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. |
| Outcome: | The proposed model significantly improves the state-of-the-art model for contextual understanding performance on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset. |
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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. |
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| Challenge: | Spoken Language Understanding models are usually trained offline on historical data, but must perform well on incoming user requests after deployment. |
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| Challenge: | Existing studies on SLU systems have focused on integrating syntactic information into language models. |
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| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
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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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| Challenge: | Pretrained language models are trained on corpora derived from the web, but ignore this information. |
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| Challenge: | This tutorial provides an overview of cutting edge research on spatial and temporal language understanding. |
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Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
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Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information (2025.acl-long)
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| Challenge: | Temporal Heads are attention heads that primarily handle temporal knowledge. |
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