Search Results for author: Eric Nyberg

Found 41 papers, 13 papers with code

Table Retrieval May Not Necessitate Table-specific Model Design

1 code implementation19 May 2022 Zhiruo Wang, Zhengbao Jiang, Eric Nyberg, Graham Neubig

In this work, we focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?"

Hard Attention Question Answering

Safe Autonomous Racing via Approximate Reachability on Ego-vision

no code implementations14 Oct 2021 Bingqing Chen, Jonathan Francis, Jean Oh, Eric Nyberg, Sylvia L. Herbert

Given the nature of the task, autonomous agents need to be able to 1) identify and avoid unsafe scenarios under the complex vehicle dynamics, and 2) make sub-second decision in a fast-changing environment.

Autonomous Driving Safe Reinforcement Learning

Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation

no code implementations29 Sep 2021 Gyan Tatiya, Jonathan Francis, Ingrid Navarro, Nariaki Kitamura, Eric Nyberg, Jivko Sinapov, Jean Oh

We define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects.

Visual Navigation

Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

1 code implementation ICCV 2021 James Herman, Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, Eric Nyberg

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.

Autonomous Driving Trajectory Prediction

Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering

1 code implementation7 Nov 2020 Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, Alessandro Oltramari

Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models.

Language Modelling Question Answering

Flexible retrieval with NMSLIB and FlexNeuART

2 code implementations EMNLP (NLPOSS) 2020 Leonid Boytsov, Eric Nyberg

Our objective is to introduce to the NLP community an existing k-NN search library NMSLIB, a new retrieval toolkit FlexNeuART, as well as their integration capabilities.

Re-Ranking

Bend but Don't Break? Multi-Challenge Stress Test for QA Models

no code implementations WS 2019 Hemant Pugaliya, James Route, Kaixin Ma, Yixuan Geng, Eric Nyberg

The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years.

Question Answering

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

no code implementations WS 2019 Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg, Alessandro Oltramari

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.

Common Sense Reasoning Question Answering

Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs

no code implementations8 Oct 2019 Leonid Boytsov, Eric Nyberg

We demonstrate that a graph-based search algorithm-relying on the construction of an approximate neighborhood graph-can directly work with challenging non-metric and/or non-symmetric distances without resorting to metric-space mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation.

graph construction

Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study

no code implementations8 Oct 2019 Leonid Boytsov, Eric Nyberg

We consider two known data-driven approaches to extend these rules to non-metric spaces: TriGen and a piece-wise linear approximation of the pruning rule.

Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions

no code implementations WS 2018 Yutong Li, Nicholas Gekakis, Qiuze Wu, Boyue Li, Ch, Khyathi u, Eric Nyberg

The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers.

Abstractive Text Summarization Answer Generation +4

Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation

no code implementations WS 2018 Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Ch, Khyathi u, Teruko Mitamura, Eric Nyberg

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.

Abstractive Text Summarization Answer Generation +3

BioAMA: Towards an End to End BioMedical Question Answering System

no code implementations WS 2018 Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, Teruko Mitamura

In this paper, we present a novel Biomedical Question Answering system, BioAMA: {``}Biomedical Ask Me Anything{''} on task 5b of the annual BioASQ challenge.

Natural Language Inference NER +3

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations WS 2018 Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

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).

Question Answering

Comparative Analysis of Neural QA models on SQuAD

no code implementations WS 2018 Soumya Wadhwa, Khyathi Raghavi Chandu, Eric Nyberg

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language.

Information Retrieval Question Answering +1

Towards Inference-Oriented Reading Comprehension: ParallelQA

no code implementations WS 2018 Soumya Wadhwa, Varsha Embar, Matthias Grabmair, Eric Nyberg

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.

Machine Reading Comprehension

CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System

no code implementations15 Nov 2017 Yuan Yang, Jingcheng Yu, Ye Hu, Xiaoyao Xu, Eric Nyberg

In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question.

Answer Generation General Classification +2

Steering Output Style and Topic in Neural Response Generation

1 code implementation EMNLP 2017 Di Wang, Nebojsa Jojic, Chris Brockett, Eric Nyberg

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation.

Response Generation Text Generation

Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models

1 code implementation WS 2017 Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg

Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.

How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing

no code implementations WS 2017 Ravich, Abhilasha er, Thomas Manzini, Matthias Grabmair, Graham Neubig, Jonathan Francis, Eric Nyberg

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.

Semantic Parsing

Tackling Biomedical Text Summarization: OAQA at BioASQ 5B

no code implementations WS 2017 Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg

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.

Answer Generation Extractive Summarization +2

Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

2 code implementations4 Jul 2017 Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg

Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.

Structural Embedding of Syntactic Trees for Machine Comprehension

no code implementations EMNLP 2017 Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg

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.

Question Answering Reading Comprehension

LAPPS/Galaxy: Current State and Next Steps

no code implementations WS 2016 Nancy Ide, Keith Suderman, Eric Nyberg, James Pustejovsky, Marc Verhagen

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.

Permutation Search Methods are Efficient, Yet Faster Search is Possible

1 code implementation10 Jun 2015 Bilegsaikhan Naidan, Leonid Boytsov, Eric Nyberg

The underpinning assumption is that, for both metric and non-metric spaces, the distance between permutations is a good proxy for the distance between original points.

The Language Application Grid

no code implementations LREC 2014 Nancy Ide, James Pustejovsky, Christopher Cieri, Eric Nyberg, Di Wang, Keith Suderman, Marc Verhagen, Jonathan Wright

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.

Machine Translation Natural Language Processing +2

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