Matthew R. Gormley

Also published as: Matthew Gormley


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Learning Mutually Informed Representations for Characters and Subwords
Yilin Wang | Xinyi Hu | Matthew Gormley
Findings of the Association for Computational Linguistics: NAACL 2024

Most pretrained language models rely on subword tokenization, which processes text as a sequence of subword tokens. However, different granularities of text, such as characters, subwords, and words, can contain different kinds of information. Previous studies have shown that incorporating multiple input granularities improves model generalization, yet very few of them outputs useful representations for each granularity. In this paper, we introduce the entanglement model, aiming to combine character and subword language models. Inspired by vision-language models, our model treats characters and subwords as separate modalities, and it generates mutually informed representations for both granularities as output. We evaluate our model on text classification, named entity recognition, POS-tagging, and character-level sequence labeling (intraword code-switching). Notably, the entanglement model outperforms its backbone language models, particularly in the presence of noisy texts and low-resource languages. Furthermore, the entanglement model even outperforms larger pre-trained models on all English sequence labeling tasks and classification tasks. We make our code publically available.


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SummQA at MEDIQA-Chat 2023: In-Context Learning with GPT-4 for Medical Summarization
Yash Mathur | Sanketh Rangreji | Raghav Kapoor | Medha Palavalli | Amanda Bertsch | Matthew Gormley
Proceedings of the 5th Clinical Natural Language Processing Workshop

Medical dialogue summarization is challenging due to the unstructured nature of medical conversations, the use of medical terminologyin gold summaries, and the need to identify key information across multiple symptom sets. We present a novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA 2023 Shared Task. Our approach for sectionwise summarization (Task A) is a two-stage process of selecting semantically similar dialogues and using the top-k similar dialogues as in-context examples for GPT-4. For full-note summarization (Task B), we use a similar solution with k=1. We achieved 3rd place in Task A (2nd among all teams), 4th place in Task B Division Wise Summarization (2nd among all teams), 15th place in Task A Section Header Classification (9th among all teams), and 8th place among all teams in Task B. Our results highlight the effectiveness of few-shot prompting for this task, though we also identify several weaknesses of prompting-based approaches. We compare GPT-4 performance with several finetuned baselines. We find that GPT-4 summaries are more abstractive and shorter. We make our code publicly available.

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It’s MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk
Amanda Bertsch | Alex Xie | Graham Neubig | Matthew Gormley
Proceedings of the Big Picture Workshop

Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates. It is a simple but powerful method: for an additional cost at inference time, MBR provides reliable several-point improvements across metrics for a wide variety of tasks without any additional data or training. Despite this, MBR is not frequently applied in NLP works, and knowledge of the method itself is limited. We first provide an introduction to the method and the recent literature. We show that several recent methods that do not reference MBR can be written as special cases of MBR; this reformulation provides additional theoretical justification for the performance of these methods, explaining some results that were previously only empirical. We provide theoretical and empirical results about the effectiveness of various MBR variants and make concrete recommendations for the application of MBR in NLP models, including future directions in this area.

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MDACE: MIMIC Documents Annotated with Code Evidence
Hua Cheng | Rana Jafari | April Russell | Russell Klopfer | Edmond Lu | Benjamin Striner | Matthew Gormley
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in recent years, thanks to advances in machine learning technologies. Yet simply predicting a set of final codes for a patient encounter is insufficient as CAC systems are required to provide supporting textual evidence to justify the billing codes. A model able to produce accurate and reliable supporting evidence for each code would be a tremendous benefit. However, a human annotated code evidence corpus is extremely difficult to create because it requires specialized knowledge. In this paper, we introduce MDACE, the first publicly available code evidence dataset, which is built on a subset of the MIMIC-III clinical records. The dataset – annotated by professional medical coders – consists of 302 Inpatient charts with 3,934 evidence spans and 52 Profee charts with 5,563 evidence spans. We implemented several evidence extraction methods based on the EffectiveCAN model (Liu et al., 2021) to establish baseline performance on this dataset. MDACE can be used to evaluate code evidence extraction methods for CAC systems, as well as the accuracy and interpretability of deep learning models for multi-label classification. We believe that the release of MDACE will greatly improve the understanding and application of deep learning technologies for medical coding and document classification.


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He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues
Amanda Bertsch | Graham Neubig | Matthew R. Gormley
Findings of the Association for Computational Linguistics: EMNLP 2022

In this work, we define a new style transfer task: perspective shift, which reframes a dialouge from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approaches and discuss further directions on this task when applied to short dialogues. As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data. Additionally, supervised extractive models perform better when trained on perspective shifted data than on the original dialogues. We release our code publicly.

