Papers with modifier
DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion (L18-1)
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| Challenge: | a text fragment is discarded when it has a smaller context, causing it to acquire a new meaning or even become false. |
| Approach: | They build a dataset to study the effect of modifiers on the larger context . they focus on single-word modifiers, the smallest unit that can be considered disposable . |
| Outcome: | The proposed dataset aims to determine whether modifiers can be removed without undesirable consequences. |
MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions. |
| Approach: | They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference. |
| Outcome: | The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset. |
Connecting degree and polarity: An artificial language learning study (2023.emnlp-main)
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| Challenge: | Existing studies have shown that degree modifiers are related to sentence polarity, but they are not related to the grammatical number of an expression. |
| Approach: | They propose to generalize degree modifiers to their polarity sensitivity in pre-trained language models by applying the Artificial Language Learning experimental paradigm from psycholinguistics to a neural language model. |
| Outcome: | The proposed generalisations are consistent with existing linguistic observations that relate de-gree semantics to polarity sensitivity, including the main one: low degree semantics is associated with preference towards positive polarities. |
Enhanced Noun-Noun Compound Interpretation through Textual Enrichment (2025.emnlp-main)
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| Challenge: | Recent benchmarks frame Noun-Noun Compound Interpretation as a multiple-choice question . but, it still faces key limitations: vague relation descriptions as options and inability to handle polysemous compounds. |
| Approach: | They propose a textual enrichment framework that parses relations into eventoriented descriptions . the framework explicitly surfaces the hidden event connecting head and modifier . |
| Outcome: | The proposed framework yields consistently higher accuracy across three LLM families. |