Eric Nyberg

Also published as: Eric H. Nyberg, Eric H. Nyberg III, Eric H. Nyberg, 3rd


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Flexible retrieval with NMSLIB and FlexNeuART
Leonid Boytsov | Eric Nyberg
Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

Our objective is to introduce to the NLP community NMSLIB, describe a new retrieval toolkit FlexNeuART, as well as their integration capabilities. NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions. Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances. FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals. FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB. In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.


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Bend but Don’t Break? Multi-Challenge Stress Test for QA Models
Hemant Pugaliya | James Route | Kaixin Ma | Yixuan Geng | Eric Nyberg
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years. Large scale datasets and focus on challenging linguistic phenomena have driven development in neural models, some of which have achieved parity with human performance in limited cases. However, an examination of state-of-the-art model output reveals that a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. We are interested in more clearly defining the strengths and limitations of leading models across diverse QA challenges, intending to help future researchers with identifying pathways to generalizable performance. We conduct extensive qualitative and quantitative analyses on the results of four models across four datasets and relate common errors to model capabilities. We also illustrate limitations in the datasets we examine and discuss a way forward for achieving generalizable models and datasets that broadly test QA capabilities.

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Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Kaixin Ma | Jonathan Francis | Quanyang Lu | Eric Nyberg | Alessandro Oltramari
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries. Recent approaches on such tasks show increased performance, only when models are either pre-trained with additional information or when domain-specific heuristics are used, without any special consideration regarding the knowledge resource type. In this paper, we perform a survey of recent commonsense QA methods and we provide a systematic analysis of popular knowledge resources and knowledge-integration methods, across benchmarks from multiple commonsense datasets. Our results and analysis show that attention-based injection seems to be a preferable choice for knowledge integration and that the degree of domain overlap, between knowledge bases and datasets, plays a crucial role in determining model success.

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Storyboarding of Recipes: Grounded Contextual Generation
Khyathi Chandu | Eric Nyberg | Alan W Black
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context. We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain. The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps. We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task. In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL). Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model. We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes. We also discuss analysis of the output highlighting key important NLP issues for prospective directions.

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Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
Hemant Pugaliya | Karan Saxena | Shefali Garg | Sheetal Shalini | Prashant Gupta | Eric Nyberg | Teruko Mitamura
Proceedings of the 18th BioNLP Workshop and Shared Task

Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets (similar to transfer learning). However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In this work, we introduce an end-to-end system, trained in a multi-task setting, to filter and re-rank answers in the medical domain. We use task-specific pre-trained models as deep feature extractors. Our model achieves the highest Spearman’s Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, on the ACL-BioNLP workshop MediQA Question Answering shared-task.

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Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Vinayshekhar Bannihatti Kumar | Ashwin Srinivasan | Aditi Chaudhary | James Route | Teruko Mitamura | Eric Nyberg
Proceedings of the 18th BioNLP Workshop and Shared Task

This paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.

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Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
Sai Abishek Bhaskar | Rashi Rungta | James Route | Eric Nyberg | Teruko Mitamura
Proceedings of the 18th BioNLP Workshop and Shared Task

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.


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Towards Inference-Oriented Reading Comprehension: ParallelQA
Soumya Wadhwa | Varsha Embar | Matthias Grabmair | Eric Nyberg
Proceedings of the Workshop on Generalization in the Age of Deep Learning

In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.

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BioAMA: Towards an End to End BioMedical Question Answering System
Vasu Sharma | Nitish Kulkarni | Srividya Pranavi | Gabriel Bayomi | Eric Nyberg | Teruko Mitamura
Proceedings of the BioNLP 2018 workshop

In this paper, we present a novel Biomedical Question Answering system, BioAMA: “Biomedical Ask Me Anything” on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed ‘ideal’ answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.

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Comparative Analysis of Neural QA models on SQuAD
Soumya Wadhwa | Khyathi Chandu | Eric Nyberg
Proceedings of the Workshop on Machine Reading for Question Answering

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.

