Papers by Bhushan Kotnis

4 papers
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation (2022.acl-long)

Copied to clipboard

Challenge: Existing benchmarks for OIE are incomplete and do not include all acceptable variants of the same fact.
Approach: They introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.
Outcome: The proposed framework is based on fact synsets, clusters, and standardized benchmarks.
Attending to Future Tokens for Bidirectional Sequence Generation (D19-1)

Copied to clipboard

Challenge: Neural sequence generation is typically performed token-by-token and left-to-right.
Approach: They propose to use placeholder tokens to make the sequence generation process bidirectional by taking past and future tokens into consideration when generating the actual output token.
Outcome: The proposed approach outperforms baselines on two conversational tasks by a large margin.
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)

Copied to clipboard

Challenge: Current OpenIE systems extract all triple slots independently.
Approach: They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones .
Outcome: The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic.
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark (2022.acl-demo)

Copied to clipboard

Challenge: Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner.
Approach: They propose an interactive annotation platform that facilitates annotating complete facts from input sentences.
Outcome: The proposed platform facilitates such challenging annotation tasks and supports creation of fact-oriented OIE evaluation benchmarks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations