Papers by Bhavana Dalvi
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension (N18-1)
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| Challenge: | Using synthetic data, existing models struggle with questions that require inference. |
| Approach: | They propose a dataset and two new neural models that exploit alternative mechanisms for state prediction. |
| Outcome: | The proposed dataset improves accuracy by 19% over previous models. |
A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
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Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
What-if I ask you to explain: Explaining the effects of perturbations in procedural text (2020.findings-emnlp)
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| Challenge: | QUARTET constructs explanations from paragraphs using procedural text . qartet achieves 18 points better on explanation accuracy compared to strong baselines on a recent process comprehension benchmark. |
| Approach: | They propose a system that constructs explanations from paragraphs by modeling the explanation task as a multitask learning problem. |
| Outcome: | The proposed system achieves 18 points better on explanation accuracy compared to strong baselines on a process comprehension benchmark. |
Explaining Answers with Entailment Trees (2021.emnlp-main)
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Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, Peter Clark
| Challenge: | ENTAILMENTBANK is the first dataset to contain multistep entailment trees. |
| Approach: | They propose to generate explanations in the form of entailment trees, a tree of multipremise entanglements steps from facts that are known to the hypothesis of interest. |
| Outcome: | The proposed model can generate explanations in the form of entailment trees . this is a tree of multipremise enttailment steps from facts known to the hypothesis of interest. |
Be Consistent! Improving Procedural Text Comprehension using Label Consistency (N19-1)
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| Challenge: | Existing systems for procedural text comprehension still struggle with this task . evaluative work shows that consistent predictions from multiple entities can improve performance . |
| Approach: | They propose a framework that leverages label consistency during training to improve prediction performance. |
| Outcome: | The proposed framework significantly improves prediction performance over previous state-of-the-art systems on a standard benchmark dataset for procedural text, ProPara. |
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications (N18-1)
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Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
| Challenge: | a dataset of 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR is presented to study peer reviews. |
| Approach: | They propose to use the dataset to collect peer reviews from top-tier venues including ACL, NIPS and ICLR and to use it to create a dataset of peer reviews for research purposes. |
| Outcome: | The proposed dataset includes 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. |
DREAM: Improving Situational QA by First Elaborating the Situation (2022.naacl-main)
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| Challenge: | Cognitive science has long promoted the formation of mental models as central to understanding and question-answering. |
| Approach: | They train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about and then provide those elaborations as additional context to a question-answering (QA) model. |
| Outcome: | The proposed model is able to create better scene elaborations than a representative state-of-the-art, zero-shot model. |
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge (D18-1)
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| Challenge: | Recent work has shown impressive progress in comprehending procedural text, but their predictions can be inconsistent or highly improbable. |
| Approach: | They propose to incorporate global constraints and bias reading with corpora-based preferences to improve the predicted effects of actions in a paragraph. |
| Outcome: | The proposed model significantly outperforms earlier models on a benchmark dataset for procedural text comprehension (+8% relative gain) it avoids nonsensical predictions that earlier models make, and it is more robust than previous models. |
WIQA: A dataset for “What if...” reasoning over procedural text (D19-1)
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| Challenge: | a dataset of “What if...” questions is available for procedural text comprehension . we present the dataset as an open challenge to the community . |
| Approach: | They propose a dataset of “What if...” questions over procedural text . they use paragraphs annotated with multiple influence graphs to create the questions . |
| Outcome: | The proposed dataset achieves 73.8% accuracy, well below the human performance of 96.3%. |
ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language (2021.findings-acl)
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| Challenge: | Recent work shows that transformers can generate both implications of a theory and the natural language proofs that support them. |
| Approach: | They propose a generative model that generates both implications of a theory and natural language proofs that support them. |
| Outcome: | The proposed model generates both implications of a theory and the natural language proofs that support them. |
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text (D19-1)
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| Challenge: | XPAD is a new model that predicts actions' effects and their dependencies based on background knowledge . previous work on extracting sequences of actions from text has focused on identifying why they are the way they are . |
| Approach: | They propose a new model that biases effect predictions towards those that explain more of the actions in the paragraph and are more plausible with respect to background knowledge. |
| Outcome: | The proposed model outperforms existing systems on explaining actions by predicting dependencies while maintaining the performance on the original task in ProPara. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |