Carolyn Rose

Also published as: Carolyn P. Rosé, Carolyn Rosé, Carolyn P. Rosé, Carolyn P. Rose, Carolyn Penstein Rosé, Carolyn Penstein-Rosé, C. P. Rose, Carolyn Penstein Rose, Carolyn Penstein Rosé


2020

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Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation
Aakanksha Naik | Carolyn Rose
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA uses adversarial training to construct representations that are predictive for trigger identification, but not predictive of the example’s domain. It requires no labeled data from the target domain, making it completely unsupervised. Experiments with two domains (English literature and news) show that ADA leads to an average F1 score improvement of 3.9 on out-of-domain data. Our best performing model (BERT-A) reaches 44-49 F1 across both domains, using no labeled target data. Preliminary experiments reveal that finetuning on 1% labeled data, followed by self-training leads to substantial improvement, reaching 51.5 and 67.2 F1 on literature and news respectively.

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Using Type Information to Improve Entity Coreference Resolution
Sopan Khosla | Carolyn Rose
Proceedings of the First Workshop on Computational Approaches to Discourse

Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.

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Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction
Yansen Wang | Zhen Fan | Carolyn Rose
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval. In contrast to other document types, web page designs are intended for easy navigation and information finding. Effective designs encode within the layout and formatting signals that point to where the important information can be found. In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task. In particular, we leverage both lexical and visual features (e.g., size, font, position) at the micro-level to enable effective strategy induction and meta-level features that describe pages at a macro-level to aid in strategy selection. Our evaluation demonstrates that a combination of effective strategy induction and strategy selection within this approach for the KPE task outperforms state-of-the-art models. A qualitative post-hoc analysis illustrates how these features function within the model.

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Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions
Ritam Dutt | Rishabh Joshi | Carolyn Rose
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The notion of face refers to the public self-image of an individual that emerges both from the individual’s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.

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MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge
Sopan Khosla | Shikhar Vashishth | Jill Fain Lehman | Carolyn Rose
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Information extraction from conversational data is particularly challenging because the task-centric nature of conversation allows for effective communication of implicit information by humans, but is challenging for machines. The challenges may differ between utterances depending on the role of the speaker within the conversation, especially when relevant expertise is distributed asymmetrically across roles. Further, the challenges may also increase over the conversation as more shared context is built up through information communicated implicitly earlier in the dialogue. In this paper, we propose the novel modeling approach MedFilter, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task. We evaluate this approach on a corpus of nearly 7,000 doctor-patient conversations where MedFilter is used to identify medically relevant contributions to the discussion (achieving a 10% improvement over SOTA baselines in terms of area under the PR curve). Identifying task-relevant utterances benefits downstream medical processing, achieving improvements of 15%, 105%, and 23% respectively for the extraction of symptoms, medications, and complaints.

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Agent-Based Dynamic Collaboration Support in a Smart Office Space
Yansen Wang | R. Charles Murray | Haogang Bao | Carolyn Rose
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat-based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios.

2019

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EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Abhilasha Ravichander | Aakanksha Naik | Carolyn Rose | Eduard Hovy
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2 %), but has limited verbal reasoning capabilities (-8.1 %). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding.

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Exploring Numeracy in Word Embeddings
Aakanksha Naik | Abhilasha Ravichander | Carolyn Rose | Eduard Hovy
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions. In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding. Numbers are ubiquitous and frequently appear in text. Inspired by cognitive studies on how humans perceive numbers, we develop an analysis framework to test how well word embeddings capture two essential properties of numbers: magnitude (e.g. 3<4) and numeration (e.g. 3=three). Our experiments reveal that most models capture an approximate notion of magnitude, but are inadequate at capturing numeration. We hope that our observations provide a starting point for the development of methods which better capture numeracy in NLP systems.

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Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
James Fiacco | Samridhi Choudhary | Carolyn Rose
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.

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Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback
Shiyan Jiang | Kexin Yang | Chandrakumari Suvarna | Pooja Casula | Mingtong Zhang | Carolyn Rosé
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.

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Using Functional Schemas to Understand Social Media Narratives
Xinru Yan | Aakanksha Naik | Yohan Jo | Carolyn Rose
Proceedings of the Second Workshop on Storytelling

We propose a novel take on understanding narratives in social media, focusing on learning ”functional story schemas”, which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4% on average. We also observe that these schemas serve as lenses that reveal community norms.

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The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy
Qinlan Shen | Carolyn Rose
Proceedings of the Third Workshop on Abusive Language Online

Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users’ ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.

