| Challenge: | Spoken Language Understanding models are usually trained offline on historical data, but must perform well on incoming user requests after deployment. |
| Approach: | They propose different strategies for achieving good temporal generalization . they focus on temporal drift, where the distribution of utterances may change . |
| Outcome: | The proposed model can perform well on unseen domains, e.g., upcoming data. |
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| Challenge: | In academic research, natural language understanding tasks are typically defined by creating annotated datasets in which each utterance is encountered once. |
| Approach: | They propose a method that explicitly uses utterance frequency in training data to learn models that are more robust to unknown distributions. |
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Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025.naacl-long)
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
| Approach: | They introduce a new evaluation framework that employs fresh text and event prediction for assessing LLMs’ temporal adaptability. |
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SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks (2023.acl-long)
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Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding (SLU) tasks have received little attention and resources compared to lower-level tasks like speech and speaker recognition. |
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Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
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On the Evaluation of Speech Foundation Models for Spoken Language Understanding (2024.findings-acl)
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Siddhant Arora, Ankita Pasad, Chung-Ming Chien, Jionghao Han, Roshan Sharma, Jee-weon Jung, Hira Dhamyal, William Chen, Suwon Shon, Hung-yi Lee, Karen Livescu, Shinji Watanabe
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How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues (N18-1)
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| Challenge: | Spoken language understanding (SLU) is an essential component in conversational systems. |
| Approach: | They propose a universal time-decay attention mechanism that can be used to decay utterances on the sentence-level and speaker-level. |
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The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (2023.findings-emnlp)
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| Challenge: | Modern artificial intelligence is characterized by large pretrained language models with strong language capabilities to be adapted to various downstream tasks. |
| Approach: | They propose to use the task of speech translation (ST) to pretrain speech models for end-to-end SLU on intra- and cross-lingual scenarios. |
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Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding (N19-1)
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| Challenge: | Existing models that use contextual information of dialogues to improve spoken language understanding (SLU) select the wrong history when the histories are similar in content. |
| Approach: | They propose time-aware models that automatically learn the latent time-decay function of the history without a manual time- decay. |
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Time-Aware Language Models as Temporal Knowledge Bases (2022.tacl-1)
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Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen
| Challenge: | Existing language models are trained on snapshots of data collected at a specific moment in time. |
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Are Large Language Model Temporally Grounded? (2024.naacl-long)
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| Challenge: | Recent large language models lack a consistent temporal model of textual narratives . sentence ordering in unlabelled texts is only weakly correlated with event ordering . |
| Approach: | They evaluate LLMs with textual narratives and evaluate their common-sense knowledge . they find that LLM models struggle the most with self-consistency . |
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