Papers with incremental processing
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. |
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023.emnlp-industry)
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| Challenge: | Key Point Analysis (KPA) extracts the main points from opinions and quantifies their prevalence. |
| Approach: | They propose a key point analysis framework that extracts the main points from opinions and quantifies their prevalence. |
| Outcome: | The proposed system is able to match sentences to key points over five datasets and demonstrate its performance. |
Dialogue is the Plan: From Interface to Joint Action in Agentic AI (2026.acl-short)
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| Challenge: | Large Language Model agents' language use is often used as an interface for instructing and reporting results. |
| Approach: | They argue that large language models are often used as an interface for instructingactions and reporting results. |
| Outcome: | We show that large-scale language models can be used to plan and act, yet their language is often used as an interface for instructing and reporting results. |
A Targeted Assessment of Incremental Processing in Neural Language Models and Humans (2021.acl-long)
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| Challenge: | Using by-word reaction time data, we compare incremental processing in humans and neural language models across a range of structural phenomena. |
| Approach: | They propose to scale up incremental processing in humans and language models by collecting by-word reaction time data for 16 different syntactic test suites. |
| Outcome: | The proposed model outputs match human and model accuracy scores, but underpredict the difference in magnitude of incremental processing difficulty between grammatical and ungrammatically-spaced sentences. |
TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model (2023.findings-acl)
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| Challenge: | Recent approaches for incremental processing use RNNs or Transformers, which consume whole sequences and are by nature non-incremental. |
| Approach: | They propose a two-pass model for AdaPtIve Revision to obtain an incremental supervision signal for learning an adaptive revision policy. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to restart-incremental Transformers while showing little degradation on full sequences. |