CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation (P18-4)
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| Challenge: | Existing systems require developers to manually generate and annotate a large number of utterances. |
| Approach: | They propose a system that guides ordinary software developers to build a high quality NLU engine from scratch. |
| Outcome: | The proposed system shows that iterative pruning of incorrect utterances reduces human workload and cognitive load. |
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| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
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Chasing the Tail with Domain Generalization: A Case Study on Frequency-Enriched Datasets (2022.aacl-main)
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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. |
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A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation (2022.findings-naacl)
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
| Challenge: | Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning. |
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Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
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| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
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| Challenge: | NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design . |
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Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback (2023.acl-long)
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| Challenge: | Frozen models trained to mimic static datasets can never improve their performance. |
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SkillBot: Towards Automatic Skill Development via User Demonstration (N19-4)
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| Challenge: | Existing industrial PA products require software developers to build new skills via IDE tools. |
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From Understanding to Generation: An Efficient Shortcut for Evaluating Language Models (2025.emnlp-main)
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| Challenge: | Iterative evaluation of large language models during training can be time- and compute-intensive. |
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Large-Scale Transfer Learning for Natural Language Generation (P19-1)
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Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf
| Challenge: | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. |
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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)
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| Challenge: | Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment. |
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