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TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events
Aakanksha Naik | Luke Breitfeller | Carolyn Rose
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffices to achieve reasonable performance on existing datasets. However, systems should be capable of incorporating cues from document-level structure to assign temporal relations. In this work, we take a first step towards discourse-level temporal ordering by creating TDDiscourse, the first dataset focusing specifically on temporal links between event pairs which are more than one sentence apart. We create TDDiscourse by augmenting TimeBank-Dense, a corpus of English news articles, manually annotating global pairs that cannot be inferred automatically from existing annotations. Our annotations double the number of temporal links in TimeBank-Dense, while possessing several desirable properties such as focusing on long-distance pairs and not being automatically inferable. We adapt and benchmark the performance of three state-of-the-art models on TDDiscourse and observe that existing systems indeed find discourse-level temporal ordering harder.

2018

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Attentive Interaction Model: Modeling Changes in View in Argumentation
Yohan Jo | Shivani Poddar | Byungsoo Jeon | Qinlan Shen | Carolyn Rosé | Graham Neubig
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH’s reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH’s reasoning and that of the challenger’s argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH’s change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.

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Stress Test Evaluation for Natural Language Inference
Aakanksha Naik | Abhilasha Ravichander | Norman Sadeh | Carolyn Rose | Graham Neubig
Proceedings of the 27th International Conference on Computational Linguistics

Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed “stress tests” that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.

2017

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Roles and Success in Wikipedia Talk Pages: Identifying Latent Patterns of Behavior
Keith Maki | Michael Yoder | Yohan Jo | Carolyn Rosé
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article. We first contribute a new public dataset including a task predicting the success of Wikipedia editors involved in discussion, measured by an operationalization of the lasting impact of their edits in the article. We then propose a probabilistic graphical model that advances earlier work inducing latent discussion roles using the light supervision of success in the negotiation task. We evaluate the performance of the model and interpret findings of roles and group configurations that lead to certain outcomes on Wikipedia.

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Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision
Aakanksha Naik | Chris Bogart | Carolyn Rose
BioNLP 2017

In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users’ participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17%, which is 92% of the performance achieved by a supervised baseline system.

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Linguistic Markers of Influence in Informal Interactions
Shrimai Prabhumoye | Samridhi Choudhary | Evangelia Spiliopoulou | Christopher Bogart | Carolyn Rose | Alan W Black
Proceedings of the Second Workshop on NLP and Computational Social Science

There has been a long standing interest in understanding ‘Social Influence’ both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts as influential or non-influential. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the structure of the language and its potential to influence others.

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Code-Switching as a Social Act: The Case of Arabic Wikipedia Talk Pages
Michael Yoder | Shruti Rijhwani | Carolyn Rosé | Lori Levin
Proceedings of the Second Workshop on NLP and Computational Social Science

Code-switching has been found to have social motivations in addition to syntactic constraints. In this work, we explore the social effect of code-switching in an online community. We present a task from the Arabic Wikipedia to capture language choice, in this case code-switching between Arabic and other languages, as a predictor of social influence in collaborative editing. We find that code-switching is positively associated with Wikipedia editor success, particularly borrowing technical language on pages with topics less directly related to Arabic-speaking regions.

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Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames
Hyeju Jang | Keith Maki | Eduard Hovy | Carolyn Rosé
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse. We infer implicit facets of a given metaphor frame using a semi-supervised bootstrapping approach on an unlabeled corpus. Our model applies this frame facet information to metaphor detection, and achieves the state-of-the-art performance on a social media dataset when building upon other proven features in a nonlinear machine learning model. In addition, we illustrate the mechanism through which the frame and topic information enable the more accurate metaphor detection.

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Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences
Yohan Jo | Michael Yoder | Hyeju Jang | Carolyn Rosé
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.

2016

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Metaphor Detection with Topic Transition, Emotion and Cognition in Context
Hyeju Jang | Yohan Jo | Qinlan Shen | Michael Miller | Seungwhan Moon | Carolyn Rosé
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Initiations and Interruptions in a Spoken Dialog System
Leah Nicolich-Henkin | Carolyn Rosé | Alan W Black
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Survey: Computational Sociolinguistics: A Survey
Dong Nguyen | A. Seza Doğruöz | Carolyn P. Rosé | Franciska de Jong
Computational Linguistics, Volume 42, Issue 3 - September 2016

2015

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Effects of Situational Factors on Metaphor Detection in an Online Discussion Forum
Hyeju Jang | Miaomiao Wen | Carolyn Rosé
Proceedings of the Third Workshop on Metaphor in NLP

