Dinesh Manocha


2022

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DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection
Puneet Mathur | Gautam Kunapuli | Riyaz Bhat | Manish Shrivastava | Dinesh Manocha | Maneesh Singh
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We present DocInfer - a novel, end-to-end Document-level Natural Language Inference model that builds a hierarchical document graph enriched through inter-sentence relations (topical, entity-based, concept-based), performs paragraph pruning using the novel SubGraph Pooling layer, followed by optimal evidence selection based on REINFORCE algorithm to identify the most important context sentences for a given hypothesis. Our evidence selection mechanism allows it to transcend the input length limitation of modern BERT-like Transformer models while presenting the entire evidence together for inferential reasoning. We show this is an important property needed to reason on large documents where the evidence may be fragmented and located arbitrarily far from each other. Extensive experiments on popular corpora - DocNLI, ContractNLI, and ConTRoL datasets, and our new proposed dataset called CaseHoldNLI on the task of legal judicial reasoning, demonstrate significant performance gains of 8-12% over SOTA methods. Our ablation studies validate the impact of our model. Performance improvement of 3-6% on annotation-scarce downstream tasks of fact verification, multiple-choice QA, and contract clause retrieval demonstrates the usefulness of DocInfer beyond primary NLI tasks.

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DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents
Puneet Mathur | Mihir Goyal | Ramit Sawhney | Ritik Mathur | Jochen Leidner | Franck Dernoncourt | Dinesh Manocha
Findings of the Association for Computational Linguistics: EMNLP 2022

Financial prediction is complex due to the stochastic nature of the stock market. Semi-structured financial documents present comprehensive financial data in tabular formats, such as earnings, profit-loss statements, and balance sheets, and can often contain rich technical analysis along with a textual discussion of corporate history, and management analysis, compliance, and risks. Existing research focuses on the textual and audio modalities of financial disclosures from company conference calls to forecast stock volatility and price movement, but ignores the rich tabular data available in financial reports. Moreover, the economic realm is still plagued with a severe under-representation of various communities spanning diverse demographics, gender, and native speakers. In this work, we show that combining tabular data from financial semi-structured documents with text transcripts and audio recordings not only improves stock volatility and price movement prediction by 5-12% but also reduces gender bias caused due to audio-based neural networks by over 30%.

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DocTime: A Document-level Temporal Dependency Graph Parser
Puneet Mathur | Vlad Morariu | Verena Kaynig-Fittkau | Jiuxiang Gu | Franck Dernoncourt | Quan Tran | Ani Nenkova | Dinesh Manocha | Rajiv Jain
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8% on three datasets from modeling the problem as a graph network with path-prediction loss to incorporate longer range dependencies. This work also demonstrates how the TDG graph can be used to improve the downstream tasks of temporal questions answering and NLI by a relative 4-10% with a new framework that incorporates the temporal dependency graph into the self-attention layer of Transformer models (Time-transformer). Finally, we develop and evaluate on a new temporal dependency graph dataset for the domain of contractual documents, which has not been previously explored in this setting.

2021

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TIMERS: Document-level Temporal Relation Extraction
Puneet Mathur | Rajiv Jain | Franck Dernoncourt | Vlad Morariu | Quan Hung Tran | Dinesh Manocha
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.