Rebecca Hwa


2020

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Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models
Meiqi Guo | Rebecca Hwa | Yu-Ru Lin | Wen-Ting Chung
Proceedings of the 28th International Conference on Computational Linguistics

We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.

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Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation
Omid Kashefi | Rebecca Hwa
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers. However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training. In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is “hard to distinguish” by considering the difference between the distribution of the augmented samples of different classes. Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.

2018

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Semantic Pleonasm Detection
Omid Kashefi | Andrew T. Lucas | Rebecca Hwa
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.

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Heuristically Informed Unsupervised Idiom Usage Recognition
Changsheng Liu | Rebecca Hwa
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.

2017

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A Corpus of Annotated Revisions for Studying Argumentative Writing
Fan Zhang | Homa B. Hashemi | Rebecca Hwa | Diane Litman
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents ArgRewrite, a corpus of between-draft revisions of argumentative essays. Drafts are manually aligned at the sentence level, and the writer’s purpose for each revision is annotated with categories analogous to those used in argument mining and discourse analysis. The corpus should enable advanced research in writing comparison and revision analysis, as demonstrated via our own studies of student revision behavior and of automatic revision purpose prediction.

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Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Martha Palmer | Rebecca Hwa | Sebastian Riedel
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2016

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An Evaluation of Parser Robustness for Ungrammatical Sentences
Homa B. Hashemi | Rebecca Hwa
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Phrasal Substitution of Idiomatic Expressions
Changsheng Liu | Rebecca Hwa
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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ArgRewrite: A Web-based Revision Assistant for Argumentative Writings
Fan Zhang | Rebecca Hwa | Diane Litman | Homa B. Hashemi
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2014

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Redundancy Detection in ESL Writings
Huichao Xue | Rebecca Hwa
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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A Comparison of MT Errors and ESL Errors
Homa B. Hashemi | Rebecca Hwa
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Generating fluent and grammatical sentences is a major goal for both Machine Translation (MT) and second-language Grammar Error Correction (GEC), but there have not been a lot of cross-fertilization between the two research communities. Arguably, an automatic translate-to-English system might be seen as an English as a Second Language (ESL) writer whose native language is the source language. This paper investigates whether research findings from the GEC community may help with characterizing MT error analysis. We describe a method for the automatic classification of MT errors according to English as a Second Language (ESL) error categories and conduct a large comparison experiment that includes both high-performing and low-performing translate-to-English MT systems for several source languages. Comparing the distribution of MT error types for all the systems suggests that MT systems have fairly similar distributions regardless of their source languages, and the high-performing MT systems have error distributions that are more similar to those of the low-performing MT systems than to those of ESL learners with the same L1.

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Improved Correction Detection in Revised ESL Sentences
Huichao Xue | Rebecca Hwa
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Recognizing Arguing Subjectivity and Argument Tags
Alexander Conrad | Janyce Wiebe | Rebecca Hwa
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

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Modeling ESL Word Choice Similarities By Representing Word Intensions and Extensions
Huichao Xue | Rebecca Hwa
Proceedings of COLING 2012

2010

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Improving Phrase-Based Translation with Prototypes of Short Phrases
Frank Liberato | Behrang Mohit | Rebecca Hwa
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Using Variable Decoding Weight for Language Model in Statistical Machine Translation
Behrang Mohit | Rebecca Hwa | Alon Lavie
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

This paper investigates varying the decoder weight of the language model (LM) when translating different parts of a sentence. We determine the condition under which the LM weight should be adapted. We find that a better translation can be achieved by varying the LM weight when decoding the most problematic spot in a sentence, which we refer to as a difficult segment. Two adaptation strategies are proposed and compared through experiments. We find that adapting a different LM weight for every difficult segment resulted in the largest improvement in translation quality.

