Papers by Wen-mei Hwu
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships (2022.emnlp-main)
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| Challenge: | Existing knowledge graphs lack two desired features for modeling entity relationships: openness and informativeness. |
| Approach: | They propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions using a transformer-based relation description synthesizing model. |
| Outcome: | The proposed system extracts and generates high-quality relation descriptions without human labeling. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Understanding Jargon: Combining Extraction and Generation for Definition Modeling (2022.emnlp-main)
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| Challenge: | Existing methods for generating definitions of words/phrases perform poorly on jargon. |
| Approach: | They propose to combine extraction and generation for jargon definition modeling by extracting definitional information from the Web and incorporating extracted definitional data. |
| Outcome: | The proposed method outperforms state-of-the-art models significantly on jargon definitions. |
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach (2021.acl-long)
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| Challenge: | Existing methods to measure fine-grained domain relevance are needed for downstream tasks in natural language processing. |
| Approach: | They propose to measure fine-grained domain relevance, defined as the degree that a term is relevant to a given domain. |
| Outcome: | The proposed method outperforms baselines and surpasses professional human performance. |
Open Relation Modeling: Learning to Define Relations between Entities (2022.findings-acl)
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| Challenge: | Existing systems identify related entities but do not provide features for exploring relations between entities. |
| Approach: | They propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities. |
| Outcome: | The proposed model can generate definition-like relation descriptions that capture the representative characteristics of entities. |
Exploring Semantic Capacity of Terms (2020.emnlp-main)
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| Challenge: | Existing models that measure semantic capacity of terms are not all considered equal . a good command of semantic capacity will give us more insight into the granularity of terms . |
| Approach: | They propose a model that evaluates semantic capacity of terms if text corpus can provide enough co-occurrence information of terms. |
| Outcome: | The proposed model can evaluate semantic capacity of terms if the corpus can provide enough co-occurrence information of terms. |
Can Language Models Be Specific? How? (2023.findings-acl)
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| Challenge: | Existing pre-trained language models have a preference for more specific answers . however, there may exist multiple answers for a query, while not all answers are equally specific. |
| Approach: | They propose to build a benchmark for specificity testing by forming masked token prediction tasks with prompts. |
| Outcome: | The proposed methods improve the specificity of pre-trained language models without additional training. |