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Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Alexander Koller | Gabriel Skantze | Filip Jurcicek | Masahiro Araki | Carolyn Penstein Rose
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Metaphor Detection in Discourse
Hyeju Jang | Seungwhan Moon | Yohan Jo | Carolyn Rosé
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Weakly Supervised Role Identification in Teamwork Interactions
Diyi Yang | Miaomiao Wen | Carolyn Rosé
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Modeling the Use of Graffiti Style Features to Signal Social Relations within a Multi-Domain Learning Paradigm
Mario Piergallini | A. Seza Doğruöz | Phani Gadde | David Adamson | Carolyn Rosé
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Conversational Metaphors in Use: Exploring the Contrast between Technical and Everyday Notions of Metaphor
Hyeju Jang | Mario Piergallini | Miaomiao Wen | Carolyn Rosé
Proceedings of the Second Workshop on Metaphor in NLP

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Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs
Carolyn Rose | George Siemens
Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

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Towards Identifying the Resolvability of Threads in MOOCs
Diyi Yang | Miaomiao Wen | Carolyn Rose
Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

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Shared Task on Prediction of Dropout Over Time in Massively Open Online Courses
Carolyn Rosé | George Siemens
Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

2013

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Recognizing Rare Social Phenomena in Conversation: Empowerment Detection in Support Group Chatrooms
Elijah Mayfield | David Adamson | Carolyn Penstein Rosé
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Extracting Events with Informal Temporal References in Personal Histories in Online Communities
Miaomiao Wen | Zeyu Zheng | Hyeju Jang | Guang Xiang | Carolyn Penstein Rosé
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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What’s in a Domain? Multi-Domain Learning for Multi-Attribute Data
Mahesh Joshi | Mark Dredze | William W. Cohen | Carolyn P. Rosé
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Hierarchical Conversation Structure Prediction in Multi-Party Chat
Elijah Mayfield | David Adamson | Carolyn Penstein Rosé
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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An Unsupervised Dynamic Bayesian Network Approach to Measuring Speech Style Accommodation
Mahaveer Jain | John McDonough | Gahgene Gweon | Bhiksha Raj | Carolyn Penstein Rosé
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Multi-Domain Learning: When Do Domains Matter?
Mahesh Joshi | Mark Dredze | William W. Cohen | Carolyn Rosé
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Towards Multi-Document Summarization of Scientific Articles:Making Interesting Comparisons with SciSumm
Nitin Agarwal | Ravi Shankar Reddy | Kiran Gvr | Carolyn Penstein Rosé
Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages

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Language use as a reflection of socialization in online communities
Dong Nguyen | Carolyn P. Rosé
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Author Age Prediction from Text using Linear Regression
Dong Nguyen | Noah A. Smith | Carolyn P. Rosé
Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Comparing Triggering Policies for Social Behaviors
Rohit Kumar | Carolyn Rosé
Proceedings of the SIGDIAL 2011 Conference

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Modeling of Stylistic Variation in Social Media with Stretchy Patterns
Philip Gianfortoni | David Adamson | Carolyn P. Rosé
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties

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Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model
Elijah Mayfield | Carolyn Penstein Rosé
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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SciSumm: A Multi-Document Summarization System for Scientific Articles
Nitin Agarwal | Ravi Shankar Reddy | Kiran Gvr | Carolyn Penstein Rosé
Proceedings of the ACL-HLT 2011 System Demonstrations

2010

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Making Conversational Structure Explicit: Identification of Initiation-response Pairs within Online Discussions
Yi-Chia Wang | Carolyn P. Rosé
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Engaging learning groups using Social Interaction Strategies
Rohit Kumar | Carolyn P. Rosé
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of the NAACL HLT 2010 Demonstration Session
Carolyn Penstein Rosé
Proceedings of the NAACL HLT 2010 Demonstration Session

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An Interactive Tool for Supporting Error Analysis for Text Mining
Elijah Mayfield | Carolyn Penstein-Rosé
Proceedings of the NAACL HLT 2010 Demonstration Session

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Sentiment Classification using Automatically Extracted Subgraph Features
Shilpa Arora | Elijah Mayfield | Carolyn Penstein-Rosé | Eric Nyberg
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text

2009

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Estimating Annotation Cost for Active Learning in a Multi-Annotator Environment
Shilpa Arora | Eric Nyberg | Carolyn P. Rosé
Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing

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Leveraging Structural Relations for Fluent Compressions at Multiple Compression Rates
Sourish Chaudhuri | Naman K. Gupta | Noah A. Smith | Carolyn P. Rosé
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Generalizing Dependency Features for Opinion Mining
Mahesh Joshi | Carolyn Penstein-Rosé
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Identifying Types of Claims in Online Customer Reviews
Shilpa Arora | Mahesh Joshi | Carolyn P. Rosé
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Evaluating the Syntactic Transformations in Gold Standard Corpora for Statistical Sentence Compression
Naman K. Gupta | Sourish Chaudhuri | Carolyn P. Rosé
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Ulrich Germann | Chirag Shah | Svetlana Stoyanchev | Carolyn Penstein Rosé | Anoop Sarkar
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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Building Conversational Agents with Basilica
Rohit Kumar | Carolyn P. Rosé | Michael J. Witbrock
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session

