Papers with TEN
Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales (2025.findings-naacl)
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| Challenge: | Existing studies focus only on textual quality and numerical accuracy for headline generation. |
| Approach: | They propose a framework for using rationales of key elements of Topic, Entities, and Numerical reasoning in news articles to enhance LLMs' ability to generate topic-aligned texts with precise numerical accuracy. |
| Outcome: | The proposed framework improves the ability of large language models to generate high-quality texts with precise numerical accuracy. |
TEN: Table Explicitization, Neurosymbolically (2026.acl-industry)
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| Challenge: | Existing methods for extracting tabular data from semistructured text are error-prone and costly. |
| Approach: | They propose a neurosymbolic approach to extract tabular data from semistructured text . TEN is a triadic feedback loop that iteratively refines table hypotheses . |
| Outcome: | The proposed approach outperforms neural baselines in exact match accuracy and lower hallucination rates. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |