Papers with Snips
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (N19-3)
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| Challenge: | Neural networks are used to understand spoken language understanding (SLU) but it is difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. |
| Approach: | They propose to use a recurrent neural network to nois slots for data augmentation by using an attention-based bi-directional recurrence neural network. |
| Outcome: | The proposed method achieves performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score) and 0.53% accuracy. |
Multi-Task Networks with Universe, Group, and Task Feature Learning (P19-1)
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| Challenge: | In multi-task learning, multiple related tasks are learned together. |
| Approach: | They propose methods that take advantage of natural groupings of related tasks . they propose parallel and serial architectures that can learn different feature spaces . |
| Outcome: | The proposed methods improve performance on natural language understanding (NLU) tasks. |
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow (2020.emnlp-main)
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| Challenge: | Existing datasets focus on relation between procedural events, but little attention has been paid to relation between events. |
| Approach: | They propose a set of reasoning tasks targeting goal-step relations and step-step temporal relations based on wikiHow articles . their automatically-generated training set allows models to transfer to out-of-domain tasks requiring knowledge of procedural events . |
| Outcome: | The proposed dataset improves on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings. |
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling (2025.acl-long)
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| Challenge: | Recent LLMs are known to hallucinate, producing responses that seem plausible but are factually incorrect. |
| Approach: | They propose an accountability model for LLM-based task-oriented dialogue agents to address user overreliance via friction turns in cases of model uncertainty and errors associated with dialogue state tracking (DST). |
| Outcome: | The proposed model improves joint goal accuracy (JGA) of DST output by 3% on two established benchmarks. |