Papers by Laure Thompson
Authorless Topic Models: Biasing Models Away from Known Structure (C18-1)
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| Challenge: | a recent study shows that topic models that highlight differences in authors are often not accurate . authors show that subsampling words that are highly correlated with metadata can reduce topic-metadata correlation . |
| Approach: | They propose three metrics for identifying topics that are highly correlated with metadata . they find that subsampling words causes topic-metadata correlation, improve topic stability . authors propose to use topic models to infer word distributions that correspond to recognizable themes . |
| Outcome: | The proposed model can predict which words cause the phenomenon and improve topic stability and quality. |
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration (2021.emnlp-main)
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| Challenge: | Phrase representations derived from pretrained language models often lack lexical similarity to determine semantic relatedness. |
| Approach: | They propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings by fine- tuning a dataset of diverse phrasal paraphrases and a large-scale dataset of phrases in context. |
| Outcome: | The proposed model outperforms baseline models across phrase-level similarity tasks while also showing increased lexical diversity between nearest neighbors in the vector space. |
Modeling Exemplification in Long-form Question Answering via Retrieval (2022.naacl-main)
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| Challenge: | Exemplification is a process by which writers explain or clarify a concept by providing an example. |
| Approach: | They propose to use a partially-written answer to query a large set of human-written examples extracted from a corpus to determine exemplification quality. |
| Outcome: | The proposed model is able to retrieve human-written examples from a corpus and show that it is more relevant than state-of-the-art models. |