Papers by Keshav Kolluru

6 papers
mOKB6: A Multilingual Open Knowledge Base Completion Benchmark (2023.acl-short)

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Challenge: Open knowledge bases (Open KBCs) are constructed from triples of the form, which can be denoted as (s, r, o) by using open information extraction (Open IE) systems.
Approach: They construct a dataset with facts from Wikipedia in six languages . they use open information extraction systems to extract triples from text .
Outcome: The proposed dataset contains facts from Wikipedia in six languages including English . it improves existing Open KB construction pipeline by doing multilingual coreference resolution and keeping only entity-linked triples .
Why and when should you pool? Analyzing Pooling in Recurrent Architectures (2020.findings-emnlp)

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Challenge: Various pooling techniques have been shown to improve performance of RNNs on text classification tasks.
Approach: They propose a pooling-based variant that captures interactions among predictive tokens in a sentence.
Outcome: The proposed pooling architecture outperforms non-pooling models on sequence classification tasks.
“Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds (2022.emnlp-main)

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Challenge: Proper noun compounds are used in short-form domains but are largely ignored in information-seeking applications.
Approach: They propose to annotate a manually annotated dataset of 22.5K proper noun compounds . they use supervised learning to generate interpretations from the compounds based on target knowledge .
Outcome: The proposed dataset is 60 times larger than prior noun compound datasets and includes non-compositional examples.
Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction (2022.acl-long)

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Challenge: Existing training data in English is limited to supervised Open Information Extraction (OpenIE) .
Approach: They propose a model that automatically converts English sentences into other languages by using Alignment-Augmented Constrained Translation (AACTrans) they train a generative OpenIE model that outputs for each sentence relations in the first stage and all extractions containing the relation in the second stage.
Outcome: The proposed model outperforms existing models on Spanish, Portuguese, Chinese, Hindi and Telugu on 5 languages.
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction (2020.emnlp-main)

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Challenge: OpenIE generates extractions iteratively, requiring repeated encoding of partial outputs.
Approach: They propose an iterative open information extraction system that generates extractions iterativly, requiring repeated encoding of partial outputs.
Outcome: The proposed system beats the previous systems by as much as 4 pts in F1 while being much faster.
IMoJIE: Iterative Memory-Based Joint Open Information Extraction (2020.acl-main)

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Challenge: Recent neural OpenIE systems are statistical or rule-based for Open Information Extraction.
Approach: They propose an extension to CopyAttention that produces the next extraction conditioned on all previously extracted tuples.
Outcome: The proposed model outperforms CopyAttention by 18 pts and a BERT-based strong baseline by 2 ptes.

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