Hal Daumé III

Also published as: Hal Daumé, Hal Daume, Hal Daume III


2019

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Controlling the Specificity of Clarification Question Generation
Yang Trista Cao | Sudha Rao | Hal Daumé III
Proceedings of the 2019 Workshop on Widening NLP

Unlike comprehension-style questions, clarification questions look for some missing information in a given context. However, without guidance, neural models for question generation, similar to dialog generation models, lead to generic and bland questions that cannot elicit useful information. We argue that controlling the level of specificity of the generated questions can have useful applications and propose a neural clarification question generation model for the same. We first train a classifier that annotates a clarification question with its level of specificity (generic or specific) to the given context. Our results on the Amazon questions dataset demonstrate that training a clarification question generation model on specificity annotated data can generate questions with varied levels of specificity to the given context.

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Non-Monotonic Sequential Text Generation
kiante brantley | Kyunghyun Cho | Hal Daumé | Sean Welleck
Proceedings of the 2019 Workshop on Widening NLP

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.

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Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning
Khanh Nguyen | Hal Daumé III
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop “Help, Anna!” (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments.

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Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
Elissa Redmiles | Lisa Maszkiewicz | Emily Hwang | Dhruv Kuchhal | Everest Liu | Miraida Morales | Denis Peskov | Sudha Rao | Rock Stevens | Kristina Gligorić | Sean Kross | Michelle Mazurek | Hal Daumé III
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The readability of a digital text can influence people’s ability to learn new things about a range topics from digital resources (e.g., Wikipedia, WebMD). Readability also impacts search rankings, and is used to evaluate the performance of NLP systems. Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. In this work, we present a comparison of the validity of well-known readability measures and introduce a novel approach, Smart Cloze, which is designed to address shortcomings of existing measures. We compare these approaches across four different corpora: crowdworker-generated stories, Wikipedia articles, security and privacy advice, and health information. On these corpora, we evaluate the convergent and content validity of each measure, and detail tradeoffs in score precision, domain-specificity, and participant burden. These results provide a foundation for more accurate readability measurements and better evaluation of new natural-language-processing systems and tools.

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Global Voices: Crossing Borders in Automatic News Summarization
Khanh Nguyen | Hal Daumé III
Proceedings of the 2nd Workshop on New Frontiers in Summarization

We construct Global Voices, a multilingual dataset for evaluating cross-lingual summarization methods. We extract social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. Especially, for the into-English summarization task, we crowd-source a high-quality evaluation dataset based on guidelines that emphasize accuracy, coverage, and understandability. To ensure the quality of this dataset, we collect human ratings to filter out bad summaries, and conduct a survey on humans, which shows that the remaining summaries are preferred over the social-network summaries. We study the effect of translation quality in cross-lingual summarization, comparing a translate-then-summarize approach with several baselines. Our results highlight the limitations of the ROUGE metric that are overlooked in monolingual summarization.

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Answer-based Adversarial Training for Generating Clarification Questions
Sudha Rao | Hal Daumé III
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 present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarification questions. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.

2018

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Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Han-Chin Shing | Suraj Nair | Ayah Zirikly | Meir Friedenberg | Hal Daumé III | Philip Resnik
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology.

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Content Selection in Deep Learning Models of Summarization
Chris Kedzie | Kathleen McKeown | Hal Daumé III
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the sumarization task.

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Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Sudha Rao | Hal Daumé III
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.

2017

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Biomedical Event Extraction using Abstract Meaning Representation
Sudha Rao | Daniel Marcu | Kevin Knight | Hal Daumé III
BioNLP 2017

We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.

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Structured Prediction via Learning to Search under Bandit Feedback
Amr Sharaf | Hal Daumé III
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

We present an algorithm for structured prediction under online bandit feedback. The learner repeatedly predicts a sequence of actions, generating a structured output. It then observes feedback for that output and no others. We consider two cases: a pure bandit setting in which it only observes a loss, and more fine-grained feedback in which it observes a loss for every action. We find that the fine-grained feedback is necessary for strong empirical performance, because it allows for a robust variance-reduction strategy. We empirically compare a number of different algorithms and exploration methods and show the efficacy of BLS on sequence labeling and dependency parsing tasks.

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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Amr Sharaf | Shi Feng | Khanh Nguyen | Kianté Brantley | Hal Daumé III
Proceedings of the Second Conference on Machine Translation

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Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
Emily Bender | Hal Daumé III | Allyson Ettinger | Sudha Rao
Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems

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Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
Allyson Ettinger | Sudha Rao | Hal Daumé III | Emily M. Bender
Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems

This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a carefully designed shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.

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Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Khanh Nguyen | Hal Daumé III | Jordan Boyd-Graber
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.