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Syntax-Driven Machine Translation as a Model of ESL Revision
Huichao Xue | Rebecca Hwa
Coling 2010: Posters

2009

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Correcting Automatic Translations through Collaborations between MT and Monolingual Target-Language Users
Joshua Albrecht | Rebecca Hwa | G. Elisabeta Marai
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Language Model Adaptation for Difficult to Translate Phrases
Behrang Mohit | Frank Liberato | Rebecca Hwa
Proceedings of the 13th Annual conference of the European Association for Machine Translation

2008

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The Role of Pseudo References in MT Evaluation
Joshua Albrecht | Rebecca Hwa
Proceedings of the Third Workshop on Statistical Machine Translation

2007

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Localization of Difficult-to-Translate Phrases
Behrang Mohit | Rebecca Hwa
Proceedings of the Second Workshop on Statistical Machine Translation

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A Re-examination of Machine Learning Approaches for Sentence-Level MT Evaluation
Joshua Albrecht | Rebecca Hwa
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Regression for Sentence-Level MT Evaluation with Pseudo References
Joshua Albrecht | Rebecca Hwa
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Proceedings of the COLING/ACL 2006 Student Research Workshop
Marine Carpuat | Kevin Duh | Rebecca Hwa
Proceedings of the COLING/ACL 2006 Student Research Workshop

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Corpus Variations for Translation Lexicon Induction
Rebecca Hwa | Carol Nichols | Khalil Sima’an
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

Lexical mappings (word translations) between languages are an invaluable resource for multilingual processing. While the problem of extracting lexical mappings from parallel corpora is well-studied, the task is more challenging when the language samples are from non-parallel corpora. The goal of this work is to investigate one such scenario: finding lexical mappings between dialects of a diglossic language, in which people conduct their written communications in a prestigious formal dialect, but they communicate verbally in a colloquial dialect. Because the two dialects serve different socio-linguistic functions, parallel corpora do not naturally exist between them. An example of a diglossic dialect pair is Modern Standard Arabic (MSA) and Levantine Arabic. In this paper, we evaluate the applicability of a standard algorithm for inducing lexical mappings between comparable corpora (Rapp, 1999) to such diglossic corpora pairs. The focus of the paper is an in-depth error analysis, exploring the notion of relatedness in diglossic corpora and scrutinizing the effects of various dimensions of relatedness (such as mode, topic, style, and statistics) on the quality of the resulting translation lexicon.

2005

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A Backoff Model for Bootstrapping Resources for Non-English Languages
Chenhai Xi | Rebecca Hwa
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Syntax-based Semi-Supervised Named Entity Tagging
Behrang Mohit | Rebecca Hwa
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Word Alignment and Cross-Lingual Resource Acquisition
Carol Nichols | Rebecca Hwa
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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Co-training for Predicting Emotions with Spoken Dialogue Data
Beatriz Maeireizo | Diane Litman | Rebecca Hwa
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Sample Selection for Statistical Parsing
Rebecca Hwa
Computational Linguistics, Volume 30, Number 3, September 2004

2003

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Example Selection for Bootstrapping Statistical Parsers
Mark Steedman | Rebecca Hwa | Stephen Clark | Miles Osborne | Anoop Sarkar | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Bootstrapping statistical parsers from small datasets
Mark Steedman | Miles Osborne | Anoop Sarkar | Stephen Clark | Rebecca Hwa | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Evaluating Translational Correspondence using Annotation Projection
Rebecca Hwa | Philip Resnik | Amy Weinberg | Okan Kolak
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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DUSTer: a method for unraveling cross-language divergences for statistical word-level alignment
Bonnie Dorr | Lisa Pearl | Rebecca Hwa | Nizar Habash
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers

The frequent occurrence of divergenceS—structural differences between languages—presents a great challenge for statistical word-level alignment. In this paper, we introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our ultimate goal is to enable more accurate alignment and projection of dependency trees in another language without requiring any training on dependency-tree data in that language. We present an empirical analysis comparing the complexities of performing word-level alignments with and without divergence handling. Our results suggest that our approach facilitates word-level alignment, particularly for sentence pairs containing divergences.

2001

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On minimizing training corpus for parser acquisition
Rebecca Hwa
Proceedings of the ACL 2001 Workshop on Computational Natural Language Learning (ConLL)

2000

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Sample Selection for Statistical Grammar Induction
Rebecca Hwa
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

1999

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Supervised Grammar Induction using Training Data with Limited Constituent Information
Rebecca Hwa
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
Rebecca Hwa
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
Rebecca Hwa
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1