021.emnlp-main)
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Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)
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| Challenge: | End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations. |
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Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization (2020.emnlp-main)
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| Challenge: | Existing studies on text summarization focus on single-speaker docs, scientific publications and encyclopedia articles. |
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