Papers by Pegah Nokhiz
Unbiasing Review Ratings with Tendency Based Collaborative Filtering (2020.aacl-srw)
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| Challenge: | Existing models for rating prediction neglect rating bias due to personal user bias preferences. |
| Approach: | They propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings. |
| Outcome: | The proposed model minimizes the user and item tendency for score prediction along with text review analysis with respect to ratings. |
SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
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| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
| Approach: | They constructed a scientific summarization dataset from MEDLINE articles from the PubMed archive to address this problem. |
| Outcome: | The proposed model outperforms existing models on news article summarization datasets and shows that it is more efficient to extract the top few lines. |
INFOTABS: Inference on Tables as Semi-structured Data (2020.acl-main)
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| Challenge: | Existing models for text understanding lack human-parity across a wide array of reasoning skills. |
| Approach: | They propose an extension of the natural language inference task to include semi-structured tabulated text . they propose a semi-structural, multi-domain and heterogeneous nature of the premises that are tables extracted from Wikipedia info-boxes. |
| Outcome: | The proposed model outperforms baseline models on the GLUE benchmark suite. |