scholarly journals Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8439
Author(s):  
Shukan Liu ◽  
Ruilin Xu ◽  
Li Duan ◽  
Mingjie Li ◽  
Yiming Liu
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Weighted Graph ◽  
Knowledge Bases ◽  
Language Models ◽  
Main Stream ◽  
Semantic Features ◽  
Open Domain ◽  
Original Graph

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build a metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then, a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

scholarly journals Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

2021 ◽  
Author(s):  
Shukan Liu ◽  
Ruilin Xu ◽  
Li Duan ◽  
Mingjie Li ◽  
Yiming Liu
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Weighted Graph ◽  
Knowledge Bases ◽  
Language Models ◽  
Main Stream ◽  
Semantic Features ◽  
Open Domain ◽  
Original Graph

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

scholarly journals Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

2021 ◽  
Author(s):  
Yuanmeng Yan ◽  
Rumei Li ◽  
Sirui Wang ◽  
Hongzhi Zhang ◽  
Zan Daoguang ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Language Models ◽  
Relation Learning

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.

scholarly journals UniMF: A Unified Framework to Incorporate Multimodal Knowledge Bases intoEnd-to-End Task-Oriented Dialogue Systems

2021 ◽  
Author(s):  
Shiquan Yang ◽  
Rui Zhang ◽  
Sarah M. Erfani ◽  
Jey Han Lau
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Language Models ◽  
Dialogue Systems ◽  
Unified Framework ◽  
Proposed Model ◽  
Multimodal Information ◽  
Task Oriented ◽  
Novel Model ◽  
Single Modality

Knowledge bases (KBs) are usually essential for building practical dialogue systems. Recently we have seen rapidly growing interest in integrating knowledge bases into dialogue systems. However, existing approaches mostly deal with knowledge bases of a single modality, typically textual information. As today's knowledge bases become abundant with multimodal information such as images, audios and videos, the limitation of existing approaches greatly hinders the development of dialogue systems. In this paper, we focus on task-oriented dialogue systems and address this limitation by proposing a novel model that integrates external multimodal KB reasoning with pre-trained language models. We further enhance the model via a novel multi-granularity fusion mechanism to capture multi-grained semantics in the dialogue history. To validate the effectiveness of the proposed model, we collect a new large-scale (14K) dialogue dataset MMDialKB, built upon multimodal KB. Both automatic and human evaluation results on MMDialKB demonstrate the superiority of our proposed framework over strong baselines.

scholarly journals From UBGs to CFGs A practical corpus-driven approach

2007 ◽  
Vol 13 (4) ◽  
pp. 317-351
Author(s):  
HANS-ULRICH KRIEGER
Keyword(s):  
Approximation Method ◽  
Large Scale ◽  
Question Answering ◽  
Syntactic Structure ◽  
Language Models ◽  
Open Approach ◽  
Domain Specific ◽  
Stochastic Parsing ◽  
Speech Recognizer ◽  
Context Free

AbstractWe present a simple and intuitive unsound corpus-driven approximation method for turning unification-based grammars, such as HPSG, CLE, or PATR-II into context-free grammars (CFGs). Our research is motivated by the idea that we can exploit (large-scale), hand-written unification grammars not only for the purpose of describing natural language and obtaining a syntactic structure (and perhaps a semantic form), but also to address several other very practical topics. Firstly, to speed up deep parsing by having a cheap recognition pre-flter (the approximated CFG). Secondly, to obtain an indirect stochastic parsing model for the unification grammar through a trained PCFG, obtained from the approximated CFG. This gives us an efficient disambiguation model for the unification-based grammar. Thirdly, to generate domain-specific subgrammars for application areas such as information extraction or question answering. And finally, to compile context-free language models which assist the acoustic model of a speech recognizer. The approximation method is unsound in that it does not generate a CFG whose language is a true superset of the language accepted by the original unification-based grammar. It is a corpus-driven method in that it relies on a corpus of parsed sentences and generates broader CFGs when given more input samples. Our open approach can be fine-tuned in different directions, allowing us to monotonically come close to the original parse trees by shifting more information into the context-free symbols. The approach has been fully implemented in JAVA.

Designing production-friendly machine learning

2021 ◽  
Vol 14 (13) ◽  
pp. 3420-3420
Author(s):  
Matei Zaharia
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Large Scale ◽  
Question Answering ◽  
Failure Modes ◽  
Computational Cost ◽  
Language Models ◽  
Software Systems ◽  
Resource Cost ◽  
Low Computational Cost

Building production ML applications is difficult because of their resource cost and complex failure modes. I will discuss these challenges from two perspectives: the Stanford DAWN Lab and experience with large-scale commercial ML users at Databricks. I will then present two emerging ideas to help address these challenges. The first is "ML platforms", an emerging class of software systems that standardize the interfaces used in ML applications to make them easier to build and maintain. I will give a few examples, including the open-source MLflow system from Databricks [3]. The second idea is models that are more "production-friendly" by design. As a concrete example, I will discuss retrieval-based NLP models such as Stanford's ColBERT [1, 2] that query documents from an updateable corpus to perform tasks such as question-answering, which gives multiple practical advantages, including low computational cost, high interpretability, and very fast updates to the model's "knowledge". These models are an exciting alternative to large language models such as GPT-3.

scholarly journals What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

2021 ◽  
Author(s):  
Di Jin ◽  
Eileen Pan ◽  
Nassim Oufattole ◽  
Wei-Hung Weng ◽  
Hanyi Fang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Free Form ◽  
Test Accuracy ◽  
Open Domain ◽  
Medical Problems ◽  
Rule Based ◽  
Medical Board ◽  
Simplified Chinese

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

scholarly journals What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams

2021 ◽  
Vol 11 (14) ◽  
pp. 6421
Author(s):  
Di Jin ◽  
Eileen Pan ◽  
Nassim Oufattole ◽  
Wei-Hung Weng ◽  
Hanyi Fang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Free Form ◽  
Test Accuracy ◽  
Open Domain ◽  
Medical Problems ◽  
Rule Based ◽  
Medical Board ◽  
Simplified Chinese

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

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