Text-to-Text Generation for Question Answering

2011 ◽  
pp. 117-145
Author(s):  
Wauter Bosma ◽  
Erwin Marsi ◽  
Emiel Krahmer ◽  
Mariët Theune
Keyword(s):  
Question Answering ◽  
Text Generation

scholarly journals End-to-End Argumentation Knowledge Graph Construction

2020 ◽  
Vol 34 (05) ◽  
pp. 7367-7374
Author(s):  
Khalid Al-Khatib ◽  
Yufang Hou ◽  
Henning Wachsmuth ◽  
Charles Jochim ◽  
Francesca Bonin ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Knowledge Graph ◽  
Exploratory Search ◽  
Text Generation ◽  
Fake News ◽  
High Quality ◽  
Web Based ◽  
Knowledge Graphs ◽  
End To End

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

scholarly journals A Survey of Paraphrasing and Textual Entailment Methods

2010 ◽  
Vol 38 ◽  
pp. 135-187 ◽  
Author(s):  
I. Androutsopoulos ◽  
P. Malakasiotis
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Question Answering ◽  
Extraction Methods ◽  
The Other ◽  
Text Generation ◽  
Wide Range ◽  
Textual Entailment

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

scholarly journals Generating Reasonable Legal Text through the Combination of Language Modeling and Question Answering

2020 ◽  
Author(s):  
Weijing Huang ◽  
Xianfeng Liao ◽  
Zhiqiang Xie ◽  
Jiang Qian ◽  
Bojin Zhuang ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Question Answering ◽  
Language Modeling ◽  
Legal Knowledge ◽  
Text Generation ◽  
Legal Text ◽  
Legal Documents ◽  
Novel Method ◽  
The Law

Due to the improvement of Language Modeling, the emerging NLP assistant tools aiming for text generation greatly reduce the human workload on writing documents. However, the generation of legal text faces greater challenges than ordinary texts because of its high requirement for keeping logic reasonable, which can not be guaranteed by Language Modeling right now. To generate reasonable legal documents, we propose a novel method CoLMQA, which (1) combines Language Modeling and Question Answering, (2) generates text with slots by Language Modeling, and (3) fills the slots by our proposed Question Answering method named Transformer-based Key-Value Memory Networks. In CoLMQA, the slots represent the text part that needs to be highly constrained by logic, such as the name of the law and the number of the law article. And the Question Answering fills the slots in context with the help of Legal Knowledge Base to keep logic reasonable. The experiment verifies the quality of legal documents generated by CoLMQA, surpassing the documents generated by pure Language Modeling.

scholarly journals A Compression-Compilation Framework for On-mobile Real-time BERT Applications

2021 ◽  
Author(s):  
Wei Niu ◽  
Zhenglun Kong ◽  
Geng Yuan ◽  
Weiwen Jiang ◽  
Jiexiong Guan ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Language Processing ◽  
Question Answering ◽  
Optimization Method ◽  
High Accuracy ◽  
Text Generation ◽  
Learning Models ◽  
Neural Architecture ◽  
Architecture Optimization

Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model meets both resource and real-time specifications of mobile devices. Our framework applies a compiler-aware neural architecture optimization method (CANAO), which can generate the optimal compressed model that balances both accuracy and latency. We are able to achieve up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss. We present two types of BERT applications on mobile devices: Question Answering (QA) and Text Generation. Both can be executed in real-time with latency as low as 45ms. Videos for demonstrating the framework can be found on https://www.youtube.com/watch?v=_WIRvK_2PZI

The Computational Sublime in Nick Montfort's ‘Round’ and ‘All the Names of God’

CounterText ◽  
2015 ◽  
Vol 1 (3) ◽  
pp. 348-365 ◽  
Author(s):  
Mario Aquilina
Keyword(s):  
Computer Code ◽  
Computer Programs ◽  
Literary Text ◽  
Text Generation ◽  
Mathematical Concepts ◽  
Literary Space

What if the post-literary also meant that which operates in a literary space (almost) devoid of language as we know it: for instance, a space in which language simply frames the literary or poetic rather than ‘containing’ it? What if the countertextual also meant the (en)countering of literary text with non-textual elements, such as mathematical concepts, or with texts that we would not normally think of as literary, such as computer code? This article addresses these issues in relation to Nick Montfort's #!, a 2014 print collection of poems that presents readers with the output of computer programs as well as the programs themselves, which are designed to operate on principles of text generation regulated by specific constraints. More specifically, it focuses on two works in the collection, ‘Round’ and ‘All the Names of God’, which are read in relation to the notions of the ‘computational sublime’ and the ‘event’.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Automated question answering in Webclopedia

2002 ◽  
Author(s):  
Ulf Hermjakob ◽  
Eduard Hovy ◽  
Chin-Yew Lin
Keyword(s):  
Question Answering
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