2016

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Learning Text Pair Similarity with Context-sensitive Autoencoders
Hadi Amiri | Philip Resnik | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the Workshop on Human-Computer Question Answering
Mohit Iyyer | He He | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the Workshop on Human-Computer Question Answering

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The UMD CLPsych 2016 Shared Task System: Text Representation for Predicting Triage of Forum Posts about Mental Health
Meir Friedenberg | Hadi Amiri | Hal Daumé III | Philip Resnik
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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A Framework for Discriminative Rule Selection in Hierarchical Moses
Fabienne Braune | Alexander Fraser | Hal Daumé III | Aleš Tamchyna
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search
Sudha Rao | Yogarshi Vyas | Hal Daumé III | Philip Resnik
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation
He He | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships
Mohit Iyyer | Anupam Guha | Snigdha Chaturvedi | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Syntax-based Rewriting for Simultaneous Machine Translation
He He | Alvin Grissom II | John Morgan | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Why discourse affects speakers’ choice of referring expressions
Naho Orita | Eliana Vornov | Naomi Feldman | Hal Daumé III
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)

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Deep Unordered Composition Rivals Syntactic Methods for Text Classification
Mohit Iyyer | Varun Manjunatha | Jordan Boyd-Graber | Hal Daumé III
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)

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Dialogue focus tracking for zero pronoun resolution
Sudha Rao | Allyson Ettinger | Hal Daumé III | Philip Resnik
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Hands-on Learning to Search for Structured Prediction
Hal Daumé III | John Langford | Kai-Wei Chang | He He | Sudha Rao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

2014

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Understanding MOOC Discussion Forums using Seeded LDA
Arti Ramesh | Dan Goldwasser | Bert Huang | Hal Daumé | Lise Getoor
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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A Neural Network for Factoid Question Answering over Paragraphs
Mohit Iyyer | Jordan Boyd-Graber | Leonardo Claudino | Richard Socher | Hal Daumé III
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Don’t Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation
Alvin Grissom II | He He | Jordan Boyd-Graber | John Morgan | Hal Daumé III
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation
Junhui Li | Yuval Marton | Philip Resnik | Hal Daumé III
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Predicting Instructor’s Intervention in MOOC forums
Snigdha Chaturvedi | Dan Goldwasser | Hal Daumé III
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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I Object!” Modeling Latent Pragmatic Effects in Courtroom Dialogues
Dan Goldwasser | Hal Daumé III
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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Measuring Machine Translation Errors in New Domains
Ann Irvine | John Morgan | Marine Carpuat | Hal Daumé III | Dragos Munteanu
Transactions of the Association for Computational Linguistics, Volume 1

We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a Parliament-trained phrase-based machine translation system is applied in four very different domains: news, medical texts, scientific articles and movie subtitles. We present quantitative and qualitative experiments that highlight opportunities for future research in domain adaptation for machine translation.

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Monolingual Marginal Matching for Translation Model Adaptation
Ann Irvine | Chris Quirk | Hal Daumé III
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Dynamic Feature Selection for Dependency Parsing
He He | Hal Daumé III | Jason Eisner
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Lucy Vanderwende | Hal Daumé III | Katrin Kirchhoff
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Modeling Syntactic and Semantic Structures in Hierarchical Phrase-based Translation
Junhui Li | Philip Resnik | Hal Daumé III
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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SenseSpotting: Never let your parallel data tie you to an old domain
Marine Carpuat | Hal Daumé III | Katharine Henry | Ann Irvine | Jagadeesh Jagarlamudi | Rachel Rudinger
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Proceedings of the First Workshop on Multilingual Modeling
Jagadeesh Jagarlamudi | Sujith Ravi | Xiaojun Wan | Hal Daume III
Proceedings of the First Workshop on Multilingual Modeling

