Papers by Charles Yang
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
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| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (2022.findings-acl)
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| Challenge: | a low-resource task-oriented semantic parser is limited by privacy requirements for unlabeled natural utterances. |
| Approach: | They propose a setup for low-resource task-oriented semantic parsing based on user interactions . they use structured canonical utterances, then simulating corresponding natural language to improve performance. |
| Outcome: | The proposed setup improves on a low-resource task-oriented semantic parser using utterances collected through user interactions. |
Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)
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Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Jankowski, Yanghua Xiao, Deqing Yang
| Challenge: | Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied. |
| Approach: | They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset. |
| Outcome: | The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs. |
Unsupervised Morphology Learning with Statistical Paradigms (C18-1)
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| Challenge: | Existing models treat words as concatenation of morphemes, but some use transformations like rewrite rules to recognize dependencies between morphs. |
| Approach: | They propose an unsupervised model that exploits the notion of paradigms for morphological segmentation that can be applied to a homogeneous set of words. |
| Outcome: | The proposed model significantly improves on the Morpho-Challenge dataset in English, Turkish, and Finnish. |