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Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques
Khyathi Chandu | Ekaterina Loginova | Vishal Gupta | Josef van Genabith | Günter Neumann | Manoj Chinnakotla | Eric Nyberg | Alan W. Black
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

Code-Mixing (CM) is the phenomenon of alternating between two or more languages which is prevalent in bi- and multi-lingual communities. Most NLP applications today are still designed with the assumption of a single interaction language and are most likely to break given a CM utterance with multiple languages mixed at a morphological, phrase or sentence level. For example, popular commercial search engines do not yet fully understand the intents expressed in CM queries. As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian). We share the details of our data collection process, techniques which were used to avoid inducing lexical bias amongst the crowd workers and other CM specific linguistic properties of the dataset. Our final dataset, which is available freely for research purposes, has 1,694 Hinglish, 2,848 Tamlish and 1,391 Tenglish factoid questions and their answers. We discuss the techniques used by the participants for the first edition of this ongoing challenge.

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Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions
Yutong Li | Nicholas Gekakis | Qiuze Wu | Boyue Li | Khyathi Chandu | Eric Nyberg
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers. Biomedical Question Answering can automatically generate answers for a user’s topic or question, significantly reducing the effort required to locate the most relevant information in a large document corpus. Extractive summarization techniques, which concatenate the most relevant text units drawn from multiple documents, perform well on automatic evaluation metrics like ROUGE, but score poorly on human readability, due to the presence of redundant text and grammatical errors in the answer. This work moves toward abstractive summarization, which attempts to distill and present the meaning of the original text in a more coherent way. We incorporate a sentence fusion approach, based on Integer Linear Programming, along with three novel approaches for sentence ordering, in an attempt to improve the human readability of ideal answers. Using an open framework for configuration space exploration (BOOM), we tested over 2000 unique system configurations in order to identify the best-performing combinations for the sixth edition of Phase B of the BioASQ challenge.

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Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation
Ashwin Naresh Kumar | Harini Kesavamoorthy | Madhura Das | Pramati Kalwad | Khyathi Chandu | Teruko Mitamura | Eric Nyberg
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine. Biomedical Question Answering systems automatically identify the most relevant documents and pinpointed answers, given an information need expressed as a natural language question. Generating a non-redundant, human-readable summary that satisfies the information need of a given biomedical question is the focus of the Ideal Answer Generation task, part of the BioASQ challenge. This paper presents a system for ideal answer generation (using ontology-based retrieval and a neural learning-to-rank approach, combined with extractive and abstractive summarization techniques) which achieved the highest ROUGE score of 0.659 on the BioASQ 5b batch 2 test.


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Tackling Biomedical Text Summarization: OAQA at BioASQ 5B
Khyathi Chandu | Aakanksha Naik | Aditya Chandrasekar | Zi Yang | Niloy Gupta | Eric Nyberg
BioNLP 2017

In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data. We describe our techniques with an emphasis on ideal answer generation, where the goal is to produce a relevant, precise, non-redundant, query-oriented summary from multiple relevant documents. We make use of extractive summarization techniques to address this task and experiment with different biomedical ontologies and various algorithms including agglomerative clustering, Maximum Marginal Relevance (MMR) and sentence compression. We propose a novel word embedding based tf-idf similarity metric and a soft positional constraint which improve our system performance. We evaluate our techniques on test batch 4 from the fourth edition of the challenge. Our best system achieves a ROUGE-2 score of 0.6534 and ROUGE-SU4 score of 0.6536.

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Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models
Harsh Jhamtani | Varun Gangal | Eduard Hovy | Eric Nyberg
Proceedings of the Workshop on Stylistic Variation

Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action. To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text. Our methods are able to get a BLEU score of 31+, an improvement of ≈ 6 points above the strongest baseline. We publicly release our code to foster further research in this area.

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How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing
Abhilasha Ravichander | Thomas Manzini | Matthias Grabmair | Graham Neubig | Jonathan Francis | Eric Nyberg
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Building dialogue interfaces for real-world scenarios often entails training semantic parsers starting from zero examples. How can we build datasets that better capture the variety of ways users might phrase their queries, and what queries are actually realistic? Wang et al. (2015) proposed a method to build semantic parsing datasets by generating canonical utterances using a grammar and having crowdworkers paraphrase them into natural wording. A limitation of this approach is that it induces bias towards using similar language as the canonical utterances. In this work, we present a methodology that elicits meaningful and lexically diverse queries from users for semantic parsing tasks. Starting from a seed lexicon and a generative grammar, we pair logical forms with mixed text-image representations and ask crowdworkers to paraphrase and confirm the plausibility of the queries that they generated. We use this method to build a semantic parsing dataset from scratch for a dialog agent in a smart-home simulation. We find evidence that this dataset, which we have named SmartHome, is demonstrably more lexically diverse and difficult to parse than existing domain-specific semantic parsing datasets.

