Pradeep Dasigi


2021

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A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Pradeep Dasigi | Kyle Lo | Iz Beltagy | Arman Cohan | Noah A. Smith | Matt Gardner
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present Qasper, a dataset of 5049 questions over 1585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.

2020

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Evaluating Models’ Local Decision Boundaries via Contrast Sets
Matt Gardner | Yoav Artzi | Victoria Basmov | Jonathan Berant | Ben Bogin | Sihao Chen | Pradeep Dasigi | Dheeru Dua | Yanai Elazar | Ananth Gottumukkala | Nitish Gupta | Hannaneh Hajishirzi | Gabriel Ilharco | Daniel Khashabi | Kevin Lin | Jiangming Liu | Nelson F. Liu | Phoebe Mulcaire | Qiang Ning | Sameer Singh | Noah A. Smith | Sanjay Subramanian | Reut Tsarfaty | Eric Wallace | Ally Zhang | Ben Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model’s decision boundary, which can be used to more accurately evaluate a model’s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.

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IIRC: A Dataset of Incomplete Information Reading Comprehension Questions
James Ferguson | Matt Gardner | Hannaneh Hajishirzi | Tushar Khot | Pradeep Dasigi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of sufficient information and locating sources for that information. To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear. This process also gave many questions without answers, and those that require discrete reasoning, increasing the difficulty of the task. We follow recent modeling work on various reading comprehension datasets to construct a baseline model for this dataset, finding that it achieves 31.1% F1 on this task, while estimated human performance is 88.4%. The dataset, code for the baseline system, and a leaderboard can be found at https://allennlp.org/iirc.

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Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
Qiang Ning | Hao Wu | Pradeep Dasigi | Dheeru Dua | Matt Gardner | Robert L. Logan IV | Ana Marasović | Zhen Nie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators efficiently; and (3) reproducibility. To address these problems, we introduce CROWDAQ, an open-source platform that standardizes the data collection pipeline with customizable user-interface components, automated annotator qualification, and saved pipelines in a re-usable format. We show that CROWDAQ simplifies data annotation significantly on a diverse set of data collection use cases and we hope it will be a convenient tool for the community.

2019

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Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Pradeep Dasigi | Nelson F. Liu | Ana Marasović | Noah A. Smith | Matt Gardner
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to resolve coreference. We present a new crowdsourced dataset containing more than 24K span-selection questions that require resolving coreference among entities in over 4.7K English paragraphs from Wikipedia. Obtaining questions focused on such phenomena is challenging, because it is hard to avoid lexical cues that shortcut complex reasoning. We deal with this issue by using a strong baseline model as an adversary in the crowdsourcing loop, which helps crowdworkers avoid writing questions with exploitable surface cues. We show that state-of-the-art reading comprehension models perform significantly worse than humans on this benchmark—the best model performance is 70.5 F1, while the estimated human performance is 93.4 F1.

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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dheeru Dua | Yizhong Wang | Pradeep Dasigi | Gabriel Stanovsky | Sameer Singh | Matt Gardner
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We introduce a new reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs. In this crowdsourced, adversarially-created, 55k-question benchmark, a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs, as they remove the paraphrase-and-entity-typing shortcuts available in prior datasets. We apply state-of-the-art methods from both the reading comprehension and semantic parsing literatures on this dataset and show that the best systems only achieve 38.4% F1 on our generalized accuracy metric, while expert human performance is 96%. We additionally present a new model that combines reading comprehension methods with simple numerical reasoning to achieve 51% F1.

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Iterative Search for Weakly Supervised Semantic Parsing
Pradeep Dasigi | Matt Gardner | Shikhar Murty | Luke Zettlemoyer | Eduard Hovy
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. This training scheme lets us iteratively train models that provide guidance to subsequent ones to search for logical forms of increasing complexity, thus dealing with the problem of spuriousness. We evaluate these techniques on two hard datasets: WikiTableQuestions (WTQ) and Cornell Natural Language Visual Reasoning (NLVR), and show that our training algorithm outperforms the previous best systems, on WTQ in a comparable setting, and on NLVR with significantly less supervision.

2018

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Neural Semantic Parsing
Matt Gardner | Pradeep Dasigi | Srinivasan Iyer | Alane Suhr | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation. In the last two years, the models used for semantic parsing have changed dramatically with the introduction of neural encoder-decoder methods that allow us to rethink many of the previous assumptions underlying semantic parsing. We aim to inform those already interested in semantic parsing research of these new developments in the field, as well as introduce the topic as an exciting research area to those who are unfamiliar with it. Current approaches for neural semantic parsing share several similarities with neural machine translation, but the key difference between the two fields is that semantic parsing translates natural language into a formal language, while machine translation translates it into a different natural language. The formal language used in semantic parsing allows for constrained decoding, where the model is constrained to only produce outputs that are valid formal statements. We will describe the various approaches researchers have taken to do this. We will also discuss the choice of formal languages used by semantic parsers, and describe why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. We will then describe a particularly challenging setting for semantic parsing, where there is additional context or interaction that the parser must take into account when translating natural language to formal language, and give an overview of recent work in this direction. Finally, we will introduce some tools available in AllenNLP for doing semantic parsing research.

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AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner | Joel Grus | Mark Neumann | Oyvind Tafjord | Pradeep Dasigi | Nelson F. Liu | Matthew Peters | Michael Schmitz | Luke Zettlemoyer
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.

2017

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Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Pradeep Dasigi | Waleed Ammar | Chris Dyer | Eduard Hovy
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.

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Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Jayant Krishnamurthy | Pradeep Dasigi | Matt Gardner
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WikiTableQuestions data set, our parser achieves a state-of-the-art accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.

2014

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Modeling Newswire Events using Neural Networks for Anomaly Detection
Pradeep Dasigi | Eduard Hovy
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon
Mona Diab | Mohamed Al-Badrashiny | Maryam Aminian | Mohammed Attia | Heba Elfardy | Nizar Habash | Abdelati Hawwari | Wael Salloum | Pradeep Dasigi | Ramy Eskander
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe Tharwa’s creation process and report on its current status. The lexical entries are augmented with various elements of linguistic information such as POS, gender, rationality, number, and root and pattern information. The lexicon is based on a compilation of information from both monolingual and bilingual existing resources such as paper dictionaries and electronic, corpus-based dictionaries. Multiple levels of quality checks are performed on the output of each step in the creation process. The importance of this lexicon lies in the fact that it is the first resource of its kind bridging multiple variants of Arabic with English. Furthermore, it is a wide coverage lexical resource containing over 73,000 Egyptian entries. Tharwa is publicly available. We believe it will have a significant impact on both Theoretical Linguistics as well as Computational Linguistics research.

2013

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Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition
Nadi Tomeh | Nizar Habash | Ryan Roth | Noura Farra | Pradeep Dasigi | Mona Diab
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Subgroup Detection in Ideological Discussions
Amjad Abu-Jbara | Pradeep Dasigi | Mona Diab | Dragomir Radev
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Genre Independent Subgroup Detection in Online Discussion Threads: A Study of Implicit Attitude using Textual Latent Semantics
Pradeep Dasigi | Weiwei Guo | Mona Diab
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Named Entity Transliteration Generation Leveraging Statistical Machine Translation Technology
Pradeep Dasigi | Mona Diab
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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CODACT: Towards Identifying Orthographic Variants in Dialectal Arabic
Pradeep Dasigi | Mona Diab
Proceedings of 5th International Joint Conference on Natural Language Processing