2008

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SIDE: The Summarization Integrated Development Environment
Moonyoung Kang | Sourish Chaudhuri | Mahesh Joshi | Carolyn P. Rosé
Proceedings of the ACL-08: HLT Demo Session

2007

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A Feature Based Approach to Leveraging Context for Classifying Newsgroup Style Discussion Segments
Yi-Chia Wang | Mahesh Joshi | Carolyn Rosé
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

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Topic-Segmentation of Dialogue
Jaime Arguello | Carolyn Rosé
Proceedings of the Analyzing Conversations in Text and Speech

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Backbone Extraction and Pruning for Speeding Up a Deep Parser for Dialogue Systems
Myroslava O. Dzikovska | Carolyn P. Rosé
Proceedings of the Third Workshop on Scalable Natural Language Understanding

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Museli: A Multi-Source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue
Jaime Arguello | Carolyn Rosé
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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InfoMagnets: Making Sense of Corpus Data
Jaime Arguello | Carolyn Rosé
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Demonstrations

2005

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Towards a Prototyping Tool for Behavior Oriented Authoring of Conversational Agents for Educational Applications
Gahgene Gweon | Jaime Arguello | Carol Pai | Regan Carey | Zachary Zaiss | Carolyn Rosé
Proceedings of the Second Workshop on Building Educational Applications Using NLP

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TFLEX: Speeding Up Deep Parsing with Strategic Pruning
Myroslava O. Dzikovska | Carolyn P. Rose
Proceedings of the Ninth International Workshop on Parsing Technology

2004

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A Little Goes a Long Way: Quick Authoring of Semantic Knowledge Sources for Interpretation
Carolyn Penstein Rosé | Brian S. Hall
Proceedings of the 2nd International Workshop on Scalable Natural Language Understanding (ScaNaLU 2004) at HLT-NAACL 2004

2003

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A Comparison of Tutor and Student Behavior in Speech Versus Text Based Tutoring
Carolyn P. Rosé | Diane Litman | Dumisizwe Bhembe | Kate Forbes | Scott Silliman | Ramesh Srivastava | Kurt VanLehn
Proceedings of the HLT-NAACL 03 Workshop on Building Educational Applications Using Natural Language Processing

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A Hybrid Text Classification Approach for Analysis of Student Essays
Carolyn P. Rosé | Antonio Roque | Dumisizwe Bhembe | Kurt VanLehn
Proceedings of the HLT-NAACL 03 Workshop on Building Educational Applications Using Natural Language Processing

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A Hybrid Approach to Content Analysis for Automatic Essay Grading
Carolyn P. Rose | Antonio Roque | Dumisizwe Bhembe | Kurt VanLehn
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

2000

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Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification
Alon Lavie | Carolyn Penstein Rosé
Proceedings of the Sixth International Workshop on Parsing Technologies

Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.

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A Framework for Robust Semantic Interpretation Learning
Carolyn P. Rose
1st Meeting of the North American Chapter of the Association for Computational Linguistics

1998

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An Interactive Domain Independent Approach to Robust Dialogue Interpretation
Carolyn Penstein Rose | Lori S. Levin
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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An Interactive Domain Independent Approach to Robust Dialogue Interpretation
Carolyn Penstein Rose | Lori S. Levin
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

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An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process
Carolyn Penstein Rose | Alon Lavie
Second Conference on Empirical Methods in Natural Language Processing

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An Efficient Two Stage Approach to Robust Language Interpretation
Carolyn Penstein Rose
Fifth Conference on Applied Natural Language Processing: Descriptions of System Demonstrations and Videos

1996

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Using Discourse Predictions for Ambiguity Resolution
Yan Qu | Carolyn P. Rose | Barbara Di Eugenio
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

1995

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Discourse Processing of Dialogues with Multiple Threads
Carolyn Penstein Rosé | Barbara Di Eugenio | Lori S. Levin | Carol Van Ess-Dykema
33rd Annual Meeting of the Association for Computational Linguistics

1994

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Recovering From Parser Failures: A Hybrid Statistical/Symbolic Approach
Carolyn Penstein Rose | Alex Waibel
The Balancing Act: Combining Symbolic and Statistical Approaches to Language

1993

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Recent Advances in Janus: A Speech Translation System
M. Woszczyna | N. Coccaro | A. Eisele | A. Lavie | A. McNair | T. Polzin | I. Rogina | C. P. Rose | T. Sloboda | M. Tomita | J. Tsutsumi | N. Aoki-Waibel | A. Waibel | W. Ward
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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