Papers by Nikola I. Nikolov

3 papers
Embedding-based Scientific Literature Discovery in a Text Editor Application (2020.acl-demos)

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Challenge: Despite the availability of powerful search engines and text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load.
Approach: They propose to combine text editing and literature discovery in an interactive user interface with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings.
Outcome: The proposed application combines text editing and literature discovery in an interactive user interface.
Abstractive Document Summarization without Parallel Data (2020.lrec-1)

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Challenge: Abstractive summarization typically relies on large collections of paired articles and summaries.
Approach: They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences .
Outcome: The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article.
Character-Level Translation with Self-attention (2020.acl-main)

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Challenge: Existing models for character-level neural machine translation operate on word-level, which makes them memory inefficient because of large vocabulary sizes.
Approach: They propose a transformer-based model and a novel variant that uses convolutions to combine information from nearby characters to facilitate character interactions.
Outcome: The proposed model outperforms the standard transformer model and learns more robust character alignments on bilingual and multilingual translation datasets.

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