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Revisiting text decomposition methods for NLI-based factuality scoring of summaries
John Glover | Federico Fancellu | Vasudevan Jagannathan | Matthew R. Gormley | Thomas Schaaf
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Scoring the factuality of a generated summary involves measuring the degree to which a target text contains factual information using the input document as support. Given the similarities in the problem formulation, previous work has shown that Natural Language Inference models can be effectively repurposed to perform this task. As these models are trained to score entailment at a sentence level, several recent studies have shown that decomposing either the input document or the summary into sentences helps with factuality scoring. But is fine-grained decomposition always a winning strategy? In this paper we systematically compare different granularities of decomposition - from document to sub-sentence level, and we show that the answer is no. Our results show that incorporating additional context can yield improvement, but that this does not necessarily apply to all datasets. We also show that small changes to previously proposed entailment-based scoring methods can result in better performance, highlighting the need for caution in model and methodology selection for downstream tasks.

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On Efficiently Acquiring Annotations for Multilingual Models
Joel Ruben Antony Moniz | Barun Patra | Matthew Gormley
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages. In this work, we show that the strategy of joint learning across multiple languages using a single model performs substantially better than the aforementioned alternatives. We also demonstrate that active learning provides additional, complementary benefits. We show that this simple approach enables the model to be data efficient by allowing it to arbitrate its annotation budget to query languages it is less certain on. We illustrate the effectiveness of our proposed method on a diverse set of tasks: a classification task with 4 languages, a sequence tagging task with 4 languages and a dependency parsing task with 5 languages. Our proposed method, whilst simple, substantially outperforms the other viable alternatives for building a model in a multilingual setting under constrained budgets.


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Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations
Longxiang Zhang | Renato Negrinho | Arindam Ghosh | Vasudevan Jagannathan | Hamid Reza Hassanzadeh | Thomas Schaaf | Matthew R. Gormley
Findings of the Association for Computational Linguistics: EMNLP 2021

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).

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Limitations of Autoregressive Models and Their Alternatives
Chu-Cheng Lin | Aaron Jaech | Xin Li | Matthew R. Gormley | Jason Eisner
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol. While this is attractive, it means they cannot model distributions whose next-symbol probability is hard to compute. Indeed, they cannot even model them well enough to solve associated easy decision problems for which an engineer might want to consult a language model. These limitations apply no matter how much computation and data are used to train the model, unless the model is given access to oracle parameters that grow superpolynomially in sequence length. Thus, simply training larger autoregressive language models is not a panacea for NLP. Alternatives include energy-based models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). Both are powerful enough to escape the above limitations.

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Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction
Maria Ryskina | Eduard Hovy | Taylor Berg-Kirkpatrick | Matthew R. Gormley
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention. Focusing on the less explored unsupervised learning scenario, we compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance. We analyze the distributions of different error classes using two unsupervised tasks as testbeds: converting informally romanized text into the native script of its language (for Russian, Arabic, and Kannada) and translating between a pair of closely related languages (Serbian and Bosnian). Finally, we investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively.

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Effective Convolutional Attention Network for Multi-label Clinical Document Classification
Yang Liu | Hua Cheng | Russell Klopfer | Matthew R. Gormley | Thomas Schaaf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin.


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An Empirical Investigation of Beam-Aware Training in Supertagging
Renato Negrinho | Matthew R. Gormley | Geoff Gordon
Findings of the Association for Computational Linguistics: EMNLP 2020

Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatches as, during training, the model is not exposed to its mistakes and does not use beam search. Beam-aware training aims to address these problems, but unfortunately, it is not yet widely used due to a lack of understanding about how it impacts performance, when it is most useful, and whether it is stable. Recently, Negrinho et al. (2018) proposed a meta-algorithm that captures beam-aware training algorithms and suggests new ones, but unfortunately did not provide empirical results. In this paper, we begin an empirical investigation: we train the supertagging model of Vaswani et al. (2018) and a simpler model with instantiations of the meta-algorithm. We explore the influence of various design choices and make recommendations for choosing them. We observe that beam-aware training improves performance for both models, with large improvements for the simpler model which must effectively manage uncertainty during decoding. Our results suggest that a model must be learned with search to maximize its effectiveness.

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Phonetic and Visual Priors for Decipherment of Informal Romanization
Maria Ryskina | Matthew R. Gormley | Taylor Berg-Kirkpatrick
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages—namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model’s performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.

