Papers by Maneesh Singh
DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)
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| Challenge: | Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents. |
| Approach: | They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer. |
| Outcome: | The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm. |
HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (2020.findings-emnlp)
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| Challenge: | a dataset for many-hop evidence extraction and fact verification challenges models to reason with information from multiple Wikipedia articles. |
| Approach: | They propose a dataset for many-hop evidence extraction and fact verification . they challenge models to extract facts from Wikipedia articles relevant to a claim . |
| Outcome: | The proposed dataset shows that state-of-the-art models degrade as the number of reasoning hops increases. |
Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning (2022.tacl-1)
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| Challenge: | Existing models that claim to reason about evidence should avoid spurious patterns . tabular inputs are well-suited for the study—they admit systematic probes . |
| Approach: | They propose to use tabular data to test whether models can reason about evidence . they show that a RoBERTa-based model fails to reason on the following counts . |
| Outcome: | The proposed model fails to reason on tabular data on the following counts . the model is over-sensitive to annotation artifacts and ignores relevant parts of the evidence . |
Sampling Bias in Deep Active Classification: An Empirical Study (D19-1)
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| Challenge: | Existing studies on active learning identify sampling bias in large datasets . cost and time needed for labeling and model training are bottlenecks preventing new and/or better models from being trained . |
| Approach: | They propose to use active learning to identify representative data samples for training . they propose to create tiny datasets that can be used for cheap training if needed . |
| Outcome: | The proposed model outperforms the state-of-the-art on active text classification using small representative datasets with active learning. |