Emily Pitler


2019

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Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Daniel Andor | Luheng He | Kenton Lee | Emily Pitler
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We enable a BERT-based reading comprehension model to perform lightweight numerical reasoning. We augment the model with a predefined set of executable ‘programs’ which encompass simple arithmetic as well as extraction. Rather than having to learn to manipulate numbers directly, the model can pick a program and execute it. On the recent Discrete Reasoning Over Passages (DROP) dataset, designed to challenge reading comprehension models, we show a 33% absolute improvement by adding shallow programs. The model can learn to predict new operations when appropriate in a math word problem setting (Roy and Roth, 2015) with very few training examples.

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Synthetic QA Corpora Generation with Roundtrip Consistency
Chris Alberti | Daniel Andor | Emily Pitler | Jacob Devlin | Michael Collins
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.

2018

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Proceedings of the First Workshop on Multilingual Surface Realisation
Simon Mille | Anja Belz | Bernd Bohnet | Emily Pitler | Leo Wanner
Proceedings of the First Workshop on Multilingual Surface Realisation

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The First Multilingual Surface Realisation Shared Task (SR’18): Overview and Evaluation Results
Simon Mille | Anja Belz | Bernd Bohnet | Yvette Graham | Emily Pitler | Leo Wanner
Proceedings of the First Workshop on Multilingual Surface Realisation

We report results from the SR’18 Shared Task, a new multilingual surface realisation task organised as part of the ACL’18 Workshop on Multilingual Surface Realisation. As in its English-only predecessor task SR’11, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in ten, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR’18 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.

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A Challenge Set and Methods for Noun-Verb Ambiguity
Ali Elkahky | Kellie Webster | Daniel Andor | Emily Pitler
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1% of each other when measured on the WSJ have accuracies ranging from 57% to 75% accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89%, a 14% absolute (52% relative) improvement. Downstream, using just this enhanced tagger yields a 28% reduction in error over the prior best learned model for homograph disambiguation for textto-speech synthesis.

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Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Bernd Bohnet | Ryan McDonald | Gonçalo Simões | Daniel Andor | Emily Pitler | Joshua Maynez
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with dynamically and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states.

2017

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Natural Language Processing with Small Feed-Forward Networks
Jan A. Botha | Emily Pitler | Ji Ma | Anton Bakalov | Alex Salcianu | David Weiss | Ryan McDonald | Slav Petrov
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.

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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

2016

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Generalized Transition-based Dependency Parsing via Control Parameters
Bernd Bohnet | Ryan McDonald | Emily Pitler | Ji Ma
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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A Linear-Time Transition System for Crossing Interval Trees
Emily Pitler | Ryan McDonald
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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A Crossing-Sensitive Third-Order Factorization for Dependency Parsing
Emily Pitler
Transactions of the Association for Computational Linguistics, Volume 2

Parsers that parametrize over wider scopes are generally more accurate than edge-factored models. For graph-based non-projective parsers, wider factorizations have so far implied large increases in the computational complexity of the parsing problem. This paper introduces a “crossing-sensitive” generalization of a third-order factorization that trades off complexity in the model structure (i.e., scoring with features over multiple edges) with complexity in the output structure (i.e., producing crossing edges). Under this model, the optimal 1-Endpoint-Crossing tree can be found in O(n4) time, matching the asymptotic run-time of both the third-order projective parser and the edge-factored 1-Endpoint-Crossing parser. The crossing-sensitive third-order parser is significantly more accurate than the third-order projective parser under many experimental settings and significantly less accurate on none.

2013

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Finding Optimal 1-Endpoint-Crossing Trees
Emily Pitler | Sampath Kannan | Mitchell Marcus
Transactions of the Association for Computational Linguistics, Volume 1

Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees. For 95.8–99.8% of dependency parses in various natural language treebanks, whenever an edge is crossed, the edges that cross it all have a common vertex. The optimal dependency tree that satisfies this 1-Endpoint-Crossing property can be found with an O(n4) parsing algorithm that recursively combines forests over intervals with one exterior point. 1-Endpoint-Crossing trees also have natural connections to linguistics and another class of graphs that has been studied in NLP.

2012

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Attacking Parsing Bottlenecks with Unlabeled Data and Relevant Factorizations
Emily Pitler
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Dynamic Programming for Higher Order Parsing of Gap-Minding Trees
Emily Pitler | Sampath Kannan | Mitchell Marcus
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Proceedings of the ACL 2011 Student Session
Sasa Petrovic | Ethan Selfridge | Emily Pitler | Miles Osborne | Thamar Solorio
Proceedings of the ACL 2011 Student Session

2010

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New Tools for Web-Scale N-grams
Dekang Lin | Kenneth Church | Heng Ji | Satoshi Sekine | David Yarowsky | Shane Bergsma | Kailash Patil | Emily Pitler | Rachel Lathbury | Vikram Rao | Kapil Dalwani | Sushant Narsale
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

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Using Web-scale N-grams to Improve Base NP Parsing Performance
Emily Pitler | Shane Bergsma | Dekang Lin | Kenneth Church
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Automatic Evaluation of Linguistic Quality in Multi-Document Summarization
Emily Pitler | Annie Louis | Ani Nenkova
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Creating Robust Supervised Classifiers via Web-Scale N-Gram Data
Shane Bergsma | Emily Pitler | Dekang Lin
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Automatic sense prediction for implicit discourse relations in text
Emily Pitler | Annie Louis | Ani Nenkova
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Using Syntax to Disambiguate Explicit Discourse Connectives in Text
Emily Pitler | Ani Nenkova
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Using Word-Sense Disambiguation Methods to Classify Web Queries by Intent
Emily Pitler | Ken Church
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Revisiting Readability: A Unified Framework for Predicting Text Quality
Emily Pitler | Ani Nenkova
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Easily Identifiable Discourse Relations
Emily Pitler | Mridhula Raghupathy | Hena Mehta | Ani Nenkova | Alan Lee | Aravind Joshi
Coling 2008: Companion volume: Posters

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