Papers by Ilya Shnayderman
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks (P18-2)
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| Challenge: | In this paper, we use Wikipedia articles to learn thematic similarity metric between sentences. |
| Approach: | They propose to leverage the partition of articles into sections to learn thematic similarity metric between sentences. |
| Outcome: | The proposed model outperforms state-of-the-art embeddings on the task of thematic clustering of sentences. |
Zero-shot Topical Text Classification with LLMs - an Experimental Study (2023.findings-emnlp)
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Shai Gretz, Alon Halfon, Ilya Shnayderman, Orith Toledo-Ronen, Artem Spector, Lena Dankin, Yannis Katsis, Ofir Arviv, Yoav Katz, Noam Slonim, Liat Ein-Dor
| Challenge: | Topical text classification is an ancient, yet timely research area in natural language processing. |
| Approach: | They compare the zero-shot performance of a variety of LMs over a large dataset of 23 publicly available TTC datasets. |
| Outcome: | The proposed models outperform their counterparts over a large dataset and show that they perform better in a zero-shot scenario. |
Quality Controlled Paraphrase Generation (2022.acl-long)
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| Challenge: | Recent studies have shown that high quality paraphrases are difficult to generate because of their low flexibility and scalability. |
| Approach: | They propose a quality-guided controlled paraphrase generation model that allows directly controlling the quality dimensions of the generated paraphrase. |
| Outcome: | The proposed method generates paraphrases which maintain original meaning while achieving higher diversity than the uncontrolled baseline. |
Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
| Challenge: | Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited. |
| Approach: | They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data. |
| Outcome: | The proposed approach obtains high correlation with human labels in the absence of labeled data. |