scholarly journals Automated Utterance Generation

2020 ◽  
Vol 34 (08) ◽  
pp. 13344-13349
Author(s):  
Soham Parikh ◽  
Quaizar Vohra ◽  
Mitul Tiwari
Keyword(s):  
Knowledge Base ◽  
Question Answering ◽  
Candidate Selection ◽  
Selection Algorithm ◽  
Generation System ◽  
Extractive Summarization ◽  
Utterance Generation ◽  
The Right

Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need for an automated utterance generation. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm.

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

scholarly journals A Generation System of Multiple Choice Questions Using Idioms with Multiple Component Keywords

2018 ◽  
Vol 7 (3.33) ◽  
pp. 257
Author(s):  
Jae-Young Lee ◽  
. .
Keyword(s):  
Correct Answer ◽  
Random Number ◽  
Multiple Choice ◽  
Random Numbers ◽  
Multiple Choice Questions ◽  
Generation System ◽  
Random Functions ◽  
Multiple Component ◽  
The Right

In order to alleviate the burden for the time-consuming and tedious tasks to make multiple choice questions, we proposed the system that generates multiple choice questions from the sentence with multiple component keywords and then relocates the questions selected by an array with random numbers instead of random functions in order to reduce the relocation time, after the system searches for the group of informative sentence with multiple component keywords by using special idioms. In this paper, the idiom is the CRm type idiom that has several components at the right side of this idiom including in a main informative sentence. The next sentences consist of other informative sentences including the components keywords. To make multiple choice questions, the system randomly selects an informative sentence including a component keyword and it also converts the informative sentence into a question. The selected component keyword is used as the correct answer and the three other component keywords are used as distractors. To produce many different questions about the same contents with different positions of the question and items, the system uses a random number array to reduce the relocation time.  

scholarly journals Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

2020 ◽  
Author(s):  
Yongrui Chen ◽  
Huiying Li ◽  
Yuncheng Hua ◽  
Guilin Qi
Keyword(s):  
Knowledge Base ◽  
Structure Prediction ◽  
Question Answering ◽  
State Transition ◽  
Knowledge Bases ◽  
Second Stage ◽  
Transition Strategy ◽  
Query Structure ◽  
Two Stages ◽  
Complex Question

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.

scholarly journals A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base

2021 ◽  
Author(s):  
Yu Feng ◽  
Jing Zhang ◽  
Gaole He ◽  
Wayne Xin Zhao ◽  
Lemao Liu ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Question Answering ◽  
Numerical Reasoning

Data mining for building knowledge bases: techniques, architectures and applications

2016 ◽  
Vol 31 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
John Calvo Martinez ◽  
Paul Compton
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Event Extraction ◽  
Data Sources ◽  
Small Scale ◽  
Knowledge Mining ◽  
Practical Applications ◽  
Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

Robust cross-lingual knowledge base question answering via knowledge distillation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shaofei Wang ◽  
Depeng Dang
Keyword(s):  
Knowledge Base ◽  
Design Methodology ◽  
Question Answering ◽  
Ground Truth ◽  
Student Model ◽  
Content Type ◽  
Final Performance ◽  
Knowledge Distillation ◽  
The Cross ◽  
Cross Lingual

PurposePrevious knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.Design/methodology/approachThe authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.FindingsThe experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.Originality/valueThe authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

A Review on Question Generation from Natural Language Text

2022 ◽  
Vol 40 (1) ◽  
pp. 1-43
Author(s):  
Ruqing Zhang ◽  
Jiafeng Guo ◽  
Lu Chen ◽  
Yixing Fan ◽  
Xueqi Cheng
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Augmentation ◽  
Text Structure ◽  
Current Status ◽  
Question Generation ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
The Right ◽  
Language Text

Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.

Predicting Lexical Answer Types in Open Domain QA

2012 ◽  
Vol 8 (3) ◽  
pp. 74-88 ◽  
Author(s):  
Alfio Massimiliano Gliozzo ◽  
Aditya Kalyanpur
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
General Purpose ◽  
Open Domain ◽  
Lexical Knowledge ◽  
Research Challenge ◽  
Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

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