86 citations · 305 across the 37 of their papers we have counts for
10 papers · 1 filter
Validity Assessment of Legal Will Statements as Natural Language Inference
Alice Saebom Kwak, Jacob O. Israelsen, Clayton T. Morrison +2
This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decis…
BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples
Mohaddeseh Bastan, Mihai Surdeanu, Niranjan Balasubramanian
Natural language inference (NLI) is critical for complex decision-making in biomedical domain. One key question, for example, is whether a given biomedical mechanism is supported b…
A Compact Pretraining Approach for Neural Language Models
Shahriar Golchin, Mihai Surdeanu, Nazgol Tavabi +1
Domain adaptation for large neural language models (NLMs) is coupled with massive amounts of unstructured data in the pretraining phase. In this study, however, we show that pretra…
Learning Open Domain Multi-hop Search Using Reinforcement Learning
Enrique Noriega-Atala, Mihai Surdeanu, Clayton T. Morrison
We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. The method learns a policy for directing…
SuMe: A Dataset Towards Summarizing Biomedical Mechanisms
Mohaddeseh Bastan, Nishant Shankar, Mihai Surdeanu +1
Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies o…
Better Retrieval May Not Lead to Better Question Answering
Zhengzhong Liang, Tushar Khot, Steven Bethard +2
Considerable progress has been made recently in open-domain question answering (QA) problems, which require Information Retrieval (IR) and Reading Comprehension (RC). A popular app…
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers
Zheng Tang, Mihai Surdeanu
We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach…
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
Robert Vacareanu, Marco A. Valenzuela-Escarcega, George C. G. Barbosa +2
While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand…
Automatic Correction of Syntactic Dependency Annotation Differences
Andrew Zupon, Andrew Carnie, Michael Hammond +1
Annotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be as easily replaced compared with resource-rich lang…
Informal Persian Universal Dependency Treebank
Roya Kabiri, Simin Karimi, Mihai Surdeanu
This paper presents the phonological, morphological, and syntactic distinctions between formal and informal Persian, showing that these two variants have fundamental differences th…