Papers with spoken language understanding systems

3 papers
Incremental processing of noisy user utterances in the spoken language understanding task (D19-55)

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Challenge: triggered actions with high executions times can cause dialog systems to react slowly due to high latency and high latex.
Approach: They propose a model-agnostic method to achieve high quality in processing incrementally produced partial utterances.
Outcome: The proposed method improves the metric F1-score by 47.91 percentage points . the proposed method can be used to create low-latency natural language understanding components on ATIS datasets.
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents (C18-1)

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Challenge: Existing methods for DA annotation are incompatible with each other and do not cover all aspects necessary for open-domain human-machine interaction.
Approach: They propose to map publicly available corpora to a subset of the ISO standard and create a task-independent training corpus for DA classification.
Outcome: The proposed method can train a domain-independent DA tagger on out-of-domain conversational data and achieve robustness across different DA categories.
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)

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Challenge: despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal.
Approach: They propose a regularization approach to align word-level and sentence-level representations across languages without external resources.
Outcome: The proposed model outperforms state-of-the-art models in few-shot and zero-shot scenarios and achieves comparable performance to supervised training with all training data.

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