Nitesh Chawla


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Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases
Chuxu Zhang | Lu Yu | Mandana Saebi | Meng Jiang | Nitesh Chawla
Findings of the Association for Computational Linguistics: EMNLP 2020

Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths. The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over few-shot relations (with limited triples) which are common in knowledge base. To this end, we propose FIRE, a novel few-shot multi-hop relation learning model. FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning. The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations. Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.


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Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text
Tianwen Jiang | Tong Zhao | Bing Qin | Ting Liu | Nitesh Chawla | Meng Jiang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Condition is essential in scientific statement. Without the conditions (e.g., equipment, environment) that were precisely specified, facts (e.g., observations) in the statements may no longer be valid. Existing ScienceIE methods, which aim at extracting factual tuples from scientific text, do not consider the conditions. In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences. The framework has (1) a multi-output module to generate one or multiple tuples and (2) a multi-input module to feed in multiple types of signals as sequences. It improves F1 score relatively by 4.2% on BioNLP2013 and by 6.2% on a new bio-text dataset for tuple extraction.