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[PDF] Top 20 Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

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Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

... Commonsense machine comprehension, how- ever, is an natural ability for human but could be very challenging for ...world knowledge whatsoever in the read- er’s mind can affect the choice of an ... See full document

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Explain Yourself! Leveraging Language Models for Commonsense Reasoning

Explain Yourself! Leveraging Language Models for Commonsense Reasoning

... modern machine learning methods (Trinh and Le, 2018) to achieve near-human per- formance, but the emphasis on pronoun resolution in those challenges leaves room for exploration of other kinds of commonsense ... See full document

11

CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning

CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning

... commonsense knowledge. The respective facts are often not formalized in knowledge bases, but can be derived indirectly, ...sense reasoning tasks using primarily information retrieval, ... See full document

7

NumNet: Machine Reading Comprehension with Numerical Reasoning

NumNet: Machine Reading Comprehension with Numerical Reasoning

... the reasoning skills required to answer the questions into the following types: (1) Exact matching/Paraphrasing; (2) Summary; (3) Logic reasoning; (4) Utilizing external knowledge; (5) Numerical ... See full document

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Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases

Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases

... control knowledge plays an important role in the optimization of KBS for at least two reasons: First, the inference algorithms of KBS ...such reasoning with very expressive languages ...where ... See full document

8

MCScript2 0: A Machine Comprehension Corpus Focused on Script Events and Participants

MCScript2 0: A Machine Comprehension Corpus Focused on Script Events and Participants

... News Texts. Two recently published machine comprehension data sets that require commonsense inference are based on news texts. First, NewsQA (Trischler et al., 2017) is a dataset of newswire texts ... See full document

15

Knowledgeable Reader: Enhancing Cloze Style Reading Comprehension with External Commonsense Knowledge

Knowledgeable Reader: Enhancing Cloze Style Reading Comprehension with External Commonsense Knowledge

... monsense knowledge, building on a single-turn neural ...external knowledge improves its results with a relative error rate re- duction of 9% on Common Nouns, thus the model is able to compete with more ... See full document

12

KagNet: Knowledge Aware Graph Networks for Commonsense Reasoning

KagNet: Knowledge Aware Graph Networks for Commonsense Reasoning

... form commonsense reasoning has been seen as the bottleneck of artificial general intelligence (Davis and Marcus, ...chine commonsense with various focuses (Zellers et ...a commonsense reasoner ... See full document

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IIT KGP at COIN 2019: Using pre trained Language Models for modeling Machine Comprehension

IIT KGP at COIN 2019: Using pre trained Language Models for modeling Machine Comprehension

... In this paper, we describe our system for COIN 2019 Shared Task 1: Commonsense Inference in Everyday Narrations Ostermann et al. (2019). We show the power of leverag- ing state-of-the-art pre-trained language mod- ... See full document

5

RACE: Large scale ReAding Comprehension Dataset From Examinations

RACE: Large scale ReAding Comprehension Dataset From Examinations

... of reasoning, the most important fea- ture as a machine comprehension dataset (Chen et ...the reasoning types in its questions, namely passage summarization and attitude anal- ysis, which have ... See full document

10

Multi hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

Multi hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

... neural machine translation, GNN has been employed to integrate syntactic and semantic in- formation into encoders (Bastings et ...a heterogeneous graph to do text classification, which inspires our idea of ... See full document

10

Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension

Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension

... State-of-the-art machine reading compre- hension models are capable of producing answers for factual questions about a given piece of ...requires commonsense knowl- edge which cannot be inferred from the ... See full document

5

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

... Multi-turn Commonsense Inference: 19% of the errors are due to multi-turn commonsense in- ...counterfactual reasoning, if she didn’t chat to her friends, then she wouldn’t have gotten up with a ... See full document

11

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

... Similarly, ConceptNet (Speer, Chin, and Havasi 2017) represents commonsense knowledge as a graph that con- nects words and phrases (concepts) with labeled edges (re- lations). While ConceptNet provides ... See full document

9

Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations

Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations

... various commonsense NLP tasks, but we lack a con- crete understanding of the capability of these ...BERT’s commonsense rep- resentation ...various commonsense features in its embedding space, but is ... See full document

12

Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

... yields near random performance. These results suggest that achieving good performance may not always be merely a matter of training high capac- ity models with large volumes of data. In addition to exploring new models ... See full document

10

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

... Previous efforts on solving the Winograd Schema Challenge and Pronoun Disambiguation Problems mostly rely on human-labeled data, so- phisticated rules, hand-crafted features, or exter- nal knowledge bases (Peng et ... See full document

10

Explicit Utilization of General Knowledge in Machine Reading Comprehension

Explicit Utilization of General Knowledge in Machine Reading Comprehension

... general knowledge, we first study the relationship between the amount of general knowledge and the performance of ...general knowledge rises monotonically, but the performance of KAR first rises ... See full document

10

Social IQa: Commonsense Reasoning about Social Interactions

Social IQa: Commonsense Reasoning about Social Interactions

... By design, the questions require inferential rea- soning about the social causes and effects of situa- tions, in line with the type of intelligence required for an AI assistant to interact with human users (e.g., know to ... See full document

11

Commonsense Knowledge Base Completion

Commonsense Knowledge Base Completion

... using knowledge of various forms. Our focus is on the type of knowledge that is often referred to as “common- sense” or “background” ...this knowledge from patterns in raw text (Gor- don, 2014; ... See full document

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