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Structural Embedding of Syntactic Trees for Machine Comprehension
Rui Liu | Junjie Hu | Wei Wei | Zi Yang | Eric Nyberg
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.

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Steering Output Style and Topic in Neural Response Generation
Di Wang | Nebojsa Jojic | Chris Brockett | Eric Nyberg
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selective-sampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.

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Charmanteau: Character Embedding Models For Portmanteau Creation
Varun Gangal | Harsh Jhamtani | Graham Neubig | Eduard Hovy | Eric Nyberg
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.


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Learning to Answer Biomedical Questions: OAQA at BioASQ 4B
Zi Yang | Yue Zhou | Eric Nyberg
Proceedings of the Fourth BioASQ workshop

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LAPPS/Galaxy: Current State and Next Steps
Nancy Ide | Keith Suderman | Eric Nyberg | James Pustejovsky | Marc Verhagen
Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSI/OIAF4HLT2016)

The US National Science Foundation (NSF) SI2-funded LAPPS/Galaxy project has developed an open-source platform for enabling complex analyses while hiding complexities associated with underlying infrastructure, that can be accessed through a web interface, deployed on any Unix system, or run from the cloud. It provides sophisticated tool integration and history capabilities, a workflow system for building automated multi-step analyses, state-of-the-art evaluation capabilities, and facilities for sharing and publishing analyses. This paper describes the current facilities available in LAPPS/Galaxy and outlines the project’s ongoing activities to enhance the framework.

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QGASP: a Framework for Question Generation Based on Different Levels of Linguistic Information
Hugo Patinho Rodrigues | Luísa Coheur | Eric Nyberg
Proceedings of the 9th International Natural Language Generation conference


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A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering
Di Wang | Eric Nyberg
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Metaphor Detection with Cross-Lingual Model Transfer
Yulia Tsvetkov | Leonid Boytsov | Anatole Gershman | Eric Nyberg | Chris Dyer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The Language Application Grid
Nancy Ide | James Pustejovsky | Christopher Cieri | Eric Nyberg | Di Wang | Keith Suderman | Marc Verhagen | Jonathan Wright
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The Language Application (LAPPS) Grid project is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing (NLP) components. It is based on the service-oriented architecture (SOA), a more recent, web-oriented version of the “pipeline” architecture that has long been used in NLP for sequencing loosely-coupled linguistic analyses. The LAPPS Grid provides access to basic NLP processing tools and resources and enables pipelining such tools to create custom NLP applications, as well as composite services such as question answering and machine translation together with language resources such as mono- and multi-lingual corpora and lexicons that support NLP. The transformative aspect of the LAPPS Grid is that it orchestrates access to and deployment of language resources and processing functions available from servers around the globe and enables users to add their own language resources, services, and even service grids to satisfy their particular needs.


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Assessing Benefit from Feature Feedback in Active Learning for Text Classification
Shilpa Arora | Eric Nyberg
Proceedings of the Fifteenth Conference on Computational Natural Language Learning


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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


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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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Interactive Annotation Learning with Indirect Feature Voting
Shilpa Arora | Eric Nyberg
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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Improving Text Retrieval Precision and Answer Accuracy in Question Answering Systems
Matthew Bilotti | Eric Nyberg
Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering

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Automatic Set Expansion for List Question Answering
Richard C. Wang | Nico Schlaefer | William W. Cohen | Eric Nyberg
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


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A Probabilistic Framework for Answer Selection in Question Answering
Jeongwoo Ko | Luo Si | Eric Nyberg
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Language-independent Probabilistic Answer Ranking for Question Answering
Jeongwoo Ko | Teruko Mitamura | Eric Nyberg
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics


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Evaluation for Scenario Question Answering Systems
Matthew W. Bilotti | Eric Nyberg
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Scenario Question Answering is a relatively new direction in Question Answering (QA) research that presents a number of challenges for evaluation. In this paper, we propose a comprehensive evaluation strategy for Scenario QA, including amethodology for building reusable test collections for Scenario QA and metrics for evaluating system performance over such test collections. Using this methodology, we have built a test collection, which we have made available for public download as a service to the research community. It is our hope that widespread availability of quality evaluation materials fuels research in new approaches to the Scenario QA task.