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Incorporating Lexical Priors into Topic Models
Jagadeesh Jagarlamudi | Hal Daumé III | Raghavendra Udupa
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Midge: Generating Image Descriptions From Computer Vision Detections
Margaret Mitchell | Jesse Dodge | Amit Goyal | Kota Yamaguchi | Karl Stratos | Xufeng Han | Alyssa Mensch | Alex Berg | Tamara Berg | Hal Daumé III
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Low-Dimensional Discriminative Reranking
Jagadeesh Jagarlamudi | Hal Daumé III
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Detecting Visual Text
Jesse Dodge | Amit Goyal | Xufeng Han | Alyssa Mensch | Margaret Mitchell | Karl Stratos | Kota Yamaguchi | Yejin Choi | Hal Daumé III | Alex Berg | Tamara Berg
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Regularized Interlingual Projections: Evaluation on Multilingual Transliteration
Jagadeesh Jagarlamudi | Hal Daumé III
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Fast Large-Scale Approximate Graph Construction for NLP
Amit Goyal | Hal Daumé III | Raul Guerra
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Sketch Algorithms for Estimating Point Queries in NLP
Amit Goyal | Hal Daumé III | Graham Cormode
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Besting the Quiz Master: Crowdsourcing Incremental Classification Games
Jordan Boyd-Graber | Brianna Satinoff | He He | Hal Daumé III
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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From Bilingual Dictionaries to Interlingual Document Representations
Jagadeesh Jagarlamudi | Hal Daumé III | Raghavendra Udupa
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Domain Adaptation for Machine Translation by Mining Unseen Words
Hal Daumé III | Jagadeesh Jagarlamudi
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Beyond Structured Prediction: Inverse Reinforcement Learning
Hal Daumé III
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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Approximate Scalable Bounded Space Sketch for Large Data NLP
Amit Goyal | Hal Daumé III
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Corpus-Guided Sentence Generation of Natural Images
Yezhou Yang | Ching Teo | Hal Daumé III | Yiannis Aloimonos
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Improving Bilingual Projections via Sparse Covariance Matrices
Jagadeesh Jagarlamudi | Raghavendra Udupa | Hal Daumé III | Abhijit Bhole
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Generating Semantic Orientation Lexicon using Large Data and Thesaurus
Amit Goyal | Hal Daumé
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

2010

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Automatically Producing Plot Unit Representations for Narrative Text
Amit Goyal | Ellen Riloff | Hal Daumé III
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Domain Adaptation meets Active Learning
Piyush Rai | Avishek Saha | Hal Daumé | Suresh Venkatasubramanian
Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing

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Toward Plot Units: Automatic Affect State Analysis
Amit Goyal | Ellen Riloff | Hal Daume III | Nathan Gilbert
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text

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Sketching Techniques for Large Scale NLP
Amit Goyal | Jagadeesh Jagarlamudi | Hal Daumé III | Suresh Venkatasubramanian
Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop

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Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Hal Daumé III | Tejaswini Deoskar | David McClosky | Barbara Plank | Jörg Tiedemann
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing

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Frustratingly Easy Semi-Supervised Domain Adaptation
Hal Daumé III | Abhishek Kumar | Avishek Saha
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing

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Sketch Techniques for Scaling Distributional Similarity to the Web
Amit Goyal | Jagadeesh Jagarlamudi | Hal Daumé III | Suresh Venkatasubramanian
Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics

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From Structured Prediction to Inverse Reinforcement Learning
Hal Daumé III
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2009

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Markov Random Topic Fields
Hal Daumé III
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Streaming for large scale NLP: Language Modeling
Amit Goyal | Hal Daumé III | Suresh Venkatasubramanian
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Non-Parametric Bayesian Areal Linguistics
Hal Daumé III
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2008

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Cross-Task Knowledge-Constrained Self Training
Hal Daumé III
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Name Translation in Statistical Machine Translation - Learning When to Transliterate
Ulf Hermjakob | Kevin Knight | Hal Daumé III
Proceedings of ACL-08: HLT

2007

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A Bayesian Model for Discovering Typological Implications
Hal Daumé III | Lyle Campbell
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Frustratingly Easy Domain Adaptation
Hal Daumé III
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Beyond EM: Bayesian Techniques for Human Language Technology Researchers
Hal Daume III
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts

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Bayesian Query-Focused Summarization
Hal Daumé III | Daniel Marcu
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Ryan McDonald | Charles Sutton | Hal Daumé III | Andrew McCallum | Fernando Pereira | Jeff Bilmes
Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing

2005

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A Large-Scale Exploration of Effective Global Features for a Joint Entity Detection and Tracking Model
Hal Daumé III | Daniel Marcu
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Induction of Word and Phrase Alignments for Automatic Document Summarization
Hal Daumé III | Daniel Marcu
Computational Linguistics, Volume 31, Number 4, December 2005

2004

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Web Search Intent Induction via Automatic Query Reformulation
Hal Daumé III | Eric Brill
Proceedings of HLT-NAACL 2004: Short Papers

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Generic Sentence Fusion is an Ill-Defined Summarization Task
Hal Daume III | Daniel Marcu
Text Summarization Branches Out

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A Phrase-Based HMM Approach to Document/Abstract Alignment
Hal Daumé III | Daniel Marcu
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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NP Bracketing by Maximum Entropy Tagging and SVM Reranking
Hal Daumé III | Daniel Marcu
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

2002

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The Importance of Lexicalized Syntax Models for Natural Language Generation Tasks
Hal Daume III | Kevin Knight | Irene Langkilde-Geary | Daniel Marcu | Kenji Yamada
Proceedings of the International Natural Language Generation Conference

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A Noisy-Channel Model for Document Compression
Hal Daume III | Daniel Marcu
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

2001

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Integrated Information Management: An Interactive, Extensible Architecture for Information Retrieval
Eric Nyberg | Hal Daume
Proceedings of the First International Conference on Human Language Technology Research

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