DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...8
Hits 1 – 20 of 151

1
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
Eskander, Ramy. - 2021
BASE
Show details
2
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ...
Eskander, Ramy. - : Columbia University, 2021
BASE
Show details
3
Blindness to Modality Helps Entailment Graph Mining ...
BASE
Show details
4
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
BASE
Show details
5
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
BASE
Show details
6
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
BASE
Show details
7
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
BASE
Show details
8
Foreseeing the Benefits of Incidental Supervision ...
BASE
Show details
9
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them ...
BASE
Show details
10
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.295/ Abstract: Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines' ability to understand human language. Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format. Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at the prediction. One of the most important interpretation forms is to attribute model decisions to input features. Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiple-choice MRC and improve the model by punishing the illogical attributions. Our method can improve model performance without any external information and model structure change. Furthermore, we also analyze how and why such a self-training method works. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems
URL: https://underline.io/lecture/37623-enhancing-multiple-choice-machine-reading-comprehension-by-punishing-illogical-interpretations
https://dx.doi.org/10.48448/tqtj-m094
BASE
Hide details
11
Mapping probability word problems to executable representations ...
BASE
Show details
12
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora ...
BASE
Show details
13
Smoothing Dialogue States for Open Conversational Machine Reading ...
BASE
Show details
14
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering ...
BASE
Show details
15
Evaluation Paradigms in Question Answering ...
BASE
Show details
16
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation ...
BASE
Show details
17
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
BASE
Show details
18
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval ...
BASE
Show details
19
Zero-Shot Dialogue State Tracking via Cross-Task Transfer ...
BASE
Show details
20
Case-based Reasoning for Natural Language Queries over Knowledge Bases ...
BASE
Show details

Page: 1 2 3 4 5...8

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
3
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
148
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern