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Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
2020
IEEE Access
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INDEX TERMS Open-domain textual question answering, deep learning, machine reading comprehension, information retrieval. ...
Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years. ...
. • SQuAD-open [37] is an open-domain textual question answering dataset based on SQuAD [13] . ...
doi:10.1109/access.2020.2988903
fatcat:po4euxfronf3pob52qc2wcgrre
R^3: Reinforced Reader-Ranker for Open-Domain Question Answering
[article]
2017
arXiv
pre-print
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Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning. ...
an answer to the question. ...
Then the Ranker and Reader are trained jointly using reinforcement learning to directly optimize the expectation of extracting the groundtruth answer from the retrieved passages. ...
arXiv:1709.00023v2
fatcat:t52l2rn2mrbfdinouv5vupof7q
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?
[article]
2022
arXiv
pre-print
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Using these detected entities, the visual questions can be rewritten so as to be answerable by open domain QA systems. ...
This allows for the reuse of existing text-based Open Domain Question Answering (QA) Systems for visual question answering. ...
Using these detected entities, the visual questions can be rewritten so as to be answerable by open domain QA systems. ...
arXiv:2202.04306v1
fatcat:ycnfaljzkfcltggmwrzcbjdite
R3: Reinforced Ranker-Reader for Open-Domain Question Answering
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. ...
an answer to the question. ...
A successful open-domain QA system must be able to effectively retrieve and comprehend one or more knowledge sources to infer a correct answer. ...
doi:10.1609/aaai.v32i1.12053
fatcat:m7mrikgvxvg5rccfvn5qzhw3nq
Reinforcement Learning for Improved Low Resource Dialogue Generation
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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The aim is to create a unified approach to dialogue generation inspired by developments in both goal oriented and open ended dialogue systems. ...
Learning methods for domain adaptation in goal oriented dialogue and 3) Introducing models that can adapt cross lingually. ...
Currently, we are using this model to train on N number of domains and adapt this model to new domains for which we don't have turn level supervision using Reinforcement Learning. ...
doi:10.1609/aaai.v33i01.33019884
fatcat:2a6i6lx7hvav7gzf57vpgnoita
Advances in Natural Language Question Answering: A Review
[article]
2019
arXiv
pre-print
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This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges. ...
The dynamic nature of language has profited from the nonlinear learning in deep learning. This has created prominent success and a spike in work on question answering. ...
Though these systems were successful initiatives to the Question Answering domain they were not without flaws. ...
arXiv:1904.05276v1
fatcat:kgnbpaey45fwrloyzj5dzfvile
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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Generating intriguing question is a key step towards building human-like open-domain chatbots. ...
First, the coherence score between generated question and answer is used as the reward function in a reinforcement learning framework, to encourage the cases that are consistent with the answer in semantic ...
Acknowledgements The work was supported by the National Key R&D Program of China under grant 2018YF-B1004700, and National Natural Science Foundation of China (61872074, 61772122). ...
doi:10.18653/v1/d19-1511
dblp:conf/emnlp/WangFWZ19
fatcat:mmt6fxhcpngwne2dxmmc4lrsca
Learning strategies for story comprehension
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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This paper describes the use of machine learning to improve the performance of natural language question answering systems. ...
We compare our approach to three prior non-learning systems, and evaluate the conditions under which learning is effective. ...
Acknowledgements We gratefully acknowledge the helpful feedback provided by Dan Roth. This research was supported, in part, by ONR MURI grant N00014-00-1-0660. ...
doi:10.1145/1102351.1102384
dblp:conf/icml/GroisW05
fatcat:bfp5funvdvgtzc46ok52c5jhli
SSL-QA: Analysis of Semi-Supervised Learning for QuestionAnswering
2017
IOSR Journal of Computer Engineering
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Open domain natural language question answering (QA) is a process of automatically finding answers to questions searching collections of text files. ...
In this paper we analyse different intensive researches in semi-supervised learning for question-answering. ...
from standard transfer learning of a
question answering model trained on a large, span-level supervision. ...
doi:10.9790/0661-1903051415
fatcat:iv2qx3pejjf7fkt5rsewld7dky
Training a Ranking Function for Open-Domain Question Answering
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
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However, these systems perform poorly compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. ...
Additionally, we analyze the relative importance of semantic similarity and word level relevance matching in open-domain QA. ...
Acknowledgments PMH is funded as an AdeptMind Scholar. This project has benefited from financial support to SB by Google, Tencent Holdings, and Samsung Research. ...
doi:10.18653/v1/n18-4017
dblp:conf/naacl/HtutBC18
fatcat:ch5rnw7stjahvheaysebrl7kn4
Open-Domain Neural Conversational Agents: The Step Towards Artificial General Intelligence
2018
International Journal of Advanced Computer Science and Applications
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In order to create a conversational agent which is able to pass the Turing Test, numerous research efforts are focused on open-domain dialogue system. ...
This paper will present latest research in domain of Neural Network reasoning and logical association, sentiment analysis and real-time learning approaches applied to open domain neural conversational ...
Authors in [30] simultaneously train the model by alternating input data between question answering (QA) and question generation (QG), both in the same model. ...
doi:10.14569/ijacsa.2018.090654
fatcat:gs5reut56ze2nbykzoorbq3aou
Training a Ranking Function for Open-Domain Question Answering
[article]
2018
arXiv
pre-print
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However, these systems perform poorly compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. ...
Additionally, we analyze the relative importance of semantic similarity and word level relevance matching in open-domain QA. ...
Acknowledgments PMH is funded as an AdeptMind Scholar. This project has benefited from financial support to SB by Google, Tencent Holdings, and Samsung Research. ...
arXiv:1804.04264v1
fatcat:net73xus5be6hm2ukgpizrtpfu
Recent advances in conversational NLP : Towards the standardization of Chatbot building
[article]
2019
arXiv
pre-print
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Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building. ...
In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. ...
To train a dialogue system with reinforcement learning, the chatbot is put in use by the end users to become increasingly efficient throughout the conversations. ...
arXiv:1903.09025v1
fatcat:cocknrgvdvguvjykorzmeu5zse
Dialog as a Vehicle for Lifelong Learning
[article]
2020
arXiv
pre-print
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Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog ...
However, dialog interactions can also be used to obtain various types of knowledge that can be used to improve an underlying language understanding system, or other machine learning systems that the dialog ...
for an open vocabulary of objects. ...
arXiv:2006.14767v1
fatcat:dozixourvfha5ljs3y3hk4yaaq
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
[article]
2021
arXiv
pre-print
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Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. ...
We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. ...
Passage retrieval is an important step in open domain question answering, and is an active area of research to improve QA systems. ...
arXiv:2007.01282v2
fatcat:r7ycylnttvc43pm5gwltoggqbu
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