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Exploiting Multiple Semantic Resources for Answer Selection
Jeongwoo Ko | Laurie Hiyakumoto | Eric Nyberg
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the utility of semantic resources such as the Web, WordNet and gazetteers in the answer selection process for a question-answering system. In contrast with previous work using individual semantic resources to support answer selection, our work combines multiple resources to boost the confidence scores assigned to correct answers and evaluates different combination strategies based on unweighted sums, weighted linear combinations, and logistic regression. We apply our approach to select answers from candidates produced by three different extraction techniques of varying quality, focusing on TREC questions whose answers represent locations or proper-names. Our experimental results demonstrate that the combination of semantic resources is more effective than individual resources for all three extraction techniques, improving answer selection accuracy by as much as 32.35% for location questions and 72% for proper-name questions. Of the combination strategies tested, logistic regression models produced the best results for both location and proper-name questions.

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Analyzing the Effects of Spoken Dialog Systems on Driving Behavior
Jeongwoo Ko | Fumihiko Murase | Teruko Mitamura | Eric Nyberg | Masahiko Tateishi | Ichiro Akahori
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper presents an evaluation of a spoken dialog system for automotive environments. Our overall goal was to measure the impact of user-system interaction on the user’s driving performance, and to determine whether adding context-awareness to the dialog system might reduce the degree of user distraction during driving. To address this issue, we incorporated context-awareness into a spoken dialog system, and implemented three system features using user context, network context and dialog context. A series of experiments were conducted under three different configurations: driving without a dialog system, driving while using a context-aware dialog system, and driving while using a context-unaware dialog system. We measured the differences between the three configurations by comparing the average car speed, the frequency of speed changes and the angle between the car’s direction and the centerline on the road. These results indicate that context-awareness could reduce the degree of user distraction when using a dialog system during driving.


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Resource Analysis for Question Answering
Lucian Vlad Lita | Warren A. Hunt | Eric Nyberg
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Pronominal Anaphora Resolution for Unrestricted Text
Anna Kupść | Teruko Mitamura | Benjamin Van Durme | Eric Nyberg
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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An Information Repository Model for Advanced Question Answering Systems
Vasco Calais Pedro | Jeongwoo Ko | Eric Nyberg | Teruko Mitamura
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Correction Grammars for Error Handling in a Speech Dialog System
Hirohiko Sagawa | Teruko Mitamura | Eric Nyberg
Proceedings of HLT-NAACL 2004: Short Papers


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Towards light semantic processing for question answering
Benjamin Van Durme | Yifen Huang | Anna Kupść | Eric Nyberg
Proceedings of the HLT-NAACL 2003 Workshop on Text Meaning

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Diagnostics for interactive controlled language checking
Teruko Mitamura | Kathryn Baker | Eric Nyberg | David Svoboda
EAMT Workshop: Improving MT through other language technology tools: resources and tools for building MT

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JAVELIN: A Flexible, Planner-Based Architecture for Question Answering
Eric Nyberg | Robert Frederking
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations


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Design and Evolution of a Language Technologies Curriculum
Robert Frederking | Eric H. Nyberg | Teruko Mitamura | Jaime G. Carbonell
Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics


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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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KANT: Knowledge-Based, Accurate Natural Language Translation
Teruko Mitamura | Eric Nyberg | Jaime Carbonell
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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Coping With Ambiguity in a Large-Scale Machine Translation System
Kathryn L. Baker | Alexander M. Franz | Pamela W. Jordan | Teruko Mitamura | Eric H. Nyberg
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

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Evaluation Metrics for Knowledge-Based Machine Translation
Eric H. Nyberg, 3rd | Teruko Mitamura | Jaime G. Carbonell
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics


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The KANT System: Fast, Accurate, High-Quality Translation in Practical Domains
Eric H. Nyberg III | Teruko Mitamura
COLING 1992 Volume 3: The 15th International Conference on Computational Linguistics

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Hierarchical Lexical Structure and Interpretive Mapping in Machine Translation
Teruko Mitamura | Eric H. Nyberg III
COLING 1992 Volume 4: The 15th International Conference on Computational Linguistics