Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Neha Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, Andrew McCallum. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures.We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task.We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts.Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types.This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.1337

Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

Given questions regarding some prototypical situation -such as Name something that people usually do before they leave the house for work?-a human can easily answer them via acquired experiences.There can be multiple right answers for such questions, with some more common for a situation than others.This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations.The training set is gathered from an existing set of questions played in a longrunning international game show -FAMILY-FEUD.The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers.We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question.After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task. * Equal contribution.(i) Name something that people usually do before they leave for work?Ask 100 crowd-workers + manual clustering

The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019a) is a selfsupervised neural model that learns to induce syntactic tree structures for input sentences without access to labeled training data.In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing.To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softlyweighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart.Our experiments show that through fine-tuning a pre-trained DIORA with our new algorithm, we improve the state of the art in unsupervised constituency parsing on the English WSJ Penn Treebank by 2.2 6% F1, depending on the data used for fine-tuning.

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.

Meaning Representation formalism is rapidly emerging as an important practical form of structured sentence semantics which, thanks to the availability of largescale annotated corpora, has potential as a convergence point for NLP research.This tutorial unmasks the design philosophy, data creation process, and existing algorithms for AMR semantics.It is intended for anyone interested in working with AMR data, including parsing text into AMRs, generating text from AMRs, and applying AMRs to tasks such as machine translation and summarization.