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Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors
Sida Gao | Matthew R. Gormley
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Most recent improvements in NLP come from changes to the neural network architectures modeling the text input. Yet, state-of-the-art models often rely on simple approaches to model the label space, e.g. bigram Conditional Random Fields (CRFs) in sequence tagging. More expressive graphical models are rarely used due to their prohibitive computational cost. In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling. Our approach is the natural adaptation of SampleRank (Wick et al., 2011) to neural models, and is widely applicable to tasks beyond sequence tagging. We apply our approach to named entity recognition and present a neural skip-chain CRF model, for which exact inference is impractical. The skip-chain model improves over a strong baseline on three languages from CoNLL-02/03. We obtain new state-of-the-art results on Dutch.


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Neural Finite-State Transducers: Beyond Rational Relations
Chu-Cheng Lin | Hao Zhu | Matthew R. Gormley | Jason Eisner
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)

We introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings. The probability of a string pair is obtained by marginalizing over all its accepting paths in a finite state transducer. In contrast to ordinary weighted FSTs, however, each path is scored using an arbitrary function such as a recurrent neural network, which breaks the usual conditional independence assumption (Markov property). NFSTs are more powerful than previous finite-state models with neural features (Rastogi et al., 2016.) We present training and inference algorithms for locally and globally normalized variants of NFSTs. In experiments on different transduction tasks, they compete favorably against seq2seq models while offering interpretable paths that correspond to hard monotonic alignments.

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Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra | Joel Ruben Antony Moniz | Sarthak Garg | Matthew R. Gormley | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.


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Neural Factor Graph Models for Cross-lingual Morphological Tagging
Chaitanya Malaviya | Matthew R. Gormley | Graham Neubig
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Morphological analysis involves predicting the syntactic traits of a word (e.g. POS: Noun, Case: Acc, Gender: Fem). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict, often false, assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing this assumption. The proposed model uses factorial conditional random fields with neural network potentials, making it possible to (1) utilize the expressive power of neural network representations to smooth over superficial differences in the surface forms, (2) model pairwise and transitive relationships between tags, and (3) accurately generate tag sets that are unseen or rare in the training data. Experiments on four languages from the Universal Dependencies Treebank demonstrate superior tagging accuracies over existing cross-lingual approaches.


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Embedding Lexical Features via Low-Rank Tensors
Mo Yu | Mark Dredze | Raman Arora | Matthew R. Gormley
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Matthew R. Gormley | Mo Yu | Mark Dredze
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Structured Belief Propagation for NLP
Matthew R. Gormley | Jason Eisner
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts

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Approximation-Aware Dependency Parsing by Belief Propagation
Matthew R. Gormley | Mark Dredze | Jason Eisner
Transactions of the Association for Computational Linguistics, Volume 3

We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O(n3) runtime. It outputs the parse with maximum expected recall—but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as others have done for loopy CRFs (Domke, 2010; Stoyanov et al., 2011; Domke, 2011; Stoyanov and Eisner, 2012). The resulting parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.

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Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction
Mo Yu | Matthew R. Gormley | Mark Dredze
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Concrete Chinese NLP Pipeline
Nanyun Peng | Francis Ferraro | Mo Yu | Nicholas Andrews | Jay DeYoung | Max Thomas | Matthew R. Gormley | Travis Wolfe | Craig Harman | Benjamin Van Durme | Mark Dredze
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations


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Low-Resource Semantic Role Labeling
Matthew R. Gormley | Margaret Mitchell | Benjamin Van Durme | Mark Dredze
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Structured Belief Propagation for NLP
Matthew Gormley | Jason Eisner
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials


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Nonconvex Global Optimization for Latent-Variable Models
Matthew R. Gormley | Jason Eisner
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Topic Models and Metadata for Visualizing Text Corpora
Justin Snyder | Rebecca Knowles | Mark Dredze | Matthew Gormley | Travis Wolfe
Proceedings of the 2013 NAACL HLT Demonstration Session


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Annotated Gigaword
Courtney Napoles | Matthew Gormley | Benjamin Van Durme
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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Entity Clustering Across Languages
Spence Green | Nicholas Andrews | Matthew R. Gormley | Mark Dredze | Christopher D. Manning
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Shared Components Topic Models
Matthew R. Gormley | Mark Dredze | Benjamin Van Durme | Jason Eisner
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Non-Expert Correction of Automatically Generated Relation Annotations
Matthew R. Gormley | Adam Gerber | Mary Harper | Mark Dredze
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk