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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (45)
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Hits 1 – 20 of 45
1
Blindness to Modality Helps Entailment Graph Mining ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Mark
;
Bijl de Vroe, Sander
. - : Underline Science Inc., 2021
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2
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chen, Xingyu
. - : Underline Science Inc., 2021
BASE
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3
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Wang, Gengyu
. - : Underline Science Inc., 2021
BASE
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4
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Baylon, Julia
;
Han, Rujun
. - : Underline Science Inc., 2021
BASE
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5
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Piyush
;
Akula, Arjun
;
Changpinyo, Soravit
;
Gong, Boqing
;
Soricut, Radu
;
Zhu, Song-Chun
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.164/ Abstract: One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role. In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse questionanswer pairs with the desired dataset style. We use it to create CrossVQA, a collection of test splits for assessing VQA generalization based on the VQA2, VizWiz, and Open Images datasets. We provide an analysis of our generated datasets and demonstrate its utility by using them to evaluate several state-of-theart VQA systems. One important finding is that the visual shifts in cross-dataset VQA matter more than the language shifts. More broadly, we ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural language generation
;
Natural Language Processing
;
Question-Answering Systems
URL:
https://underline.io/lecture/37935-crossvqa-scalably-generating-benchmarks-for-systematically-testing-vqa-generalization
https://dx.doi.org/10.48448/yxhj-5g43
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6
Foreseeing the Benefits of Incidental Supervision ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Dan
;
He, Hangfeng
. - : Underline Science Inc., 2021
BASE
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7
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Lewis, Patrick
. - : Underline Science Inc., 2021
BASE
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8
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ju, Yiming
;
Zhang, Yuanzhe
. - : Underline Science Inc., 2021
BASE
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9
Mapping probability word problems to executable representations ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Daelemans, Walter
;
Davis, Jesse
. - : Underline Science Inc., 2021
BASE
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10
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Feuerriegel, Stefan
;
Kratzwald, Bernhard
. - : Underline Science Inc., 2021
BASE
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11
Smoothing Dialogue States for Open Conversational Machine Reading ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ouyang, Siru
;
Zhang, Zhuosheng
. - : Underline Science Inc., 2021
BASE
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12
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Jiang, Zhengbao
. - : Underline Science Inc., 2021
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13
Evaluation Paradigms in Question Answering ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Jordan
;
Rodriguez, Pedro
. - : Underline Science Inc., 2021
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14
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Srinivasan
;
Ghoshal, Asish
. - : Underline Science Inc., 2021
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15
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bartolo, Max
;
Jia, Robin
. - : Underline Science Inc., 2021
BASE
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16
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Belfer, Robert
;
Kulshreshtha, Devang
. - : Underline Science Inc., 2021
BASE
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17
Zero-Shot Dialogue State Tracking via Cross-Task Transfer ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cho, Eunjoon
;
Crook, Paul
. - : Underline Science Inc., 2021
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18
Case-based Reasoning for Natural Language Queries over Knowledge Bases ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Das, Rajarshi
;
Godbole, Ameya
. - : Underline Science Inc., 2021
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19
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Garg, Siddhant
;
Moschitti, Alessandro
. - : Underline Science Inc., 2021
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20
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Rebuffel, Clément
. - : Underline Science Inc., 2021
BASE
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